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Applied Data Analysis and Machine Learning
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Dimensionality Reduction
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Week 41 Neural networks and constructing a neural network code
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Week 42 Constructing a Neural Network code with introduction to Tensor flow
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Exercises weeks 43 and 44
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Week 43: Deep Learning: Constructing a Neural Network code and solving differential equations
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Week 44, Convolutional Neural Networks (CNN)
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Week 45, Recurrent Neural Networks
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Exercises weeks 43 and 44
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Overarching aims of the exercises weeks 43 and 44
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<a class="reference internal nav-link" href="#the-and-and-xor-gates">
The AND and XOR Gates
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Representing the Data Sets
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Setting up dimensionalities by hand
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Setting up the Neural Network
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The Code using Scikit-Learn
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Building a neural network code
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Learning rate methods
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Usage of the above learning rate schedulers
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Cost functions
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Activation functions
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The Neural Network
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Multiclass classification
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Testing the XOR gate and other gates
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<h1>Exercises weeks 43 and 44</h1>
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<h2> Contents </h2>
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Exercises weeks 43 and 44
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Overarching aims of the exercises weeks 43 and 44
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<a class="reference internal nav-link" href="#the-and-and-xor-gates">
The AND and XOR Gates
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<a class="reference internal nav-link" href="#representing-the-data-sets">
Representing the Data Sets
</a>
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<a class="reference internal nav-link" href="#setting-up-dimensionalities-by-hand">
Setting up dimensionalities by hand
</a>
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<a class="reference internal nav-link" href="#setting-up-the-neural-network">
Setting up the Neural Network
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The Code using Scikit-Learn
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Building a neural network code
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Learning rate methods
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<a class="reference internal nav-link" href="#usage-of-the-above-learning-rate-schedulers">
Usage of the above learning rate schedulers
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<a class="reference internal nav-link" href="#cost-functions">
Cost functions
</a>
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<a class="reference internal nav-link" href="#activation-functions">
Activation functions
</a>
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The Neural Network
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Multiclass classification
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<a class="reference internal nav-link" href="#testing-the-xor-gate-and-other-gates">
Testing the XOR gate and other gates
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<!-- HTML file automatically generated from DocOnce source (https://github.com/doconce/doconce/)
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<!-- dom:TITLE: Exercises weeks 43 and 44 --><div class="tex2jax_ignore mathjax_ignore section" id="exercises-weeks-43-and-44">
<h1>Exercises weeks 43 and 44<a class="headerlink" href="#exercises-weeks-43-and-44" title="Permalink to this headline">¶</a></h1>
<p><strong>October 23-27, 2023</strong></p>
<p>Date: <strong>Deadline is Sunday November 5 at midnight</strong></p>
<p>You can hand in the exercises from week 43 and week 44 as one exercise and get a total score of two additional points.</p>
</div>
<div class="tex2jax_ignore mathjax_ignore section" id="overarching-aims-of-the-exercises-weeks-43-and-44">
<h1>Overarching aims of the exercises weeks 43 and 44<a class="headerlink" href="#overarching-aims-of-the-exercises-weeks-43-and-44" title="Permalink to this headline">¶</a></h1>
<p>The aim of the exercises this week and next week is to get started with writing a neural network code
of relevance for project 2.</p>
<p>During week 41 we discussed three different types of gates, the
so-called XOR, the OR and the AND gates. In order to develop a code
for neural networks, it can be useful to set up a simpler system with
only two inputs and one output. This can make it easier to debug and
study the feed forward pass and the back propagation part. In the
exercise this and next week, we propose to study this system with just
one hidden layer and two hidden nodes. There is only one output node
and we can choose to use either a simple regression case (fitting a
line) or just a binary classification case with the cross-entropy as
cost function.</p>
<p>Their inputs and outputs can be
summarized using the following tables, first for the OR gate with
inputs <span class="math notranslate nohighlight">\(x_1\)</span> and <span class="math notranslate nohighlight">\(x_2\)</span> and outputs <span class="math notranslate nohighlight">\(y\)</span>:</p>
<table class="dotable" border="1">
<thead>
<tr><th align="center">$x_1$</th> <th align="center">$x_2$</th> <th align="center">$y$</th> </tr>
</thead>
<tbody>
<tr><td align="center"> 0 </td> <td align="center"> 0 </td> <td align="center"> 0 </td> </tr>
<tr><td align="center"> 0 </td> <td align="center"> 1 </td> <td align="center"> 1 </td> </tr>
<tr><td align="center"> 1 </td> <td align="center"> 0 </td> <td align="center"> 1 </td> </tr>
<tr><td align="center"> 1 </td> <td align="center"> 1 </td> <td align="center"> 1 </td> </tr>
</tbody>
</table><div class="section" id="the-and-and-xor-gates">
<h2>The AND and XOR Gates<a class="headerlink" href="#the-and-and-xor-gates" title="Permalink to this headline">¶</a></h2>
<p>The AND gate is defined as</p>
<table class="dotable" border="1">
<thead>
<tr><th align="center">$x_1$</th> <th align="center">$x_2$</th> <th align="center">$y$</th> </tr>
</thead>
<tbody>
<tr><td align="center"> 0 </td> <td align="center"> 0 </td> <td align="center"> 0 </td> </tr>
<tr><td align="center"> 0 </td> <td align="center"> 1 </td> <td align="center"> 0 </td> </tr>
<tr><td align="center"> 1 </td> <td align="center"> 0 </td> <td align="center"> 0 </td> </tr>
<tr><td align="center"> 1 </td> <td align="center"> 1 </td> <td align="center"> 1 </td> </tr>
</tbody>
</table>
<p>And finally we have the XOR gate</p>
<table class="dotable" border="1">
<thead>
<tr><th align="center">$x_1$</th> <th align="center">$x_2$</th> <th align="center">$y$</th> </tr>
</thead>
<tbody>
<tr><td align="center"> 0 </td> <td align="center"> 0 </td> <td align="center"> 0 </td> </tr>
<tr><td align="center"> 0 </td> <td align="center"> 1 </td> <td align="center"> 1 </td> </tr>
<tr><td align="center"> 1 </td> <td align="center"> 0 </td> <td align="center"> 1 </td> </tr>
<tr><td align="center"> 1 </td> <td align="center"> 1 </td> <td align="center"> 0 </td> </tr>
</tbody>
</table></div>
<div class="section" id="representing-the-data-sets">
<h2>Representing the Data Sets<a class="headerlink" href="#representing-the-data-sets" title="Permalink to this headline">¶</a></h2>
<p>Our design matrix is defined by the input values <span class="math notranslate nohighlight">\(x_1\)</span> and <span class="math notranslate nohighlight">\(x_2\)</span>. Since we have four possible outputs, our design matrix reads</p>
<div class="math notranslate nohighlight">
\[\begin{split}
\boldsymbol{X}=\begin{bmatrix} 0 &amp; 0 \\
0 &amp; 1 \\
1 &amp; 0 \\
1 &amp; 1 \end{bmatrix},
\end{split}\]</div>
<p>while the vector of outputs is <span class="math notranslate nohighlight">\(\boldsymbol{y}^T=[0,1,1,0]\)</span> for the XOR gate, <span class="math notranslate nohighlight">\(\boldsymbol{y}^T=[0,0,0,1]\)</span> for the AND gate and <span class="math notranslate nohighlight">\(\boldsymbol{y}^T=[0,1,1,1]\)</span> for the OR gate.</p>
<p>Your tasks here are</p>
<ol class="simple">
<li><p>Set up the design matrix with the inputs as discussed above and a vector containing the output, the so-called targets. Note that the design matrix is the same for all gates. You need just to define different outputs.</p></li>
<li><p>Construct a neural network with only one hidden layer and two hidden nodes using the Sigmoid function as activation function.</p></li>
<li><p>Set up the output layer with only one output node and use again the Sigmoid function as activation function for the output.</p></li>
<li><p>Initialize the weights and biases and perform a feed forward pass and compare the outputs with the targets.</p></li>
<li><p>Set up the cost function (cross entropy for classification of binary cases).</p></li>
<li><p>Calculate the gradients needed for the back propagation part.</p></li>
<li><p>Use the gradients to train the network in the back propagation part. Think of using automatic differentiation.</p></li>
<li><p>Train the network and study your results and compare with results obtained either with <strong>scikit-learn</strong> or <strong>TensorFlow</strong>.</p></li>
</ol>
<p>Everything you develop here can be used directly into the code for the project.</p>
</div>
<div class="section" id="setting-up-dimensionalities-by-hand">
<h2>Setting up dimensionalities by hand<a class="headerlink" href="#setting-up-dimensionalities-by-hand" title="Permalink to this headline">¶</a></h2>
<p>It can be useful to test the dimensionalities for the network. Let us assume we have performed an optimization for XOR gate and found that the weights for the hidden layer are given by</p>
<div class="math notranslate nohighlight">
\[\begin{split}
\boldsymbol{W_h}=\begin{bmatrix} 1 &amp; 1 \\
1 &amp; 1 \end{bmatrix},
\end{split}\]</div>
<p>Multiplying <span class="math notranslate nohighlight">\(\boldsymbol{X}\)</span> and <span class="math notranslate nohighlight">\(\boldsymbol{W}\)</span> gives</p>
<div class="math notranslate nohighlight">
\[\begin{split}
\boldsymbol{X}{W}_h=\begin{bmatrix} 0 &amp; 0 \\
1 &amp; 1 \\
1 &amp; 1 \\
2 &amp; 2 \end{bmatrix},
\end{split}\]</div>
<p>Assume also that the bias vector for the hidden layer is</p>
<div class="math notranslate nohighlight">
\[\begin{split}
\boldsymbol{b}_h=\begin{bmatrix} 0 \\
-1\end{bmatrix},
\end{split}\]</div>
<p>Adding it gives us the input to the activation function of the hidden layer</p>
<div class="math notranslate nohighlight">
\[\begin{split}
\boldsymbol{z}_h=\boldsymbol{X}\boldsymbol{W}_h+\boldsymbol{b}_h=\begin{bmatrix} 0 &amp; -1 \\
1 &amp; 0 \\
1 &amp; 0 \\
2 &amp; 1 \end{bmatrix},
\end{split}\]</div>
<p>Let us then assume that our activation function is the RELU function, which simply means that we take the max of <span class="math notranslate nohighlight">\(0\)</span> and the elements of the input argument <span class="math notranslate nohighlight">\(\boldsymbol{z}_h\)</span>, that is we have</p>
<div class="math notranslate nohighlight">
\[\begin{split}
\boldsymbol{a}_h=\mathrm{RELU}(\boldsymbol{z}_h=\boldsymbol{X}\boldsymbol{W}_h+\boldsymbol{b}_h)=\begin{bmatrix} 0 &amp; 0 \\
1 &amp; 0 \\
1 &amp; 0 \\
2 &amp; 1 \end{bmatrix},
\end{split}\]</div>
<p>Assume also that the bias of the output layer is zero and that the weights of the output layer are</p>
<div class="math notranslate nohighlight">
\[\begin{split}
\boldsymbol{w}_o=\begin{bmatrix} 1 \\
-2\end{bmatrix},
\end{split}\]</div>
<p>and multiplying with <span class="math notranslate nohighlight">\(\boldsymbol{a}_h\)</span> gives the output</p>
<div class="math notranslate nohighlight">
\[\begin{split}
\boldsymbol{a}_o=\begin{bmatrix} 0 &amp; 0 \\
1 &amp; 0 \\
1 &amp; 0 \\
2 &amp; 1 \end{bmatrix}\begin{bmatrix} 1 \\
-2\end{bmatrix}=\begin{bmatrix} 0 \\ 1 \\ 1 \\0\end{bmatrix},
\end{split}\]</div>
<p>the wanted result. Pay attention to the dimensionalities as well.</p>
</div>
<div class="section" id="setting-up-the-neural-network">
<h2>Setting up the Neural Network<a class="headerlink" href="#setting-up-the-neural-network" title="Permalink to this headline">¶</a></h2>
<p>We define first our design matrix and the various output vectors for the different gates.</p>
<div class="cell docutils container">
<div class="cell_input docutils container">
<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="o">%</span><span class="k">matplotlib</span> inline
<span class="sd">&quot;&quot;&quot;</span>
<span class="sd">Simple code that tests XOR, OR and AND gates with linear regression</span>
<span class="sd">&quot;&quot;&quot;</span>
<span class="c1"># import necessary packages</span>
<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
<span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
<span class="kn">from</span> <span class="nn">sklearn</span> <span class="kn">import</span> <span class="n">datasets</span>
<span class="k">def</span> <span class="nf">sigmoid</span><span class="p">(</span><span class="n">x</span><span class="p">):</span>
<span class="k">return</span> <span class="mi">1</span><span class="o">/</span><span class="p">(</span><span class="mi">1</span> <span class="o">+</span> <span class="n">np</span><span class="o">.</span><span class="n">exp</span><span class="p">(</span><span class="o">-</span><span class="n">x</span><span class="p">))</span>
<span class="k">def</span> <span class="nf">feed_forward</span><span class="p">(</span><span class="n">X</span><span class="p">):</span>
<span class="c1"># weighted sum of inputs to the hidden layer</span>
<span class="n">z_h</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">matmul</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">hidden_weights</span><span class="p">)</span> <span class="o">+</span> <span class="n">hidden_bias</span>
<span class="c1"># activation in the hidden layer</span>
<span class="n">a_h</span> <span class="o">=</span> <span class="n">sigmoid</span><span class="p">(</span><span class="n">z_h</span><span class="p">)</span>
<span class="c1"># weighted sum of inputs to the output layer</span>
<span class="n">z_o</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">matmul</span><span class="p">(</span><span class="n">a_h</span><span class="p">,</span> <span class="n">output_weights</span><span class="p">)</span> <span class="o">+</span> <span class="n">output_bias</span>
<span class="c1"># softmax output</span>
<span class="c1"># axis 0 holds each input and axis 1 the probabilities of each category</span>
<span class="n">probabilities</span> <span class="o">=</span> <span class="n">sigmoid</span><span class="p">(</span><span class="n">z_o</span><span class="p">)</span>
<span class="k">return</span> <span class="n">probabilities</span>
<span class="c1"># ensure the same random numbers appear every time</span>
<span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">seed</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span>
<span class="c1"># Design matrix</span>
<span class="n">X</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span> <span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">],</span> <span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">],</span> <span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">0</span><span class="p">],[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">]],</span><span class="n">dtype</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">float64</span><span class="p">)</span>
<span class="c1"># The XOR gate</span>
<span class="n">yXOR</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span> <span class="p">[</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">1</span> <span class="p">,</span><span class="mi">1</span><span class="p">,</span> <span class="mi">0</span><span class="p">])</span>
<span class="c1"># The OR gate</span>
<span class="n">yOR</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span> <span class="p">[</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">1</span> <span class="p">,</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">])</span>
<span class="c1"># The AND gate</span>
<span class="n">yAND</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span> <span class="p">[</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">0</span> <span class="p">,</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">])</span>
<span class="c1"># Defining the neural network</span>
<span class="n">n_inputs</span><span class="p">,</span> <span class="n">n_features</span> <span class="o">=</span> <span class="n">X</span><span class="o">.</span><span class="n">shape</span>
<span class="n">n_hidden_neurons</span> <span class="o">=</span> <span class="mi">2</span>
<span class="n">n_categories</span> <span class="o">=</span> <span class="mi">1</span>
<span class="n">n_features</span> <span class="o">=</span> <span class="mi">2</span>
<span class="c1"># we make the weights normally distributed using numpy.random.randn</span>
<span class="c1"># weights and bias in the hidden layer</span>
<span class="n">hidden_weights</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">randn</span><span class="p">(</span><span class="n">n_features</span><span class="p">,</span> <span class="n">n_hidden_neurons</span><span class="p">)</span>
<span class="n">hidden_bias</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">(</span><span class="n">n_hidden_neurons</span><span class="p">)</span> <span class="o">+</span> <span class="mf">0.01</span>
<span class="c1"># weights and bias in the output layer</span>
<span class="n">output_weights</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">randn</span><span class="p">(</span><span class="n">n_hidden_neurons</span><span class="p">,</span> <span class="n">n_categories</span><span class="p">)</span>
<span class="n">output_bias</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">(</span><span class="n">n_categories</span><span class="p">)</span> <span class="o">+</span> <span class="mf">0.01</span>
<span class="n">probabilities</span> <span class="o">=</span> <span class="n">feed_forward</span><span class="p">(</span><span class="n">X</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="n">probabilities</span><span class="p">)</span>
</pre></div>
</div>
</div>
<div class="cell_output docutils container">
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>[[0.61238907]
[0.61939429]
[0.73482109]
[0.70115106]]
</pre></div>
</div>
</div>
</div>
<p>Not an impressive result, but this was our first forward pass with randomly assigned weights. Let us now add the full network with the back-propagation algorithm discussed above.</p>
</div>
<div class="section" id="the-code-using-scikit-learn">
<h2>The Code using Scikit-Learn<a class="headerlink" href="#the-code-using-scikit-learn" title="Permalink to this headline">¶</a></h2>
<div class="cell docutils container">
<div class="cell_input docutils container">
<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># import necessary packages</span>
<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
<span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
<span class="kn">from</span> <span class="nn">sklearn.neural_network</span> <span class="kn">import</span> <span class="n">MLPClassifier</span>
<span class="kn">from</span> <span class="nn">sklearn.metrics</span> <span class="kn">import</span> <span class="n">accuracy_score</span>
<span class="kn">import</span> <span class="nn">seaborn</span> <span class="k">as</span> <span class="nn">sns</span>
<span class="c1"># ensure the same random numbers appear every time</span>
<span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">seed</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span>
<span class="c1"># Design matrix</span>
<span class="n">X</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span> <span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">],</span> <span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">],</span> <span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">0</span><span class="p">],[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">]],</span><span class="n">dtype</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">float64</span><span class="p">)</span>
<span class="c1"># The XOR gate</span>
<span class="n">yXOR</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span> <span class="p">[</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">1</span> <span class="p">,</span><span class="mi">1</span><span class="p">,</span> <span class="mi">0</span><span class="p">])</span>
<span class="c1"># The OR gate</span>
<span class="n">yOR</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span> <span class="p">[</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">1</span> <span class="p">,</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">])</span>
<span class="c1"># The AND gate</span>
<span class="n">yAND</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span> <span class="p">[</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">0</span> <span class="p">,</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">])</span>
<span class="c1"># Defining the neural network</span>
<span class="n">n_hidden_neurons</span> <span class="o">=</span> <span class="mi">2</span>
<span class="n">eta_vals</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">logspace</span><span class="p">(</span><span class="o">-</span><span class="mi">5</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">7</span><span class="p">)</span>
<span class="n">lmbd_vals</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">logspace</span><span class="p">(</span><span class="o">-</span><span class="mi">5</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">7</span><span class="p">)</span>
<span class="c1"># store models for later use</span>
<span class="n">DNN_scikit</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">((</span><span class="nb">len</span><span class="p">(</span><span class="n">eta_vals</span><span class="p">),</span> <span class="nb">len</span><span class="p">(</span><span class="n">lmbd_vals</span><span class="p">)),</span> <span class="n">dtype</span><span class="o">=</span><span class="nb">object</span><span class="p">)</span>
<span class="n">epochs</span> <span class="o">=</span> <span class="mi">100</span>
<span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">eta</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">eta_vals</span><span class="p">):</span>
<span class="k">for</span> <span class="n">j</span><span class="p">,</span> <span class="n">lmbd</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">lmbd_vals</span><span class="p">):</span>
<span class="n">dnn</span> <span class="o">=</span> <span class="n">MLPClassifier</span><span class="p">(</span><span class="n">hidden_layer_sizes</span><span class="o">=</span><span class="p">(</span><span class="n">n_hidden_neurons</span><span class="p">),</span> <span class="n">activation</span><span class="o">=</span><span class="s1">&#39;logistic&#39;</span><span class="p">,</span>
<span class="n">alpha</span><span class="o">=</span><span class="n">lmbd</span><span class="p">,</span> <span class="n">learning_rate_init</span><span class="o">=</span><span class="n">eta</span><span class="p">,</span> <span class="n">max_iter</span><span class="o">=</span><span class="n">epochs</span><span class="p">)</span>
<span class="n">dnn</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">yXOR</span><span class="p">)</span>
<span class="n">DNN_scikit</span><span class="p">[</span><span class="n">i</span><span class="p">][</span><span class="n">j</span><span class="p">]</span> <span class="o">=</span> <span class="n">dnn</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Learning rate = &quot;</span><span class="p">,</span> <span class="n">eta</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Lambda = &quot;</span><span class="p">,</span> <span class="n">lmbd</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Accuracy score on data set: &quot;</span><span class="p">,</span> <span class="n">dnn</span><span class="o">.</span><span class="n">score</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">yXOR</span><span class="p">))</span>
<span class="nb">print</span><span class="p">()</span>
<span class="n">sns</span><span class="o">.</span><span class="n">set</span><span class="p">()</span>
<span class="n">test_accuracy</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">((</span><span class="nb">len</span><span class="p">(</span><span class="n">eta_vals</span><span class="p">),</span> <span class="nb">len</span><span class="p">(</span><span class="n">lmbd_vals</span><span class="p">)))</span>
<span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">eta_vals</span><span class="p">)):</span>
<span class="k">for</span> <span class="n">j</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">lmbd_vals</span><span class="p">)):</span>
<span class="n">dnn</span> <span class="o">=</span> <span class="n">DNN_scikit</span><span class="p">[</span><span class="n">i</span><span class="p">][</span><span class="n">j</span><span class="p">]</span>
<span class="n">test_pred</span> <span class="o">=</span> <span class="n">dnn</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">X</span><span class="p">)</span>
<span class="n">test_accuracy</span><span class="p">[</span><span class="n">i</span><span class="p">][</span><span class="n">j</span><span class="p">]</span> <span class="o">=</span> <span class="n">accuracy_score</span><span class="p">(</span><span class="n">yXOR</span><span class="p">,</span> <span class="n">test_pred</span><span class="p">)</span>
<span class="n">fig</span><span class="p">,</span> <span class="n">ax</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplots</span><span class="p">(</span><span class="n">figsize</span> <span class="o">=</span> <span class="p">(</span><span class="mi">10</span><span class="p">,</span> <span class="mi">10</span><span class="p">))</span>
<span class="n">sns</span><span class="o">.</span><span class="n">heatmap</span><span class="p">(</span><span class="n">test_accuracy</span><span class="p">,</span> <span class="n">annot</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">ax</span><span class="o">=</span><span class="n">ax</span><span class="p">,</span> <span class="n">cmap</span><span class="o">=</span><span class="s2">&quot;viridis&quot;</span><span class="p">)</span>
<span class="n">ax</span><span class="o">.</span><span class="n">set_title</span><span class="p">(</span><span class="s2">&quot;Test Accuracy&quot;</span><span class="p">)</span>
<span class="n">ax</span><span class="o">.</span><span class="n">set_ylabel</span><span class="p">(</span><span class="s2">&quot;$\eta$&quot;</span><span class="p">)</span>
<span class="n">ax</span><span class="o">.</span><span class="n">set_xlabel</span><span class="p">(</span><span class="s2">&quot;$\lambda$&quot;</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
</pre></div>
</div>
</div>
<div class="cell_output docutils container">
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Learning rate = 1e-05
Lambda = 1e-05
Accuracy score on data set: 0.5
Learning rate = 1e-05
Lambda = 0.0001
Accuracy score on data set: 0.5
Learning rate = 1e-05
Lambda = 0.001
Accuracy score on data set: 0.5
Learning rate = 1e-05
Lambda = 0.01
Accuracy score on data set: 0.5
Learning rate = 1e-05
Lambda = 0.1
Accuracy score on data set: 0.5
Learning rate = 1e-05
Lambda = 1.0
Accuracy score on data set: 0.5
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Learning rate = 1e-05
Lambda = 10.0
Accuracy score on data set: 0.5
Learning rate = 0.0001
Lambda = 1e-05
Accuracy score on data set: 0.5
Learning rate = 0.0001
Lambda = 0.0001
Accuracy score on data set: 0.5
Learning rate = 0.0001
Lambda = 0.001
Accuracy score on data set: 0.5
Learning rate = 0.0001
Lambda = 0.01
Accuracy score on data set: 0.5
Learning rate = 0.0001
Lambda = 0.1
Accuracy score on data set: 0.5
Learning rate = 0.0001
Lambda = 1.0
Accuracy score on data set: 0.5
Learning rate = 0.0001
Lambda = 10.0
Accuracy score on data set: 0.5
Learning rate = 0.001
Lambda = 1e-05
Accuracy score on data set: 0.5
Learning rate = 0.001
Lambda = 0.0001
Accuracy score on data set: 0.5
Learning rate = 0.001
Lambda = 0.001
Accuracy score on data set: 0.5
Learning rate = 0.001
Lambda = 0.01
Accuracy score on data set: 0.5
Learning rate = 0.001
Lambda = 0.1
Accuracy score on data set: 0.5
Learning rate = 0.001
Lambda = 1.0
Accuracy score on data set: 0.5
Learning rate = 0.001
Lambda = 10.0
Accuracy score on data set: 0.5
Learning rate = 0.01
Lambda = 1e-05
Accuracy score on data set: 0.25
Learning rate = 0.01
Lambda = 0.0001
Accuracy score on data set: 0.75
Learning rate = 0.01
Lambda = 0.001
Accuracy score on data set: 0.5
Learning rate = 0.01
Lambda = 0.01
Accuracy score on data set: 0.75
Learning rate = 0.01
Lambda = 0.1
Accuracy score on data set: 0.5
Learning rate = 0.01
Lambda = 1.0
Accuracy score on data set: 0.5
Learning rate = 0.01
Lambda = 10.0
Accuracy score on data set: 0.5
Learning rate = 0.1
Lambda = 1e-05
Accuracy score on data set: 0.5
Learning rate = 0.1
Lambda = 0.0001
Accuracy score on data set: 0.5
Learning rate = 0.1
Lambda = 0.001
Accuracy score on data set: 1.0
Learning rate = 0.1
Lambda = 0.01
Accuracy score on data set: 1.0
Learning rate = 0.1
Lambda = 0.1
Accuracy score on data set: 0.5
Learning rate = 0.1
Lambda = 1.0
Accuracy score on data set: 0.5
Learning rate = 0.1
Lambda = 10.0
Accuracy score on data set: 0.5
Learning rate = 1.0
Lambda = 1e-05
Accuracy score on data set: 0.75
Learning rate = 1.0
Lambda = 0.0001
Accuracy score on data set: 0.75
Learning rate = 1.0
Lambda = 0.001
Accuracy score on data set: 0.75
Learning rate = 1.0
Lambda = 0.01
Accuracy score on data set: 0.5
Learning rate = 1.0
Lambda = 0.1
Accuracy score on data set: 0.5
Learning rate = 1.0
Lambda = 1.0
Accuracy score on data set: 0.5
Learning rate = 1.0
Lambda = 10.0
Accuracy score on data set: 0.5
Learning rate = 10.0
Lambda = 1e-05
Accuracy score on data set: 0.5
Learning rate = 10.0
Lambda = 0.0001
Accuracy score on data set: 0.5
Learning rate = 10.0
Lambda = 0.001
Accuracy score on data set: 0.5
Learning rate = 10.0
Lambda = 0.01
Accuracy score on data set: 0.5
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Learning rate = 10.0
Lambda = 0.1
Accuracy score on data set: 0.5
Learning rate = 10.0
Lambda = 1.0
Accuracy score on data set: 0.5
Learning rate = 10.0
Lambda = 10.0
Accuracy score on data set: 0.5
</pre></div>
</div>
<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn&#39;t converged yet.
warnings.warn(
/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn&#39;t converged yet.
warnings.warn(
/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn&#39;t converged yet.
warnings.warn(
/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn&#39;t converged yet.
warnings.warn(
/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn&#39;t converged yet.
warnings.warn(
/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn&#39;t converged yet.
warnings.warn(
/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn&#39;t converged yet.
warnings.warn(
/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn&#39;t converged yet.
warnings.warn(
/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn&#39;t converged yet.
warnings.warn(
/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn&#39;t converged yet.
warnings.warn(
</pre></div>
</div>
<img alt="_images/exercisesweek43_26_4.png" src="_images/exercisesweek43_26_4.png" />
</div>
</div>
</div>
<div class="section" id="building-a-neural-network-code">
<h2>Building a neural network code<a class="headerlink" href="#building-a-neural-network-code" title="Permalink to this headline">¶</a></h2>
<p>Here we present a flexible object oriented codebase
for a feed forward neural network, along with a demonstration of how
to use it. Before we get into the details of the neural network, we
will first present some implementations of various schedulers, cost
functions and activation functions that can be used together with the
neural network.</p>
<p>The codes here were developed by Eric Reber and Gregor Kajda during spring 2023.</p>
<div class="section" id="learning-rate-methods">
<h3>Learning rate methods<a class="headerlink" href="#learning-rate-methods" title="Permalink to this headline">¶</a></h3>
<p>The code below shows object oriented implementations of the Constant,
Momentum, Adagrad, AdagradMomentum, RMS prop and Adam schedulers. All
of the classes belong to the shared abstract Scheduler class, and
share the update_change() and reset() methods allowing for any of the
schedulers to be seamlessly used during the training stage, as will
later be shown in the fit() method of the neural
network. Update_change() only has one parameter, the gradient
(<span class="math notranslate nohighlight">\(δ^l_ja^{l−1}_k\)</span>), and returns the change which will be subtracted
from the weights. The reset() function takes no parameters, and resets
the desired variables. For Constant and Momentum, reset does nothing.</p>
<div class="cell docutils container">
<div class="cell_input docutils container">
<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">autograd.numpy</span> <span class="k">as</span> <span class="nn">np</span>
<span class="k">class</span> <span class="nc">Scheduler</span><span class="p">:</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Abstract class for Schedulers</span>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">eta</span><span class="p">):</span>
<span class="bp">self</span><span class="o">.</span><span class="n">eta</span> <span class="o">=</span> <span class="n">eta</span>
<span class="c1"># should be overwritten</span>
<span class="k">def</span> <span class="nf">update_change</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">gradient</span><span class="p">):</span>
<span class="k">raise</span> <span class="ne">NotImplementedError</span>
<span class="c1"># overwritten if needed</span>
<span class="k">def</span> <span class="nf">reset</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="k">pass</span>
<span class="k">class</span> <span class="nc">Constant</span><span class="p">(</span><span class="n">Scheduler</span><span class="p">):</span>
<span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">eta</span><span class="p">):</span>
<span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="n">eta</span><span class="p">)</span>
<span class="k">def</span> <span class="nf">update_change</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">gradient</span><span class="p">):</span>
<span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">eta</span> <span class="o">*</span> <span class="n">gradient</span>
<span class="k">def</span> <span class="nf">reset</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="k">pass</span>
<span class="k">class</span> <span class="nc">Momentum</span><span class="p">(</span><span class="n">Scheduler</span><span class="p">):</span>
<span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">eta</span><span class="p">:</span> <span class="nb">float</span><span class="p">,</span> <span class="n">momentum</span><span class="p">:</span> <span class="nb">float</span><span class="p">):</span>
<span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="n">eta</span><span class="p">)</span>
<span class="bp">self</span><span class="o">.</span><span class="n">momentum</span> <span class="o">=</span> <span class="n">momentum</span>
<span class="bp">self</span><span class="o">.</span><span class="n">change</span> <span class="o">=</span> <span class="mi">0</span>
<span class="k">def</span> <span class="nf">update_change</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">gradient</span><span class="p">):</span>
<span class="bp">self</span><span class="o">.</span><span class="n">change</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">momentum</span> <span class="o">*</span> <span class="bp">self</span><span class="o">.</span><span class="n">change</span> <span class="o">+</span> <span class="bp">self</span><span class="o">.</span><span class="n">eta</span> <span class="o">*</span> <span class="n">gradient</span>
<span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">change</span>
<span class="k">def</span> <span class="nf">reset</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="k">pass</span>
<span class="k">class</span> <span class="nc">Adagrad</span><span class="p">(</span><span class="n">Scheduler</span><span class="p">):</span>
<span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">eta</span><span class="p">):</span>
<span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="n">eta</span><span class="p">)</span>
<span class="bp">self</span><span class="o">.</span><span class="n">G_t</span> <span class="o">=</span> <span class="kc">None</span>
<span class="k">def</span> <span class="nf">update_change</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">gradient</span><span class="p">):</span>
<span class="n">delta</span> <span class="o">=</span> <span class="mf">1e-8</span> <span class="c1"># avoid division ny zero</span>
<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">G_t</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
<span class="bp">self</span><span class="o">.</span><span class="n">G_t</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">((</span><span class="n">gradient</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">gradient</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]))</span>
<span class="bp">self</span><span class="o">.</span><span class="n">G_t</span> <span class="o">+=</span> <span class="n">gradient</span> <span class="o">@</span> <span class="n">gradient</span><span class="o">.</span><span class="n">T</span>
<span class="n">G_t_inverse</span> <span class="o">=</span> <span class="mi">1</span> <span class="o">/</span> <span class="p">(</span>
<span class="n">delta</span> <span class="o">+</span> <span class="n">np</span><span class="o">.</span><span class="n">sqrt</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">diagonal</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">G_t</span><span class="p">),</span> <span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">G_t</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="mi">1</span><span class="p">)))</span>
<span class="p">)</span>
<span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">eta</span> <span class="o">*</span> <span class="n">gradient</span> <span class="o">*</span> <span class="n">G_t_inverse</span>
<span class="k">def</span> <span class="nf">reset</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="bp">self</span><span class="o">.</span><span class="n">G_t</span> <span class="o">=</span> <span class="kc">None</span>
<span class="k">class</span> <span class="nc">AdagradMomentum</span><span class="p">(</span><span class="n">Scheduler</span><span class="p">):</span>
<span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">eta</span><span class="p">,</span> <span class="n">momentum</span><span class="p">):</span>
<span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="n">eta</span><span class="p">)</span>
<span class="bp">self</span><span class="o">.</span><span class="n">G_t</span> <span class="o">=</span> <span class="kc">None</span>
<span class="bp">self</span><span class="o">.</span><span class="n">momentum</span> <span class="o">=</span> <span class="n">momentum</span>
<span class="bp">self</span><span class="o">.</span><span class="n">change</span> <span class="o">=</span> <span class="mi">0</span>
<span class="k">def</span> <span class="nf">update_change</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">gradient</span><span class="p">):</span>
<span class="n">delta</span> <span class="o">=</span> <span class="mf">1e-8</span> <span class="c1"># avoid division ny zero</span>
<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">G_t</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
<span class="bp">self</span><span class="o">.</span><span class="n">G_t</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">((</span><span class="n">gradient</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">gradient</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]))</span>
<span class="bp">self</span><span class="o">.</span><span class="n">G_t</span> <span class="o">+=</span> <span class="n">gradient</span> <span class="o">@</span> <span class="n">gradient</span><span class="o">.</span><span class="n">T</span>
<span class="n">G_t_inverse</span> <span class="o">=</span> <span class="mi">1</span> <span class="o">/</span> <span class="p">(</span>
<span class="n">delta</span> <span class="o">+</span> <span class="n">np</span><span class="o">.</span><span class="n">sqrt</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">diagonal</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">G_t</span><span class="p">),</span> <span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">G_t</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="mi">1</span><span class="p">)))</span>
<span class="p">)</span>
<span class="bp">self</span><span class="o">.</span><span class="n">change</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">change</span> <span class="o">*</span> <span class="bp">self</span><span class="o">.</span><span class="n">momentum</span> <span class="o">+</span> <span class="bp">self</span><span class="o">.</span><span class="n">eta</span> <span class="o">*</span> <span class="n">gradient</span> <span class="o">*</span> <span class="n">G_t_inverse</span>
<span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">change</span>
<span class="k">def</span> <span class="nf">reset</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="bp">self</span><span class="o">.</span><span class="n">G_t</span> <span class="o">=</span> <span class="kc">None</span>
<span class="k">class</span> <span class="nc">RMS_prop</span><span class="p">(</span><span class="n">Scheduler</span><span class="p">):</span>
<span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">eta</span><span class="p">,</span> <span class="n">rho</span><span class="p">):</span>
<span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="n">eta</span><span class="p">)</span>
<span class="bp">self</span><span class="o">.</span><span class="n">rho</span> <span class="o">=</span> <span class="n">rho</span>
<span class="bp">self</span><span class="o">.</span><span class="n">second</span> <span class="o">=</span> <span class="mf">0.0</span>
<span class="k">def</span> <span class="nf">update_change</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">gradient</span><span class="p">):</span>
<span class="n">delta</span> <span class="o">=</span> <span class="mf">1e-8</span> <span class="c1"># avoid division ny zero</span>
<span class="bp">self</span><span class="o">.</span><span class="n">second</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">rho</span> <span class="o">*</span> <span class="bp">self</span><span class="o">.</span><span class="n">second</span> <span class="o">+</span> <span class="p">(</span><span class="mi">1</span> <span class="o">-</span> <span class="bp">self</span><span class="o">.</span><span class="n">rho</span><span class="p">)</span> <span class="o">*</span> <span class="n">gradient</span> <span class="o">*</span> <span class="n">gradient</span>
<span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">eta</span> <span class="o">*</span> <span class="n">gradient</span> <span class="o">/</span> <span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">sqrt</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">second</span> <span class="o">+</span> <span class="n">delta</span><span class="p">))</span>
<span class="k">def</span> <span class="nf">reset</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="bp">self</span><span class="o">.</span><span class="n">second</span> <span class="o">=</span> <span class="mf">0.0</span>
<span class="k">class</span> <span class="nc">Adam</span><span class="p">(</span><span class="n">Scheduler</span><span class="p">):</span>
<span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">eta</span><span class="p">,</span> <span class="n">rho</span><span class="p">,</span> <span class="n">rho2</span><span class="p">):</span>
<span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="n">eta</span><span class="p">)</span>
<span class="bp">self</span><span class="o">.</span><span class="n">rho</span> <span class="o">=</span> <span class="n">rho</span>
<span class="bp">self</span><span class="o">.</span><span class="n">rho2</span> <span class="o">=</span> <span class="n">rho2</span>
<span class="bp">self</span><span class="o">.</span><span class="n">moment</span> <span class="o">=</span> <span class="mi">0</span>
<span class="bp">self</span><span class="o">.</span><span class="n">second</span> <span class="o">=</span> <span class="mi">0</span>
<span class="bp">self</span><span class="o">.</span><span class="n">n_epochs</span> <span class="o">=</span> <span class="mi">1</span>
<span class="k">def</span> <span class="nf">update_change</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">gradient</span><span class="p">):</span>
<span class="n">delta</span> <span class="o">=</span> <span class="mf">1e-8</span> <span class="c1"># avoid division ny zero</span>
<span class="bp">self</span><span class="o">.</span><span class="n">moment</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">rho</span> <span class="o">*</span> <span class="bp">self</span><span class="o">.</span><span class="n">moment</span> <span class="o">+</span> <span class="p">(</span><span class="mi">1</span> <span class="o">-</span> <span class="bp">self</span><span class="o">.</span><span class="n">rho</span><span class="p">)</span> <span class="o">*</span> <span class="n">gradient</span>
<span class="bp">self</span><span class="o">.</span><span class="n">second</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">rho2</span> <span class="o">*</span> <span class="bp">self</span><span class="o">.</span><span class="n">second</span> <span class="o">+</span> <span class="p">(</span><span class="mi">1</span> <span class="o">-</span> <span class="bp">self</span><span class="o">.</span><span class="n">rho2</span><span class="p">)</span> <span class="o">*</span> <span class="n">gradient</span> <span class="o">*</span> <span class="n">gradient</span>
<span class="n">moment_corrected</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">moment</span> <span class="o">/</span> <span class="p">(</span><span class="mi">1</span> <span class="o">-</span> <span class="bp">self</span><span class="o">.</span><span class="n">rho</span><span class="o">**</span><span class="bp">self</span><span class="o">.</span><span class="n">n_epochs</span><span class="p">)</span>
<span class="n">second_corrected</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">second</span> <span class="o">/</span> <span class="p">(</span><span class="mi">1</span> <span class="o">-</span> <span class="bp">self</span><span class="o">.</span><span class="n">rho2</span><span class="o">**</span><span class="bp">self</span><span class="o">.</span><span class="n">n_epochs</span><span class="p">)</span>
<span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">eta</span> <span class="o">*</span> <span class="n">moment_corrected</span> <span class="o">/</span> <span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">sqrt</span><span class="p">(</span><span class="n">second_corrected</span> <span class="o">+</span> <span class="n">delta</span><span class="p">))</span>
<span class="k">def</span> <span class="nf">reset</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="bp">self</span><span class="o">.</span><span class="n">n_epochs</span> <span class="o">+=</span> <span class="mi">1</span>
<span class="bp">self</span><span class="o">.</span><span class="n">moment</span> <span class="o">=</span> <span class="mi">0</span>
<span class="bp">self</span><span class="o">.</span><span class="n">second</span> <span class="o">=</span> <span class="mi">0</span>
</pre></div>
</div>
</div>
</div>
</div>
<div class="section" id="usage-of-the-above-learning-rate-schedulers">
<h3>Usage of the above learning rate schedulers<a class="headerlink" href="#usage-of-the-above-learning-rate-schedulers" title="Permalink to this headline">¶</a></h3>
<p>To initalize a scheduler, simply create the object and pass in the
necessary parameters such as the learning rate and the momentum as
shown below. As the Scheduler class is an abstract class it should not
called directly, and will raise an error upon usage.</p>
<div class="cell docutils container">
<div class="cell_input docutils container">
<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">momentum_scheduler</span> <span class="o">=</span> <span class="n">Momentum</span><span class="p">(</span><span class="n">eta</span><span class="o">=</span><span class="mf">1e-3</span><span class="p">,</span> <span class="n">momentum</span><span class="o">=</span><span class="mf">0.9</span><span class="p">)</span>
<span class="n">adam_scheduler</span> <span class="o">=</span> <span class="n">Adam</span><span class="p">(</span><span class="n">eta</span><span class="o">=</span><span class="mf">1e-3</span><span class="p">,</span> <span class="n">rho</span><span class="o">=</span><span class="mf">0.9</span><span class="p">,</span> <span class="n">rho2</span><span class="o">=</span><span class="mf">0.999</span><span class="p">)</span>
</pre></div>
</div>
</div>
</div>
<p>Here is a small example for how a segment of code using schedulers
could look. Switching out the schedulers is simple.</p>
<div class="cell docutils container">
<div class="cell_input docutils container">
<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">weights</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">ones</span><span class="p">((</span><span class="mi">3</span><span class="p">,</span><span class="mi">3</span><span class="p">))</span>
<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;Before scheduler:</span><span class="se">\n</span><span class="si">{</span><span class="n">weights</span><span class="si">=}</span><span class="s2">&quot;</span><span class="p">)</span>
<span class="n">epochs</span> <span class="o">=</span> <span class="mi">10</span>
<span class="k">for</span> <span class="n">e</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">epochs</span><span class="p">):</span>
<span class="n">gradient</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">rand</span><span class="p">(</span><span class="mi">3</span><span class="p">,</span> <span class="mi">3</span><span class="p">)</span>
<span class="n">change</span> <span class="o">=</span> <span class="n">adam_scheduler</span><span class="o">.</span><span class="n">update_change</span><span class="p">(</span><span class="n">gradient</span><span class="p">)</span>
<span class="n">weights</span> <span class="o">=</span> <span class="n">weights</span> <span class="o">-</span> <span class="n">change</span>
<span class="n">adam_scheduler</span><span class="o">.</span><span class="n">reset</span><span class="p">()</span>
<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;</span><span class="se">\n</span><span class="s2">After scheduler:</span><span class="se">\n</span><span class="si">{</span><span class="n">weights</span><span class="si">=}</span><span class="s2">&quot;</span><span class="p">)</span>
</pre></div>
</div>
</div>
<div class="cell_output docutils container">
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Before scheduler:
weights=array([[1., 1., 1.],
[1., 1., 1.],
[1., 1., 1.]])
After scheduler:
weights=array([[0.993993 , 0.993993 , 0.99399301],
[0.99399308, 0.99399315, 0.99399301],
[0.99399301, 0.99399309, 0.99399301]])
</pre></div>
</div>
</div>
</div>
</div>
<div class="section" id="cost-functions">
<h3>Cost functions<a class="headerlink" href="#cost-functions" title="Permalink to this headline">¶</a></h3>
<p>Here we discuss cost functions that can be used when creating the
neural network. Every cost function takes the target vector as its
parameter, and returns a function valued only at <span class="math notranslate nohighlight">\(x\)</span> such that it may
easily be differentiated.</p>
<div class="cell docutils container">
<div class="cell_input docutils container">
<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">autograd.numpy</span> <span class="k">as</span> <span class="nn">np</span>
<span class="k">def</span> <span class="nf">CostOLS</span><span class="p">(</span><span class="n">target</span><span class="p">):</span>
<span class="k">def</span> <span class="nf">func</span><span class="p">(</span><span class="n">X</span><span class="p">):</span>
<span class="k">return</span> <span class="p">(</span><span class="mf">1.0</span> <span class="o">/</span> <span class="n">target</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">sum</span><span class="p">((</span><span class="n">target</span> <span class="o">-</span> <span class="n">X</span><span class="p">)</span> <span class="o">**</span> <span class="mi">2</span><span class="p">)</span>
<span class="k">return</span> <span class="n">func</span>
<span class="k">def</span> <span class="nf">CostLogReg</span><span class="p">(</span><span class="n">target</span><span class="p">):</span>
<span class="k">def</span> <span class="nf">func</span><span class="p">(</span><span class="n">X</span><span class="p">):</span>
<span class="k">return</span> <span class="o">-</span><span class="p">(</span><span class="mf">1.0</span> <span class="o">/</span> <span class="n">target</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span>
<span class="p">(</span><span class="n">target</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">log</span><span class="p">(</span><span class="n">X</span> <span class="o">+</span> <span class="mf">10e-10</span><span class="p">))</span> <span class="o">+</span> <span class="p">((</span><span class="mi">1</span> <span class="o">-</span> <span class="n">target</span><span class="p">)</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">log</span><span class="p">(</span><span class="mi">1</span> <span class="o">-</span> <span class="n">X</span> <span class="o">+</span> <span class="mf">10e-10</span><span class="p">))</span>
<span class="p">)</span>
<span class="k">return</span> <span class="n">func</span>
<span class="k">def</span> <span class="nf">CostCrossEntropy</span><span class="p">(</span><span class="n">target</span><span class="p">):</span>
<span class="k">def</span> <span class="nf">func</span><span class="p">(</span><span class="n">X</span><span class="p">):</span>
<span class="k">return</span> <span class="o">-</span><span class="p">(</span><span class="mf">1.0</span> <span class="o">/</span> <span class="n">target</span><span class="o">.</span><span class="n">size</span><span class="p">)</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="n">target</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">log</span><span class="p">(</span><span class="n">X</span> <span class="o">+</span> <span class="mf">10e-10</span><span class="p">))</span>
<span class="k">return</span> <span class="n">func</span>
</pre></div>
</div>
</div>
</div>
<p>Below we give a short example of how these cost function may be used
to obtain results if you wish to test them out on your own using
AutoGrad’s automatics differentiation.</p>
<div class="cell docutils container">
<div class="cell_input docutils container">
<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">autograd</span> <span class="kn">import</span> <span class="n">grad</span>
<span class="n">target</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">]])</span><span class="o">.</span><span class="n">T</span>
<span class="n">a</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([[</span><span class="mi">4</span><span class="p">,</span> <span class="mi">5</span><span class="p">,</span> <span class="mi">6</span><span class="p">]])</span><span class="o">.</span><span class="n">T</span>
<span class="n">cost_func</span> <span class="o">=</span> <span class="n">CostCrossEntropy</span>
<span class="n">cost_func_derivative</span> <span class="o">=</span> <span class="n">grad</span><span class="p">(</span><span class="n">cost_func</span><span class="p">(</span><span class="n">target</span><span class="p">))</span>
<span class="n">valued_at_a</span> <span class="o">=</span> <span class="n">cost_func_derivative</span><span class="p">(</span><span class="n">a</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;Derivative of cost function </span><span class="si">{</span><span class="n">cost_func</span><span class="o">.</span><span class="vm">__name__</span><span class="si">}</span><span class="s2"> valued at a:</span><span class="se">\n</span><span class="si">{</span><span class="n">valued_at_a</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
</pre></div>
</div>
</div>
<div class="cell_output docutils container">
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Derivative of cost function CostCrossEntropy valued at a:
[[-0.08333333]
[-0.13333333]
[-0.16666667]]
</pre></div>
</div>
</div>
</div>
</div>
<div class="section" id="activation-functions">
<h3>Activation functions<a class="headerlink" href="#activation-functions" title="Permalink to this headline">¶</a></h3>
<p>Finally, before we look at the neural network, we will look at the
activation functions which can be specified between the hidden layers
and as the output function. Each function can be valued for any given
vector or matrix X, and can be differentiated via derivate().</p>
<div class="cell docutils container">
<div class="cell_input docutils container">
<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">autograd.numpy</span> <span class="k">as</span> <span class="nn">np</span>
<span class="kn">from</span> <span class="nn">autograd</span> <span class="kn">import</span> <span class="n">elementwise_grad</span>
<span class="k">def</span> <span class="nf">identity</span><span class="p">(</span><span class="n">X</span><span class="p">):</span>
<span class="k">return</span> <span class="n">X</span>
<span class="k">def</span> <span class="nf">sigmoid</span><span class="p">(</span><span class="n">X</span><span class="p">):</span>
<span class="k">try</span><span class="p">:</span>
<span class="k">return</span> <span class="mf">1.0</span> <span class="o">/</span> <span class="p">(</span><span class="mi">1</span> <span class="o">+</span> <span class="n">np</span><span class="o">.</span><span class="n">exp</span><span class="p">(</span><span class="o">-</span><span class="n">X</span><span class="p">))</span>
<span class="k">except</span> <span class="ne">FloatingPointError</span><span class="p">:</span>
<span class="k">return</span> <span class="n">np</span><span class="o">.</span><span class="n">where</span><span class="p">(</span><span class="n">X</span> <span class="o">&gt;</span> <span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">(</span><span class="n">X</span><span class="o">.</span><span class="n">shape</span><span class="p">),</span> <span class="n">np</span><span class="o">.</span><span class="n">ones</span><span class="p">(</span><span class="n">X</span><span class="o">.</span><span class="n">shape</span><span class="p">),</span> <span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">(</span><span class="n">X</span><span class="o">.</span><span class="n">shape</span><span class="p">))</span>
<span class="k">def</span> <span class="nf">softmax</span><span class="p">(</span><span class="n">X</span><span class="p">):</span>
<span class="n">X</span> <span class="o">=</span> <span class="n">X</span> <span class="o">-</span> <span class="n">np</span><span class="o">.</span><span class="n">max</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">axis</span><span class="o">=-</span><span class="mi">1</span><span class="p">,</span> <span class="n">keepdims</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
<span class="n">delta</span> <span class="o">=</span> <span class="mf">10e-10</span>
<span class="k">return</span> <span class="n">np</span><span class="o">.</span><span class="n">exp</span><span class="p">(</span><span class="n">X</span><span class="p">)</span> <span class="o">/</span> <span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">exp</span><span class="p">(</span><span class="n">X</span><span class="p">),</span> <span class="n">axis</span><span class="o">=-</span><span class="mi">1</span><span class="p">,</span> <span class="n">keepdims</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span> <span class="o">+</span> <span class="n">delta</span><span class="p">)</span>
<span class="k">def</span> <span class="nf">RELU</span><span class="p">(</span><span class="n">X</span><span class="p">):</span>
<span class="k">return</span> <span class="n">np</span><span class="o">.</span><span class="n">where</span><span class="p">(</span><span class="n">X</span> <span class="o">&gt;</span> <span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">(</span><span class="n">X</span><span class="o">.</span><span class="n">shape</span><span class="p">),</span> <span class="n">X</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">(</span><span class="n">X</span><span class="o">.</span><span class="n">shape</span><span class="p">))</span>
<span class="k">def</span> <span class="nf">LRELU</span><span class="p">(</span><span class="n">X</span><span class="p">):</span>
<span class="n">delta</span> <span class="o">=</span> <span class="mf">10e-4</span>
<span class="k">return</span> <span class="n">np</span><span class="o">.</span><span class="n">where</span><span class="p">(</span><span class="n">X</span> <span class="o">&gt;</span> <span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">(</span><span class="n">X</span><span class="o">.</span><span class="n">shape</span><span class="p">),</span> <span class="n">X</span><span class="p">,</span> <span class="n">delta</span> <span class="o">*</span> <span class="n">X</span><span class="p">)</span>
<span class="k">def</span> <span class="nf">derivate</span><span class="p">(</span><span class="n">func</span><span class="p">):</span>
<span class="k">if</span> <span class="n">func</span><span class="o">.</span><span class="vm">__name__</span> <span class="o">==</span> <span class="s2">&quot;RELU&quot;</span><span class="p">:</span>
<span class="k">def</span> <span class="nf">func</span><span class="p">(</span><span class="n">X</span><span class="p">):</span>
<span class="k">return</span> <span class="n">np</span><span class="o">.</span><span class="n">where</span><span class="p">(</span><span class="n">X</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">0</span><span class="p">)</span>
<span class="k">return</span> <span class="n">func</span>
<span class="k">elif</span> <span class="n">func</span><span class="o">.</span><span class="vm">__name__</span> <span class="o">==</span> <span class="s2">&quot;LRELU&quot;</span><span class="p">:</span>
<span class="k">def</span> <span class="nf">func</span><span class="p">(</span><span class="n">X</span><span class="p">):</span>
<span class="n">delta</span> <span class="o">=</span> <span class="mf">10e-4</span>
<span class="k">return</span> <span class="n">np</span><span class="o">.</span><span class="n">where</span><span class="p">(</span><span class="n">X</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="n">delta</span><span class="p">)</span>
<span class="k">return</span> <span class="n">func</span>
<span class="k">else</span><span class="p">:</span>
<span class="k">return</span> <span class="n">elementwise_grad</span><span class="p">(</span><span class="n">func</span><span class="p">)</span>
</pre></div>
</div>
</div>
</div>
<p>Below follows a short demonstration of how to use an activation
function. The derivative of the activation function will be important
when calculating the output delta term during backpropagation. Note
that derivate() can also be used for cost functions for a more
generalized approach.</p>
<div class="cell docutils container">
<div class="cell_input docutils container">
<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">z</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([[</span><span class="mi">4</span><span class="p">,</span> <span class="mi">5</span><span class="p">,</span> <span class="mi">6</span><span class="p">]])</span><span class="o">.</span><span class="n">T</span>
<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;Input to activation function:</span><span class="se">\n</span><span class="si">{</span><span class="n">z</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
<span class="n">act_func</span> <span class="o">=</span> <span class="n">sigmoid</span>
<span class="n">a</span> <span class="o">=</span> <span class="n">act_func</span><span class="p">(</span><span class="n">z</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;</span><span class="se">\n</span><span class="s2">Output from </span><span class="si">{</span><span class="n">act_func</span><span class="o">.</span><span class="vm">__name__</span><span class="si">}</span><span class="s2"> activation function:</span><span class="se">\n</span><span class="si">{</span><span class="n">a</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
<span class="n">act_func_derivative</span> <span class="o">=</span> <span class="n">derivate</span><span class="p">(</span><span class="n">act_func</span><span class="p">)</span>
<span class="n">valued_at_z</span> <span class="o">=</span> <span class="n">act_func_derivative</span><span class="p">(</span><span class="n">a</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;</span><span class="se">\n</span><span class="s2">Derivative of </span><span class="si">{</span><span class="n">act_func</span><span class="o">.</span><span class="vm">__name__</span><span class="si">}</span><span class="s2"> activation function valued at z:</span><span class="se">\n</span><span class="si">{</span><span class="n">valued_at_z</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
</pre></div>
</div>
</div>
<div class="cell_output docutils container">
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Input to activation function:
[[4]
[5]
[6]]
Output from sigmoid activation function:
[[0.98201379]
[0.99330715]
[0.99752738]]
Derivative of sigmoid activation function valued at z:
[[0.19824029]
[0.19721923]
[0.19683648]]
</pre></div>
</div>
</div>
</div>
</div>
<div class="section" id="the-neural-network">
<h3>The Neural Network<a class="headerlink" href="#the-neural-network" title="Permalink to this headline">¶</a></h3>
<p>Now that we have gotten a good understanding of the implementation of
some important components, we can take a look at an object oriented
implementation of a feed forward neural network. The feed forward
neural network has been implemented as a class named FFNN, which can
be initiated as a regressor or classifier dependant on the choice of
cost function. The FFNN can have any number of input nodes, hidden
layers with any amount of hidden nodes, and any amount of output nodes
meaning it can perform multiclass classification as well as binary
classification and regression problems. Although there is a lot of
code present, it makes for an easy to use and generalizeable interface
for creating many types of neural networks as will be demonstrated
below.</p>
<div class="cell docutils container">
<div class="cell_input docutils container">
<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">math</span>
<span class="kn">import</span> <span class="nn">autograd.numpy</span> <span class="k">as</span> <span class="nn">np</span>
<span class="kn">import</span> <span class="nn">sys</span>
<span class="kn">import</span> <span class="nn">warnings</span>
<span class="kn">from</span> <span class="nn">autograd</span> <span class="kn">import</span> <span class="n">grad</span><span class="p">,</span> <span class="n">elementwise_grad</span>
<span class="kn">from</span> <span class="nn">random</span> <span class="kn">import</span> <span class="n">random</span><span class="p">,</span> <span class="n">seed</span>
<span class="kn">from</span> <span class="nn">copy</span> <span class="kn">import</span> <span class="n">deepcopy</span><span class="p">,</span> <span class="n">copy</span>
<span class="kn">from</span> <span class="nn">typing</span> <span class="kn">import</span> <span class="n">Tuple</span><span class="p">,</span> <span class="n">Callable</span>
<span class="kn">from</span> <span class="nn">sklearn.utils</span> <span class="kn">import</span> <span class="n">resample</span>
<span class="n">warnings</span><span class="o">.</span><span class="n">simplefilter</span><span class="p">(</span><span class="s2">&quot;error&quot;</span><span class="p">)</span>
<span class="k">class</span> <span class="nc">FFNN</span><span class="p">:</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Description:</span>
<span class="sd"> ------------</span>
<span class="sd"> Feed Forward Neural Network with interface enabling flexible design of a</span>
<span class="sd"> nerual networks architecture and the specification of activation function</span>
<span class="sd"> in the hidden layers and output layer respectively. This model can be used</span>
<span class="sd"> for both regression and classification problems, depending on the output function.</span>
<span class="sd"> Attributes:</span>
<span class="sd"> ------------</span>
<span class="sd"> I dimensions (tuple[int]): A list of positive integers, which specifies the</span>
<span class="sd"> number of nodes in each of the networks layers. The first integer in the array</span>
<span class="sd"> defines the number of nodes in the input layer, the second integer defines number</span>
<span class="sd"> of nodes in the first hidden layer and so on until the last number, which</span>
<span class="sd"> specifies the number of nodes in the output layer.</span>
<span class="sd"> II hidden_func (Callable): The activation function for the hidden layers</span>
<span class="sd"> III output_func (Callable): The activation function for the output layer</span>
<span class="sd"> IV cost_func (Callable): Our cost function</span>
<span class="sd"> V seed (int): Sets random seed, makes results reproducible</span>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span>
<span class="bp">self</span><span class="p">,</span>
<span class="n">dimensions</span><span class="p">:</span> <span class="nb">tuple</span><span class="p">[</span><span class="nb">int</span><span class="p">],</span>
<span class="n">hidden_func</span><span class="p">:</span> <span class="n">Callable</span> <span class="o">=</span> <span class="n">sigmoid</span><span class="p">,</span>
<span class="n">output_func</span><span class="p">:</span> <span class="n">Callable</span> <span class="o">=</span> <span class="k">lambda</span> <span class="n">x</span><span class="p">:</span> <span class="n">x</span><span class="p">,</span>
<span class="n">cost_func</span><span class="p">:</span> <span class="n">Callable</span> <span class="o">=</span> <span class="n">CostOLS</span><span class="p">,</span>
<span class="n">seed</span><span class="p">:</span> <span class="nb">int</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
<span class="p">):</span>
<span class="bp">self</span><span class="o">.</span><span class="n">dimensions</span> <span class="o">=</span> <span class="n">dimensions</span>
<span class="bp">self</span><span class="o">.</span><span class="n">hidden_func</span> <span class="o">=</span> <span class="n">hidden_func</span>
<span class="bp">self</span><span class="o">.</span><span class="n">output_func</span> <span class="o">=</span> <span class="n">output_func</span>
<span class="bp">self</span><span class="o">.</span><span class="n">cost_func</span> <span class="o">=</span> <span class="n">cost_func</span>
<span class="bp">self</span><span class="o">.</span><span class="n">seed</span> <span class="o">=</span> <span class="n">seed</span>
<span class="bp">self</span><span class="o">.</span><span class="n">weights</span> <span class="o">=</span> <span class="nb">list</span><span class="p">()</span>
<span class="bp">self</span><span class="o">.</span><span class="n">schedulers_weight</span> <span class="o">=</span> <span class="nb">list</span><span class="p">()</span>
<span class="bp">self</span><span class="o">.</span><span class="n">schedulers_bias</span> <span class="o">=</span> <span class="nb">list</span><span class="p">()</span>
<span class="bp">self</span><span class="o">.</span><span class="n">a_matrices</span> <span class="o">=</span> <span class="nb">list</span><span class="p">()</span>
<span class="bp">self</span><span class="o">.</span><span class="n">z_matrices</span> <span class="o">=</span> <span class="nb">list</span><span class="p">()</span>
<span class="bp">self</span><span class="o">.</span><span class="n">classification</span> <span class="o">=</span> <span class="kc">None</span>
<span class="bp">self</span><span class="o">.</span><span class="n">reset_weights</span><span class="p">()</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_set_classification</span><span class="p">()</span>
<span class="k">def</span> <span class="nf">fit</span><span class="p">(</span>
<span class="bp">self</span><span class="p">,</span>
<span class="n">X</span><span class="p">:</span> <span class="n">np</span><span class="o">.</span><span class="n">ndarray</span><span class="p">,</span>
<span class="n">t</span><span class="p">:</span> <span class="n">np</span><span class="o">.</span><span class="n">ndarray</span><span class="p">,</span>
<span class="n">scheduler</span><span class="p">:</span> <span class="n">Scheduler</span><span class="p">,</span>
<span class="n">batches</span><span class="p">:</span> <span class="nb">int</span> <span class="o">=</span> <span class="mi">1</span><span class="p">,</span>
<span class="n">epochs</span><span class="p">:</span> <span class="nb">int</span> <span class="o">=</span> <span class="mi">100</span><span class="p">,</span>
<span class="n">lam</span><span class="p">:</span> <span class="nb">float</span> <span class="o">=</span> <span class="mi">0</span><span class="p">,</span>
<span class="n">X_val</span><span class="p">:</span> <span class="n">np</span><span class="o">.</span><span class="n">ndarray</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
<span class="n">t_val</span><span class="p">:</span> <span class="n">np</span><span class="o">.</span><span class="n">ndarray</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
<span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Description:</span>
<span class="sd"> ------------</span>
<span class="sd"> This function performs the training the neural network by performing the feedforward and backpropagation</span>
<span class="sd"> algorithm to update the networks weights.</span>
<span class="sd"> Parameters:</span>
<span class="sd"> ------------</span>
<span class="sd"> I X (np.ndarray) : training data</span>
<span class="sd"> II t (np.ndarray) : target data</span>
<span class="sd"> III scheduler (Scheduler) : specified scheduler (algorithm for optimization of gradient descent)</span>
<span class="sd"> IV scheduler_args (list[int]) : list of all arguments necessary for scheduler</span>
<span class="sd"> Optional Parameters:</span>
<span class="sd"> ------------</span>
<span class="sd"> V batches (int) : number of batches the datasets are split into, default equal to 1</span>
<span class="sd"> VI epochs (int) : number of iterations used to train the network, default equal to 100</span>
<span class="sd"> VII lam (float) : regularization hyperparameter lambda</span>
<span class="sd"> VIII X_val (np.ndarray) : validation set</span>
<span class="sd"> IX t_val (np.ndarray) : validation target set</span>
<span class="sd"> Returns:</span>
<span class="sd"> ------------</span>
<span class="sd"> I scores (dict) : A dictionary containing the performance metrics of the model.</span>
<span class="sd"> The number of the metrics depends on the parameters passed to the fit-function.</span>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="c1"># setup </span>
<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">seed</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
<span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">seed</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">seed</span><span class="p">)</span>
<span class="n">val_set</span> <span class="o">=</span> <span class="kc">False</span>
<span class="k">if</span> <span class="n">X_val</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span> <span class="ow">and</span> <span class="n">t_val</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
<span class="n">val_set</span> <span class="o">=</span> <span class="kc">True</span>
<span class="c1"># creating arrays for score metrics</span>
<span class="n">train_errors</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">empty</span><span class="p">(</span><span class="n">epochs</span><span class="p">)</span>
<span class="n">train_errors</span><span class="o">.</span><span class="n">fill</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">nan</span><span class="p">)</span>
<span class="n">val_errors</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">empty</span><span class="p">(</span><span class="n">epochs</span><span class="p">)</span>
<span class="n">val_errors</span><span class="o">.</span><span class="n">fill</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">nan</span><span class="p">)</span>
<span class="n">train_accs</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">empty</span><span class="p">(</span><span class="n">epochs</span><span class="p">)</span>
<span class="n">train_accs</span><span class="o">.</span><span class="n">fill</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">nan</span><span class="p">)</span>
<span class="n">val_accs</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">empty</span><span class="p">(</span><span class="n">epochs</span><span class="p">)</span>
<span class="n">val_accs</span><span class="o">.</span><span class="n">fill</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">nan</span><span class="p">)</span>
<span class="bp">self</span><span class="o">.</span><span class="n">schedulers_weight</span> <span class="o">=</span> <span class="nb">list</span><span class="p">()</span>
<span class="bp">self</span><span class="o">.</span><span class="n">schedulers_bias</span> <span class="o">=</span> <span class="nb">list</span><span class="p">()</span>
<span class="n">batch_size</span> <span class="o">=</span> <span class="n">X</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">//</span> <span class="n">batches</span>
<span class="n">X</span><span class="p">,</span> <span class="n">t</span> <span class="o">=</span> <span class="n">resample</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">t</span><span class="p">)</span>
<span class="c1"># this function returns a function valued only at X</span>
<span class="n">cost_function_train</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">cost_func</span><span class="p">(</span><span class="n">t</span><span class="p">)</span>
<span class="k">if</span> <span class="n">val_set</span><span class="p">:</span>
<span class="n">cost_function_val</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">cost_func</span><span class="p">(</span><span class="n">t_val</span><span class="p">)</span>
<span class="c1"># create schedulers for each weight matrix</span>
<span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">weights</span><span class="p">)):</span>
<span class="bp">self</span><span class="o">.</span><span class="n">schedulers_weight</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">copy</span><span class="p">(</span><span class="n">scheduler</span><span class="p">))</span>
<span class="bp">self</span><span class="o">.</span><span class="n">schedulers_bias</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">copy</span><span class="p">(</span><span class="n">scheduler</span><span class="p">))</span>
<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="n">scheduler</span><span class="o">.</span><span class="vm">__class__</span><span class="o">.</span><span class="vm">__name__</span><span class="si">}</span><span class="s2">: Eta=</span><span class="si">{</span><span class="n">scheduler</span><span class="o">.</span><span class="n">eta</span><span class="si">}</span><span class="s2">, Lambda=</span><span class="si">{</span><span class="n">lam</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
<span class="k">try</span><span class="p">:</span>
<span class="k">for</span> <span class="n">e</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">epochs</span><span class="p">):</span>
<span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">batches</span><span class="p">):</span>
<span class="c1"># allows for minibatch gradient descent</span>
<span class="k">if</span> <span class="n">i</span> <span class="o">==</span> <span class="n">batches</span> <span class="o">-</span> <span class="mi">1</span><span class="p">:</span>
<span class="c1"># If the for loop has reached the last batch, take all thats left</span>
<span class="n">X_batch</span> <span class="o">=</span> <span class="n">X</span><span class="p">[</span><span class="n">i</span> <span class="o">*</span> <span class="n">batch_size</span> <span class="p">:,</span> <span class="p">:]</span>
<span class="n">t_batch</span> <span class="o">=</span> <span class="n">t</span><span class="p">[</span><span class="n">i</span> <span class="o">*</span> <span class="n">batch_size</span> <span class="p">:,</span> <span class="p">:]</span>
<span class="k">else</span><span class="p">:</span>
<span class="n">X_batch</span> <span class="o">=</span> <span class="n">X</span><span class="p">[</span><span class="n">i</span> <span class="o">*</span> <span class="n">batch_size</span> <span class="p">:</span> <span class="p">(</span><span class="n">i</span> <span class="o">+</span> <span class="mi">1</span><span class="p">)</span> <span class="o">*</span> <span class="n">batch_size</span><span class="p">,</span> <span class="p">:]</span>
<span class="n">t_batch</span> <span class="o">=</span> <span class="n">t</span><span class="p">[</span><span class="n">i</span> <span class="o">*</span> <span class="n">batch_size</span> <span class="p">:</span> <span class="p">(</span><span class="n">i</span> <span class="o">+</span> <span class="mi">1</span><span class="p">)</span> <span class="o">*</span> <span class="n">batch_size</span><span class="p">,</span> <span class="p">:]</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_feedforward</span><span class="p">(</span><span class="n">X_batch</span><span class="p">)</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_backpropagate</span><span class="p">(</span><span class="n">X_batch</span><span class="p">,</span> <span class="n">t_batch</span><span class="p">,</span> <span class="n">lam</span><span class="p">)</span>
<span class="c1"># reset schedulers for each epoch (some schedulers pass in this call)</span>
<span class="k">for</span> <span class="n">scheduler</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">schedulers_weight</span><span class="p">:</span>
<span class="n">scheduler</span><span class="o">.</span><span class="n">reset</span><span class="p">()</span>
<span class="k">for</span> <span class="n">scheduler</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">schedulers_bias</span><span class="p">:</span>
<span class="n">scheduler</span><span class="o">.</span><span class="n">reset</span><span class="p">()</span>
<span class="c1"># computing performance metrics</span>
<span class="n">pred_train</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">X</span><span class="p">)</span>
<span class="n">train_error</span> <span class="o">=</span> <span class="n">cost_function_train</span><span class="p">(</span><span class="n">pred_train</span><span class="p">)</span>
<span class="n">train_errors</span><span class="p">[</span><span class="n">e</span><span class="p">]</span> <span class="o">=</span> <span class="n">train_error</span>
<span class="k">if</span> <span class="n">val_set</span><span class="p">:</span>
<span class="n">pred_val</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">X_val</span><span class="p">)</span>
<span class="n">val_error</span> <span class="o">=</span> <span class="n">cost_function_val</span><span class="p">(</span><span class="n">pred_val</span><span class="p">)</span>
<span class="n">val_errors</span><span class="p">[</span><span class="n">e</span><span class="p">]</span> <span class="o">=</span> <span class="n">val_error</span>
<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">classification</span><span class="p">:</span>
<span class="n">train_acc</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_accuracy</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">X</span><span class="p">),</span> <span class="n">t</span><span class="p">)</span>
<span class="n">train_accs</span><span class="p">[</span><span class="n">e</span><span class="p">]</span> <span class="o">=</span> <span class="n">train_acc</span>
<span class="k">if</span> <span class="n">val_set</span><span class="p">:</span>
<span class="n">val_acc</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_accuracy</span><span class="p">(</span><span class="n">pred_val</span><span class="p">,</span> <span class="n">t_val</span><span class="p">)</span>
<span class="n">val_accs</span><span class="p">[</span><span class="n">e</span><span class="p">]</span> <span class="o">=</span> <span class="n">val_acc</span>
<span class="c1"># printing progress bar</span>
<span class="n">progression</span> <span class="o">=</span> <span class="n">e</span> <span class="o">/</span> <span class="n">epochs</span>
<span class="n">print_length</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_progress_bar</span><span class="p">(</span>
<span class="n">progression</span><span class="p">,</span>
<span class="n">train_error</span><span class="o">=</span><span class="n">train_errors</span><span class="p">[</span><span class="n">e</span><span class="p">],</span>
<span class="n">train_acc</span><span class="o">=</span><span class="n">train_accs</span><span class="p">[</span><span class="n">e</span><span class="p">],</span>
<span class="n">val_error</span><span class="o">=</span><span class="n">val_errors</span><span class="p">[</span><span class="n">e</span><span class="p">],</span>
<span class="n">val_acc</span><span class="o">=</span><span class="n">val_accs</span><span class="p">[</span><span class="n">e</span><span class="p">],</span>
<span class="p">)</span>
<span class="k">except</span> <span class="ne">KeyboardInterrupt</span><span class="p">:</span>
<span class="c1"># allows for stopping training at any point and seeing the result</span>
<span class="k">pass</span>
<span class="c1"># visualization of training progression (similiar to tensorflow progression bar)</span>
<span class="n">sys</span><span class="o">.</span><span class="n">stdout</span><span class="o">.</span><span class="n">write</span><span class="p">(</span><span class="s2">&quot;</span><span class="se">\r</span><span class="s2">&quot;</span> <span class="o">+</span> <span class="s2">&quot; &quot;</span> <span class="o">*</span> <span class="n">print_length</span><span class="p">)</span>
<span class="n">sys</span><span class="o">.</span><span class="n">stdout</span><span class="o">.</span><span class="n">flush</span><span class="p">()</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_progress_bar</span><span class="p">(</span>
<span class="mi">1</span><span class="p">,</span>
<span class="n">train_error</span><span class="o">=</span><span class="n">train_errors</span><span class="p">[</span><span class="n">e</span><span class="p">],</span>
<span class="n">train_acc</span><span class="o">=</span><span class="n">train_accs</span><span class="p">[</span><span class="n">e</span><span class="p">],</span>
<span class="n">val_error</span><span class="o">=</span><span class="n">val_errors</span><span class="p">[</span><span class="n">e</span><span class="p">],</span>
<span class="n">val_acc</span><span class="o">=</span><span class="n">val_accs</span><span class="p">[</span><span class="n">e</span><span class="p">],</span>
<span class="p">)</span>
<span class="n">sys</span><span class="o">.</span><span class="n">stdout</span><span class="o">.</span><span class="n">write</span><span class="p">(</span><span class="s2">&quot;&quot;</span><span class="p">)</span>
<span class="c1"># return performance metrics for the entire run</span>
<span class="n">scores</span> <span class="o">=</span> <span class="nb">dict</span><span class="p">()</span>
<span class="n">scores</span><span class="p">[</span><span class="s2">&quot;train_errors&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">train_errors</span>
<span class="k">if</span> <span class="n">val_set</span><span class="p">:</span>
<span class="n">scores</span><span class="p">[</span><span class="s2">&quot;val_errors&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">val_errors</span>
<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">classification</span><span class="p">:</span>
<span class="n">scores</span><span class="p">[</span><span class="s2">&quot;train_accs&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">train_accs</span>
<span class="k">if</span> <span class="n">val_set</span><span class="p">:</span>
<span class="n">scores</span><span class="p">[</span><span class="s2">&quot;val_accs&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">val_accs</span>
<span class="k">return</span> <span class="n">scores</span>
<span class="k">def</span> <span class="nf">predict</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X</span><span class="p">:</span> <span class="n">np</span><span class="o">.</span><span class="n">ndarray</span><span class="p">,</span> <span class="o">*</span><span class="p">,</span> <span class="n">threshold</span><span class="o">=</span><span class="mf">0.5</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Description:</span>
<span class="sd"> ------------</span>
<span class="sd"> Performs prediction after training of the network has been finished.</span>
<span class="sd"> Parameters:</span>
<span class="sd"> ------------</span>
<span class="sd"> I X (np.ndarray): The design matrix, with n rows of p features each</span>
<span class="sd"> Optional Parameters:</span>
<span class="sd"> ------------</span>
<span class="sd"> II threshold (float) : sets minimal value for a prediction to be predicted as the positive class</span>
<span class="sd"> in classification problems</span>
<span class="sd"> Returns:</span>
<span class="sd"> ------------</span>
<span class="sd"> I z (np.ndarray): A prediction vector (row) for each row in our design matrix</span>
<span class="sd"> This vector is thresholded if regression=False, meaning that classification results</span>
<span class="sd"> in a vector of 1s and 0s, while regressions in an array of decimal numbers</span>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="n">predict</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_feedforward</span><span class="p">(</span><span class="n">X</span><span class="p">)</span>
<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">classification</span><span class="p">:</span>
<span class="k">return</span> <span class="n">np</span><span class="o">.</span><span class="n">where</span><span class="p">(</span><span class="n">predict</span> <span class="o">&gt;</span> <span class="n">threshold</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">0</span><span class="p">)</span>
<span class="k">else</span><span class="p">:</span>
<span class="k">return</span> <span class="n">predict</span>
<span class="k">def</span> <span class="nf">reset_weights</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Description:</span>
<span class="sd"> ------------</span>
<span class="sd"> Resets/Reinitializes the weights in order to train the network for a new problem.</span>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">seed</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
<span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">seed</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">seed</span><span class="p">)</span>
<span class="bp">self</span><span class="o">.</span><span class="n">weights</span> <span class="o">=</span> <span class="nb">list</span><span class="p">()</span>
<span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">dimensions</span><span class="p">)</span> <span class="o">-</span> <span class="mi">1</span><span class="p">):</span>
<span class="n">weight_array</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">randn</span><span class="p">(</span>
<span class="bp">self</span><span class="o">.</span><span class="n">dimensions</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">+</span> <span class="mi">1</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">dimensions</span><span class="p">[</span><span class="n">i</span> <span class="o">+</span> <span class="mi">1</span><span class="p">]</span>
<span class="p">)</span>
<span class="n">weight_array</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="p">:]</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">randn</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">dimensions</span><span class="p">[</span><span class="n">i</span> <span class="o">+</span> <span class="mi">1</span><span class="p">])</span> <span class="o">*</span> <span class="mf">0.01</span>
<span class="bp">self</span><span class="o">.</span><span class="n">weights</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">weight_array</span><span class="p">)</span>
<span class="k">def</span> <span class="nf">_feedforward</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X</span><span class="p">:</span> <span class="n">np</span><span class="o">.</span><span class="n">ndarray</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Description:</span>
<span class="sd"> ------------</span>
<span class="sd"> Calculates the activation of each layer starting at the input and ending at the output.</span>
<span class="sd"> Each following activation is calculated from a weighted sum of each of the preceeding</span>
<span class="sd"> activations (except in the case of the input layer).</span>
<span class="sd"> Parameters:</span>
<span class="sd"> ------------</span>
<span class="sd"> I X (np.ndarray): The design matrix, with n rows of p features each</span>
<span class="sd"> Returns:</span>
<span class="sd"> ------------</span>
<span class="sd"> I z (np.ndarray): A prediction vector (row) for each row in our design matrix</span>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="c1"># reset matrices</span>
<span class="bp">self</span><span class="o">.</span><span class="n">a_matrices</span> <span class="o">=</span> <span class="nb">list</span><span class="p">()</span>
<span class="bp">self</span><span class="o">.</span><span class="n">z_matrices</span> <span class="o">=</span> <span class="nb">list</span><span class="p">()</span>
<span class="c1"># if X is just a vector, make it into a matrix</span>
<span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">X</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span> <span class="o">==</span> <span class="mi">1</span><span class="p">:</span>
<span class="n">X</span> <span class="o">=</span> <span class="n">X</span><span class="o">.</span><span class="n">reshape</span><span class="p">((</span><span class="mi">1</span><span class="p">,</span> <span class="n">X</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]))</span>
<span class="c1"># Add a coloumn of zeros as the first coloumn of the design matrix, in order</span>
<span class="c1"># to add bias to our data</span>
<span class="n">bias</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">ones</span><span class="p">((</span><span class="n">X</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="mi">1</span><span class="p">))</span> <span class="o">*</span> <span class="mf">0.01</span>
<span class="n">X</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">hstack</span><span class="p">([</span><span class="n">bias</span><span class="p">,</span> <span class="n">X</span><span class="p">])</span>
<span class="c1"># a^0, the nodes in the input layer (one a^0 for each row in X - where the</span>
<span class="c1"># exponent indicates layer number).</span>
<span class="n">a</span> <span class="o">=</span> <span class="n">X</span>
<span class="bp">self</span><span class="o">.</span><span class="n">a_matrices</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">a</span><span class="p">)</span>
<span class="bp">self</span><span class="o">.</span><span class="n">z_matrices</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">a</span><span class="p">)</span>
<span class="c1"># The feed forward algorithm</span>
<span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">weights</span><span class="p">)):</span>
<span class="k">if</span> <span class="n">i</span> <span class="o">&lt;</span> <span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">weights</span><span class="p">)</span> <span class="o">-</span> <span class="mi">1</span><span class="p">:</span>
<span class="n">z</span> <span class="o">=</span> <span class="n">a</span> <span class="o">@</span> <span class="bp">self</span><span class="o">.</span><span class="n">weights</span><span class="p">[</span><span class="n">i</span><span class="p">]</span>
<span class="bp">self</span><span class="o">.</span><span class="n">z_matrices</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">z</span><span class="p">)</span>
<span class="n">a</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">hidden_func</span><span class="p">(</span><span class="n">z</span><span class="p">)</span>
<span class="c1"># bias column again added to the data here</span>
<span class="n">bias</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">ones</span><span class="p">((</span><span class="n">a</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="mi">1</span><span class="p">))</span> <span class="o">*</span> <span class="mf">0.01</span>
<span class="n">a</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">hstack</span><span class="p">([</span><span class="n">bias</span><span class="p">,</span> <span class="n">a</span><span class="p">])</span>
<span class="bp">self</span><span class="o">.</span><span class="n">a_matrices</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">a</span><span class="p">)</span>
<span class="k">else</span><span class="p">:</span>
<span class="k">try</span><span class="p">:</span>
<span class="c1"># a^L, the nodes in our output layers</span>
<span class="n">z</span> <span class="o">=</span> <span class="n">a</span> <span class="o">@</span> <span class="bp">self</span><span class="o">.</span><span class="n">weights</span><span class="p">[</span><span class="n">i</span><span class="p">]</span>
<span class="n">a</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">output_func</span><span class="p">(</span><span class="n">z</span><span class="p">)</span>
<span class="bp">self</span><span class="o">.</span><span class="n">a_matrices</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">a</span><span class="p">)</span>
<span class="bp">self</span><span class="o">.</span><span class="n">z_matrices</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">z</span><span class="p">)</span>
<span class="k">except</span> <span class="ne">Exception</span> <span class="k">as</span> <span class="ne">OverflowError</span><span class="p">:</span>
<span class="nb">print</span><span class="p">(</span>
<span class="s2">&quot;OverflowError in fit() in FFNN</span><span class="se">\n</span><span class="s2">HOW TO DEBUG ERROR: Consider lowering your learning rate or scheduler specific parameters such as momentum, or check if your input values need scaling&quot;</span>
<span class="p">)</span>
<span class="c1"># this will be a^L</span>
<span class="k">return</span> <span class="n">a</span>
<span class="k">def</span> <span class="nf">_backpropagate</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X</span><span class="p">,</span> <span class="n">t</span><span class="p">,</span> <span class="n">lam</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Description:</span>
<span class="sd"> ------------</span>
<span class="sd"> Performs the backpropagation algorithm. In other words, this method</span>
<span class="sd"> calculates the gradient of all the layers starting at the</span>
<span class="sd"> output layer, and moving from right to left accumulates the gradient until</span>
<span class="sd"> the input layer is reached. Each layers respective weights are updated while</span>
<span class="sd"> the algorithm propagates backwards from the output layer (auto-differentation in reverse mode).</span>
<span class="sd"> Parameters:</span>
<span class="sd"> ------------</span>
<span class="sd"> I X (np.ndarray): The design matrix, with n rows of p features each.</span>
<span class="sd"> II t (np.ndarray): The target vector, with n rows of p targets.</span>
<span class="sd"> III lam (float32): regularization parameter used to punish the weights in case of overfitting</span>
<span class="sd"> Returns:</span>
<span class="sd"> ------------</span>
<span class="sd"> No return value.</span>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="n">out_derivative</span> <span class="o">=</span> <span class="n">derivate</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">output_func</span><span class="p">)</span>
<span class="n">hidden_derivative</span> <span class="o">=</span> <span class="n">derivate</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">hidden_func</span><span class="p">)</span>
<span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">weights</span><span class="p">)</span> <span class="o">-</span> <span class="mi">1</span><span class="p">,</span> <span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="o">-</span><span class="mi">1</span><span class="p">):</span>
<span class="c1"># delta terms for output</span>
<span class="k">if</span> <span class="n">i</span> <span class="o">==</span> <span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">weights</span><span class="p">)</span> <span class="o">-</span> <span class="mi">1</span><span class="p">:</span>
<span class="c1"># for multi-class classification</span>
<span class="k">if</span> <span class="p">(</span>
<span class="bp">self</span><span class="o">.</span><span class="n">output_func</span><span class="o">.</span><span class="vm">__name__</span> <span class="o">==</span> <span class="s2">&quot;softmax&quot;</span>
<span class="p">):</span>
<span class="n">delta_matrix</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">a_matrices</span><span class="p">[</span><span class="n">i</span> <span class="o">+</span> <span class="mi">1</span><span class="p">]</span> <span class="o">-</span> <span class="n">t</span>
<span class="c1"># for single class classification</span>
<span class="k">else</span><span class="p">:</span>
<span class="n">cost_func_derivative</span> <span class="o">=</span> <span class="n">grad</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">cost_func</span><span class="p">(</span><span class="n">t</span><span class="p">))</span>
<span class="n">delta_matrix</span> <span class="o">=</span> <span class="n">out_derivative</span><span class="p">(</span>
<span class="bp">self</span><span class="o">.</span><span class="n">z_matrices</span><span class="p">[</span><span class="n">i</span> <span class="o">+</span> <span class="mi">1</span><span class="p">]</span>
<span class="p">)</span> <span class="o">*</span> <span class="n">cost_func_derivative</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">a_matrices</span><span class="p">[</span><span class="n">i</span> <span class="o">+</span> <span class="mi">1</span><span class="p">])</span>
<span class="c1"># delta terms for hidden layer</span>
<span class="k">else</span><span class="p">:</span>
<span class="n">delta_matrix</span> <span class="o">=</span> <span class="p">(</span>
<span class="bp">self</span><span class="o">.</span><span class="n">weights</span><span class="p">[</span><span class="n">i</span> <span class="o">+</span> <span class="mi">1</span><span class="p">][</span><span class="mi">1</span><span class="p">:,</span> <span class="p">:]</span> <span class="o">@</span> <span class="n">delta_matrix</span><span class="o">.</span><span class="n">T</span>
<span class="p">)</span><span class="o">.</span><span class="n">T</span> <span class="o">*</span> <span class="n">hidden_derivative</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">z_matrices</span><span class="p">[</span><span class="n">i</span> <span class="o">+</span> <span class="mi">1</span><span class="p">])</span>
<span class="c1"># calculate gradient</span>
<span class="n">gradient_weights</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">a_matrices</span><span class="p">[</span><span class="n">i</span><span class="p">][:,</span> <span class="mi">1</span><span class="p">:]</span><span class="o">.</span><span class="n">T</span> <span class="o">@</span> <span class="n">delta_matrix</span>
<span class="n">gradient_bias</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="n">delta_matrix</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span>
<span class="mi">1</span><span class="p">,</span> <span class="n">delta_matrix</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span>
<span class="p">)</span>
<span class="c1"># regularization term</span>
<span class="n">gradient_weights</span> <span class="o">+=</span> <span class="bp">self</span><span class="o">.</span><span class="n">weights</span><span class="p">[</span><span class="n">i</span><span class="p">][</span><span class="mi">1</span><span class="p">:,</span> <span class="p">:]</span> <span class="o">*</span> <span class="n">lam</span>
<span class="c1"># use scheduler</span>
<span class="n">update_matrix</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">vstack</span><span class="p">(</span>
<span class="p">[</span>
<span class="bp">self</span><span class="o">.</span><span class="n">schedulers_bias</span><span class="p">[</span><span class="n">i</span><span class="p">]</span><span class="o">.</span><span class="n">update_change</span><span class="p">(</span><span class="n">gradient_bias</span><span class="p">),</span>
<span class="bp">self</span><span class="o">.</span><span class="n">schedulers_weight</span><span class="p">[</span><span class="n">i</span><span class="p">]</span><span class="o">.</span><span class="n">update_change</span><span class="p">(</span><span class="n">gradient_weights</span><span class="p">),</span>
<span class="p">]</span>
<span class="p">)</span>
<span class="c1"># update weights and bias</span>
<span class="bp">self</span><span class="o">.</span><span class="n">weights</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">-=</span> <span class="n">update_matrix</span>
<span class="k">def</span> <span class="nf">_accuracy</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">prediction</span><span class="p">:</span> <span class="n">np</span><span class="o">.</span><span class="n">ndarray</span><span class="p">,</span> <span class="n">target</span><span class="p">:</span> <span class="n">np</span><span class="o">.</span><span class="n">ndarray</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Description:</span>
<span class="sd"> ------------</span>
<span class="sd"> Calculates accuracy of given prediction to target</span>
<span class="sd"> Parameters:</span>
<span class="sd"> ------------</span>
<span class="sd"> I prediction (np.ndarray): vector of predicitons output network</span>
<span class="sd"> (1s and 0s in case of classification, and real numbers in case of regression)</span>
<span class="sd"> II target (np.ndarray): vector of true values (What the network ideally should predict)</span>
<span class="sd"> Returns:</span>
<span class="sd"> ------------</span>
<span class="sd"> A floating point number representing the percentage of correctly classified instances.</span>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="k">assert</span> <span class="n">prediction</span><span class="o">.</span><span class="n">size</span> <span class="o">==</span> <span class="n">target</span><span class="o">.</span><span class="n">size</span>
<span class="k">return</span> <span class="n">np</span><span class="o">.</span><span class="n">average</span><span class="p">((</span><span class="n">target</span> <span class="o">==</span> <span class="n">prediction</span><span class="p">))</span>
<span class="k">def</span> <span class="nf">_set_classification</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Description:</span>
<span class="sd"> ------------</span>
<span class="sd"> Decides if FFNN acts as classifier (True) og regressor (False),</span>
<span class="sd"> sets self.classification during init()</span>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="bp">self</span><span class="o">.</span><span class="n">classification</span> <span class="o">=</span> <span class="kc">False</span>
<span class="k">if</span> <span class="p">(</span>
<span class="bp">self</span><span class="o">.</span><span class="n">cost_func</span><span class="o">.</span><span class="vm">__name__</span> <span class="o">==</span> <span class="s2">&quot;CostLogReg&quot;</span>
<span class="ow">or</span> <span class="bp">self</span><span class="o">.</span><span class="n">cost_func</span><span class="o">.</span><span class="vm">__name__</span> <span class="o">==</span> <span class="s2">&quot;CostCrossEntropy&quot;</span>
<span class="p">):</span>
<span class="bp">self</span><span class="o">.</span><span class="n">classification</span> <span class="o">=</span> <span class="kc">True</span>
<span class="k">def</span> <span class="nf">_progress_bar</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">progression</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Description:</span>
<span class="sd"> ------------</span>
<span class="sd"> Displays progress of training</span>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="n">print_length</span> <span class="o">=</span> <span class="mi">40</span>
<span class="n">num_equals</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="n">progression</span> <span class="o">*</span> <span class="n">print_length</span><span class="p">)</span>
<span class="n">num_not</span> <span class="o">=</span> <span class="n">print_length</span> <span class="o">-</span> <span class="n">num_equals</span>
<span class="n">arrow</span> <span class="o">=</span> <span class="s2">&quot;&gt;&quot;</span> <span class="k">if</span> <span class="n">num_equals</span> <span class="o">&gt;</span> <span class="mi">0</span> <span class="k">else</span> <span class="s2">&quot;&quot;</span>
<span class="n">bar</span> <span class="o">=</span> <span class="s2">&quot;[&quot;</span> <span class="o">+</span> <span class="s2">&quot;=&quot;</span> <span class="o">*</span> <span class="p">(</span><span class="n">num_equals</span> <span class="o">-</span> <span class="mi">1</span><span class="p">)</span> <span class="o">+</span> <span class="n">arrow</span> <span class="o">+</span> <span class="s2">&quot;-&quot;</span> <span class="o">*</span> <span class="n">num_not</span> <span class="o">+</span> <span class="s2">&quot;]&quot;</span>
<span class="n">perc_print</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_format</span><span class="p">(</span><span class="n">progression</span> <span class="o">*</span> <span class="mi">100</span><span class="p">,</span> <span class="n">decimals</span><span class="o">=</span><span class="mi">5</span><span class="p">)</span>
<span class="n">line</span> <span class="o">=</span> <span class="sa">f</span><span class="s2">&quot; </span><span class="si">{</span><span class="n">bar</span><span class="si">}</span><span class="s2"> </span><span class="si">{</span><span class="n">perc_print</span><span class="si">}</span><span class="s2">% &quot;</span>
<span class="k">for</span> <span class="n">key</span> <span class="ow">in</span> <span class="n">kwargs</span><span class="p">:</span>
<span class="k">if</span> <span class="ow">not</span> <span class="n">np</span><span class="o">.</span><span class="n">isnan</span><span class="p">(</span><span class="n">kwargs</span><span class="p">[</span><span class="n">key</span><span class="p">]):</span>
<span class="n">value</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_format</span><span class="p">(</span><span class="n">kwargs</span><span class="p">[</span><span class="n">key</span><span class="p">],</span> <span class="n">decimals</span><span class="o">=</span><span class="mi">4</span><span class="p">)</span>
<span class="n">line</span> <span class="o">+=</span> <span class="sa">f</span><span class="s2">&quot;| </span><span class="si">{</span><span class="n">key</span><span class="si">}</span><span class="s2">: </span><span class="si">{</span><span class="n">value</span><span class="si">}</span><span class="s2"> &quot;</span>
<span class="n">sys</span><span class="o">.</span><span class="n">stdout</span><span class="o">.</span><span class="n">write</span><span class="p">(</span><span class="s2">&quot;</span><span class="se">\r</span><span class="s2">&quot;</span> <span class="o">+</span> <span class="n">line</span><span class="p">)</span>
<span class="n">sys</span><span class="o">.</span><span class="n">stdout</span><span class="o">.</span><span class="n">flush</span><span class="p">()</span>
<span class="k">return</span> <span class="nb">len</span><span class="p">(</span><span class="n">line</span><span class="p">)</span>
<span class="k">def</span> <span class="nf">_format</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">value</span><span class="p">,</span> <span class="n">decimals</span><span class="o">=</span><span class="mi">4</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Description:</span>
<span class="sd"> ------------</span>
<span class="sd"> Formats decimal numbers for progress bar</span>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="k">if</span> <span class="n">value</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="p">:</span>
<span class="n">v</span> <span class="o">=</span> <span class="n">value</span>
<span class="k">elif</span> <span class="n">value</span> <span class="o">&lt;</span> <span class="mi">0</span><span class="p">:</span>
<span class="n">v</span> <span class="o">=</span> <span class="o">-</span><span class="mi">10</span> <span class="o">*</span> <span class="n">value</span>
<span class="k">else</span><span class="p">:</span>
<span class="n">v</span> <span class="o">=</span> <span class="mi">1</span>
<span class="n">n</span> <span class="o">=</span> <span class="mi">1</span> <span class="o">+</span> <span class="n">math</span><span class="o">.</span><span class="n">floor</span><span class="p">(</span><span class="n">math</span><span class="o">.</span><span class="n">log10</span><span class="p">(</span><span class="n">v</span><span class="p">))</span>
<span class="k">if</span> <span class="n">n</span> <span class="o">&gt;=</span> <span class="n">decimals</span> <span class="o">-</span> <span class="mi">1</span><span class="p">:</span>
<span class="k">return</span> <span class="nb">str</span><span class="p">(</span><span class="nb">round</span><span class="p">(</span><span class="n">value</span><span class="p">))</span>
<span class="k">return</span> <span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="n">value</span><span class="si">:</span><span class="s2">.</span><span class="si">{</span><span class="n">decimals</span><span class="o">-</span><span class="n">n</span><span class="o">-</span><span class="mi">1</span><span class="si">}</span><span class="s2">f</span><span class="si">}</span><span class="s2">&quot;</span>
</pre></div>
</div>
</div>
</div>
<p>Before we make a model, we will quickly generate a dataset we can use
for our linear regression problem as shown below</p>
<div class="cell docutils container">
<div class="cell_input docutils container">
<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">autograd.numpy</span> <span class="k">as</span> <span class="nn">np</span>
<span class="kn">from</span> <span class="nn">sklearn.model_selection</span> <span class="kn">import</span> <span class="n">train_test_split</span>
<span class="k">def</span> <span class="nf">SkrankeFunction</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">):</span>
<span class="k">return</span> <span class="n">np</span><span class="o">.</span><span class="n">ravel</span><span class="p">(</span><span class="mi">0</span> <span class="o">+</span> <span class="mi">1</span><span class="o">*</span><span class="n">x</span> <span class="o">+</span> <span class="mi">2</span><span class="o">*</span><span class="n">y</span> <span class="o">+</span> <span class="mi">3</span><span class="o">*</span><span class="n">x</span><span class="o">**</span><span class="mi">2</span> <span class="o">+</span> <span class="mi">4</span><span class="o">*</span><span class="n">x</span><span class="o">*</span><span class="n">y</span> <span class="o">+</span> <span class="mi">5</span><span class="o">*</span><span class="n">y</span><span class="o">**</span><span class="mi">2</span><span class="p">)</span>
<span class="k">def</span> <span class="nf">create_X</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="n">n</span><span class="p">):</span>
<span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">x</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span> <span class="o">&gt;</span> <span class="mi">1</span><span class="p">:</span>
<span class="n">x</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">ravel</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
<span class="n">y</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">ravel</span><span class="p">(</span><span class="n">y</span><span class="p">)</span>
<span class="n">N</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
<span class="n">l</span> <span class="o">=</span> <span class="nb">int</span><span class="p">((</span><span class="n">n</span> <span class="o">+</span> <span class="mi">1</span><span class="p">)</span> <span class="o">*</span> <span class="p">(</span><span class="n">n</span> <span class="o">+</span> <span class="mi">2</span><span class="p">)</span> <span class="o">/</span> <span class="mi">2</span><span class="p">)</span> <span class="c1"># Number of elements in beta</span>
<span class="n">X</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">ones</span><span class="p">((</span><span class="n">N</span><span class="p">,</span> <span class="n">l</span><span class="p">))</span>
<span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="n">n</span> <span class="o">+</span> <span class="mi">1</span><span class="p">):</span>
<span class="n">q</span> <span class="o">=</span> <span class="nb">int</span><span class="p">((</span><span class="n">i</span><span class="p">)</span> <span class="o">*</span> <span class="p">(</span><span class="n">i</span> <span class="o">+</span> <span class="mi">1</span><span class="p">)</span> <span class="o">/</span> <span class="mi">2</span><span class="p">)</span>
<span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">i</span> <span class="o">+</span> <span class="mi">1</span><span class="p">):</span>
<span class="n">X</span><span class="p">[:,</span> <span class="n">q</span> <span class="o">+</span> <span class="n">k</span><span class="p">]</span> <span class="o">=</span> <span class="p">(</span><span class="n">x</span> <span class="o">**</span> <span class="p">(</span><span class="n">i</span> <span class="o">-</span> <span class="n">k</span><span class="p">))</span> <span class="o">*</span> <span class="p">(</span><span class="n">y</span><span class="o">**</span><span class="n">k</span><span class="p">)</span>
<span class="k">return</span> <span class="n">X</span>
<span class="n">step</span><span class="o">=</span><span class="mf">0.5</span>
<span class="n">x</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="n">step</span><span class="p">)</span>
<span class="n">y</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="n">step</span><span class="p">)</span>
<span class="n">x</span><span class="p">,</span> <span class="n">y</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">meshgrid</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">)</span>
<span class="n">target</span> <span class="o">=</span> <span class="n">SkrankeFunction</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">)</span>
<span class="n">target</span> <span class="o">=</span> <span class="n">target</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="n">target</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="mi">1</span><span class="p">)</span>
<span class="n">poly_degree</span><span class="o">=</span><span class="mi">3</span>
<span class="n">X</span> <span class="o">=</span> <span class="n">create_X</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="n">poly_degree</span><span class="p">)</span>
<span class="n">X_train</span><span class="p">,</span> <span class="n">X_test</span><span class="p">,</span> <span class="n">t_train</span><span class="p">,</span> <span class="n">t_test</span> <span class="o">=</span> <span class="n">train_test_split</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">target</span><span class="p">)</span>
</pre></div>
</div>
</div>
</div>
<p>Now that we have our dataset ready for the regression, we can create
our regressor. Note that with the seed parameter, we can make sure our
results stay the same every time we run the neural network. For
inititialization, we simply specify the dimensions (we wish the amount
of input nodes to be equal to the datapoints, and the output to
predict one value).</p>
<div class="cell docutils container">
<div class="cell_input docutils container">
<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">input_nodes</span> <span class="o">=</span> <span class="n">X_train</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span>
<span class="n">output_nodes</span> <span class="o">=</span> <span class="mi">1</span>
<span class="n">linear_regression</span> <span class="o">=</span> <span class="n">FFNN</span><span class="p">((</span><span class="n">input_nodes</span><span class="p">,</span> <span class="n">output_nodes</span><span class="p">),</span> <span class="n">output_func</span><span class="o">=</span><span class="n">identity</span><span class="p">,</span> <span class="n">cost_func</span><span class="o">=</span><span class="n">CostOLS</span><span class="p">,</span> <span class="n">seed</span><span class="o">=</span><span class="mi">2023</span><span class="p">)</span>
</pre></div>
</div>
</div>
</div>
<p>We then fit our model with our training data using the scheduler of our choice.</p>
<div class="cell docutils container">
<div class="cell_input docutils container">
<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">linear_regression</span><span class="o">.</span><span class="n">reset_weights</span><span class="p">()</span> <span class="c1"># reset weights such that previous runs or reruns don&#39;t affect the weights</span>
<span class="n">scheduler</span> <span class="o">=</span> <span class="n">Constant</span><span class="p">(</span><span class="n">eta</span><span class="o">=</span><span class="mf">1e-3</span><span class="p">)</span>
<span class="n">scores</span> <span class="o">=</span> <span class="n">linear_regression</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X_train</span><span class="p">,</span> <span class="n">t_train</span><span class="p">,</span> <span class="n">scheduler</span><span class="p">)</span>
</pre></div>
</div>
</div>
<div class="cell_output docutils container">
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Constant: Eta=0.001, Lambda=0
[----------------------------------------] 0.000% | train_error: 3.69
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [----------------------------------------] 1.000% | train_error: 3.67
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [----------------------------------------] 2.000% | train_error: 3.65
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [&gt;---------------------------------------] 3.000% | train_error: 3.64
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [&gt;---------------------------------------] 4.000% | train_error: 3.62
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=&gt;--------------------------------------] 5.000% | train_error: 3.60
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=&gt;--------------------------------------] 6.000% | train_error: 3.58
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=&gt;--------------------------------------] 7.000% | train_error: 3.57
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==&gt;-------------------------------------] 8.000% | train_error: 3.55
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==&gt;-------------------------------------] 9.000% | train_error: 3.53
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===&gt;------------------------------------] 10.00% | train_error: 3.52
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===&gt;------------------------------------] 11.00% | train_error: 3.50
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===&gt;------------------------------------] 12.00% | train_error: 3.48
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [====&gt;-----------------------------------] 13.00% | train_error: 3.47
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [====&gt;-----------------------------------] 14.00% | train_error: 3.45
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=====&gt;----------------------------------] 15.00% | train_error: 3.43
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=====&gt;----------------------------------] 16.00% | train_error: 3.42
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=====&gt;----------------------------------] 17.00% | train_error: 3.40
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [======&gt;---------------------------------] 18.00% | train_error: 3.38
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [======&gt;---------------------------------] 19.00% | train_error: 3.37
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=======&gt;--------------------------------] 20.00% | train_error: 3.35
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=======&gt;--------------------------------] 21.00% | train_error: 3.34
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=======&gt;--------------------------------] 22.00% | train_error: 3.32
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [========&gt;-------------------------------] 23.00% | train_error: 3.31
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [========&gt;-------------------------------] 24.00% | train_error: 3.29
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=========&gt;------------------------------] 25.00% | train_error: 3.27
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=========&gt;------------------------------] 26.00% | train_error: 3.26
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=========&gt;------------------------------] 27.00% | train_error: 3.24
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==========&gt;-----------------------------] 28.00% | train_error: 3.23
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==========&gt;-----------------------------] 29.00% | train_error: 3.21
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===========&gt;----------------------------] 30.00% | train_error: 3.20
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===========&gt;----------------------------] 31.00% | train_error: 3.18
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===========&gt;----------------------------] 32.00% | train_error: 3.17
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [============&gt;---------------------------] 33.00% | train_error: 3.15
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [============&gt;---------------------------] 34.00% | train_error: 3.14
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=============&gt;--------------------------] 35.00% | train_error: 3.12
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=============&gt;--------------------------] 36.00% | train_error: 3.11
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=============&gt;--------------------------] 37.00% | train_error: 3.09
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==============&gt;-------------------------] 38.00% | train_error: 3.08
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==============&gt;-------------------------] 39.00% | train_error: 3.06
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===============&gt;------------------------] 40.00% | train_error: 3.05
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===============&gt;------------------------] 41.00% | train_error: 3.03
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===============&gt;------------------------] 42.00% | train_error: 3.02
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [================&gt;-----------------------] 43.00% | train_error: 3.00
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [================&gt;-----------------------] 44.00% | train_error: 2.99
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=================&gt;----------------------] 45.00% | train_error: 2.98
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=================&gt;----------------------] 46.00% | train_error: 2.96
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=================&gt;----------------------] 47.00% | train_error: 2.95
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==================&gt;---------------------] 48.00% | train_error: 2.93
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==================&gt;---------------------] 49.00% | train_error: 2.92
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===================&gt;--------------------] 50.00% | train_error: 2.91
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===================&gt;--------------------] 51.00% | train_error: 2.89
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===================&gt;--------------------] 52.00% | train_error: 2.88
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [====================&gt;-------------------] 53.00% | train_error: 2.86
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [====================&gt;-------------------] 54.00% | train_error: 2.85
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=====================&gt;------------------] 55.00% | train_error: 2.84
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=====================&gt;------------------] 56.00% | train_error: 2.82
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=====================&gt;------------------] 57.00% | train_error: 2.81
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [======================&gt;-----------------] 58.00% | train_error: 2.80
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [======================&gt;-----------------] 59.00% | train_error: 2.78
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=======================&gt;----------------] 60.00% | train_error: 2.77
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=======================&gt;----------------] 61.00% | train_error: 2.76
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=======================&gt;----------------] 62.00% | train_error: 2.74
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [========================&gt;---------------] 63.00% | train_error: 2.73
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [========================&gt;---------------] 64.00% | train_error: 2.72
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=========================&gt;--------------] 65.00% | train_error: 2.70
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=========================&gt;--------------] 66.00% | train_error: 2.69
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=========================&gt;--------------] 67.00% | train_error: 2.68
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==========================&gt;-------------] 68.00% | train_error: 2.67
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==========================&gt;-------------] 69.00% | train_error: 2.65
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===========================&gt;------------] 70.00% | train_error: 2.64
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===========================&gt;------------] 71.00% | train_error: 2.63
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===========================&gt;------------] 72.00% | train_error: 2.62
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [============================&gt;-----------] 73.00% | train_error: 2.60
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [============================&gt;-----------] 74.00% | train_error: 2.59
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=============================&gt;----------] 75.00% | train_error: 2.58
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=============================&gt;----------] 76.00% | train_error: 2.57
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=============================&gt;----------] 77.00% | train_error: 2.55
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==============================&gt;---------] 78.00% | train_error: 2.54
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==============================&gt;---------] 79.00% | train_error: 2.53
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===============================&gt;--------] 80.00% | train_error: 2.52
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===============================&gt;--------] 81.00% | train_error: 2.51
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===============================&gt;--------] 82.00% | train_error: 2.49
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [================================&gt;-------] 83.00% | train_error: 2.48
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [================================&gt;-------] 84.00% | train_error: 2.47
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=================================&gt;------] 85.00% | train_error: 2.46
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=================================&gt;------] 86.00% | train_error: 2.45
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=================================&gt;------] 87.00% | train_error: 2.44
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==================================&gt;-----] 88.00% | train_error: 2.42
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==================================&gt;-----] 89.00% | train_error: 2.41
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===================================&gt;----] 90.00% | train_error: 2.40
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===================================&gt;----] 91.00% | train_error: 2.39
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===================================&gt;----] 92.00% | train_error: 2.38
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [====================================&gt;---] 93.00% | train_error: 2.37
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [====================================&gt;---] 94.00% | train_error: 2.36
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=====================================&gt;--] 95.00% | train_error: 2.34
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=====================================&gt;--] 96.00% | train_error: 2.33
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=====================================&gt;--] 97.00% | train_error: 2.32
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [======================================&gt;-] 98.00% | train_error: 2.31
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [======================================&gt;-] 99.00% | train_error: 2.30
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=======================================&gt;] 100.0% | train_error: 2.30
</pre></div>
</div>
</div>
</div>
<p>Due to the progress bar we can see the MSE (train_error) throughout
the FFNN’s training. Note that the fit() function has some optional
parameters with defualt arguments. For example, the regularization
hyperparameter can be left ignored if not needed, and equally the FFNN
will by default run for 100 epochs. These can easily be changed, such
as for example:</p>
<div class="cell docutils container">
<div class="cell_input docutils container">
<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">linear_regression</span><span class="o">.</span><span class="n">reset_weights</span><span class="p">()</span> <span class="c1"># reset weights such that previous runs or reruns don&#39;t affect the weights</span>
<span class="n">scores</span> <span class="o">=</span> <span class="n">linear_regression</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X_train</span><span class="p">,</span> <span class="n">t_train</span><span class="p">,</span> <span class="n">scheduler</span><span class="p">,</span> <span class="n">lam</span><span class="o">=</span><span class="mf">1e-4</span><span class="p">,</span> <span class="n">epochs</span><span class="o">=</span><span class="mi">1000</span><span class="p">)</span>
</pre></div>
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<div class="cell_output docutils container">
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Constant: Eta=0.001, Lambda=0.0001
[----------------------------------------] 0.000% | train_error: 3.69
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [----------------------------------------] 0.1000% | train_error: 3.67
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [----------------------------------------] 0.2000% | train_error: 3.65
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [----------------------------------------] 0.3000% | train_error: 3.64
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [----------------------------------------] 0.4000% | train_error: 3.62
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [----------------------------------------] 0.5000% | train_error: 3.60
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [----------------------------------------] 0.6000% | train_error: 3.58
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [----------------------------------------] 0.7000% | train_error: 3.57
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [----------------------------------------] 0.8000% | train_error: 3.55
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [----------------------------------------] 0.9000% | train_error: 3.53
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [----------------------------------------] 1.000% | train_error: 3.52
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [----------------------------------------] 1.100% | train_error: 3.50
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [----------------------------------------] 1.200% | train_error: 3.48
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [----------------------------------------] 1.300% | train_error: 3.47
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [----------------------------------------] 1.400% | train_error: 3.45
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [----------------------------------------] 1.500% | train_error: 3.43
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [----------------------------------------] 1.600% | train_error: 3.42
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [----------------------------------------] 1.700% | train_error: 3.40
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [----------------------------------------] 1.800% | train_error: 3.38
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [----------------------------------------] 1.900% | train_error: 3.37
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [----------------------------------------] 2.000% | train_error: 3.35
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [----------------------------------------] 2.100% | train_error: 3.34
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [----------------------------------------] 2.200% | train_error: 3.32
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [----------------------------------------] 2.300% | train_error: 3.31
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [----------------------------------------] 2.400% | train_error: 3.29
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [&gt;---------------------------------------] 2.500% | train_error: 3.27
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [&gt;---------------------------------------] 2.600% | train_error: 3.26
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [&gt;---------------------------------------] 2.700% | train_error: 3.24
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [&gt;---------------------------------------] 2.800% | train_error: 3.23
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [&gt;---------------------------------------] 2.900% | train_error: 3.21
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [&gt;---------------------------------------] 3.000% | train_error: 3.20
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [&gt;---------------------------------------] 3.100% | train_error: 3.18
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [&gt;---------------------------------------] 3.200% | train_error: 3.17
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [&gt;---------------------------------------] 3.300% | train_error: 3.15
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [&gt;---------------------------------------] 3.400% | train_error: 3.14
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [&gt;---------------------------------------] 3.500% | train_error: 3.12
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [&gt;---------------------------------------] 3.600% | train_error: 3.11
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [&gt;---------------------------------------] 3.700% | train_error: 3.09
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [&gt;---------------------------------------] 3.800% | train_error: 3.08
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [&gt;---------------------------------------] 3.900% | train_error: 3.06
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [&gt;---------------------------------------] 4.000% | train_error: 3.05
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [&gt;---------------------------------------] 4.100% | train_error: 3.03
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [&gt;---------------------------------------] 4.200% | train_error: 3.02
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [&gt;---------------------------------------] 4.300% | train_error: 3.00
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [&gt;---------------------------------------] 4.400% | train_error: 2.99
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [&gt;---------------------------------------] 4.500% | train_error: 2.98
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [&gt;---------------------------------------] 4.600% | train_error: 2.96
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [&gt;---------------------------------------] 4.700% | train_error: 2.95
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [&gt;---------------------------------------] 4.800% | train_error: 2.93
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [&gt;---------------------------------------] 4.900% | train_error: 2.92
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=&gt;--------------------------------------] 5.000% | train_error: 2.91
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=&gt;--------------------------------------] 5.100% | train_error: 2.89
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=&gt;--------------------------------------] 5.200% | train_error: 2.88
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=&gt;--------------------------------------] 5.300% | train_error: 2.86
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=&gt;--------------------------------------] 5.400% | train_error: 2.85
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=&gt;--------------------------------------] 5.500% | train_error: 2.84
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=&gt;--------------------------------------] 5.600% | train_error: 2.82
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=&gt;--------------------------------------] 5.700% | train_error: 2.81
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=&gt;--------------------------------------] 5.800% | train_error: 2.80
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=&gt;--------------------------------------] 5.900% | train_error: 2.78
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=&gt;--------------------------------------] 6.000% | train_error: 2.77
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=&gt;--------------------------------------] 6.100% | train_error: 2.76
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=&gt;--------------------------------------] 6.200% | train_error: 2.74
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=&gt;--------------------------------------] 6.300% | train_error: 2.73
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=&gt;--------------------------------------] 6.400% | train_error: 2.72
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=&gt;--------------------------------------] 6.500% | train_error: 2.70
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=&gt;--------------------------------------] 6.600% | train_error: 2.69
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=&gt;--------------------------------------] 6.700% | train_error: 2.68
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=&gt;--------------------------------------] 6.800% | train_error: 2.67
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=&gt;--------------------------------------] 6.900% | train_error: 2.65
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=&gt;--------------------------------------] 7.000% | train_error: 2.64
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=&gt;--------------------------------------] 7.100% | train_error: 2.63
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=&gt;--------------------------------------] 7.200% | train_error: 2.62
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=&gt;--------------------------------------] 7.300% | train_error: 2.60
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=&gt;--------------------------------------] 7.400% | train_error: 2.59
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==&gt;-------------------------------------] 7.500% | train_error: 2.58
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==&gt;-------------------------------------] 7.600% | train_error: 2.57
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==&gt;-------------------------------------] 7.700% | train_error: 2.55
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==&gt;-------------------------------------] 7.800% | train_error: 2.54
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==&gt;-------------------------------------] 7.900% | train_error: 2.53
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==&gt;-------------------------------------] 8.000% | train_error: 2.52
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==&gt;-------------------------------------] 8.100% | train_error: 2.51
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==&gt;-------------------------------------] 8.200% | train_error: 2.49
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==&gt;-------------------------------------] 8.300% | train_error: 2.48
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==&gt;-------------------------------------] 8.400% | train_error: 2.47
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==&gt;-------------------------------------] 8.500% | train_error: 2.46
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==&gt;-------------------------------------] 8.600% | train_error: 2.45
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==&gt;-------------------------------------] 8.700% | train_error: 2.44
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==&gt;-------------------------------------] 8.800% | train_error: 2.42
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==&gt;-------------------------------------] 8.900% | train_error: 2.41
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==&gt;-------------------------------------] 9.000% | train_error: 2.40
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==&gt;-------------------------------------] 9.100% | train_error: 2.39
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==&gt;-------------------------------------] 9.200% | train_error: 2.38
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==&gt;-------------------------------------] 9.300% | train_error: 2.37
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==&gt;-------------------------------------] 9.400% | train_error: 2.36
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==&gt;-------------------------------------] 9.500% | train_error: 2.34
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==&gt;-------------------------------------] 9.600% | train_error: 2.33
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==&gt;-------------------------------------] 9.700% | train_error: 2.32
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==&gt;-------------------------------------] 9.800% | train_error: 2.31
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==&gt;-------------------------------------] 9.900% | train_error: 2.30
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===&gt;------------------------------------] 10.00% | train_error: 2.29
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===&gt;------------------------------------] 10.10% | train_error: 2.28
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===&gt;------------------------------------] 10.20% | train_error: 2.27
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===&gt;------------------------------------] 10.30% | train_error: 2.26
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===&gt;------------------------------------] 10.40% | train_error: 2.25
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===&gt;------------------------------------] 10.50% | train_error: 2.23
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===&gt;------------------------------------] 10.60% | train_error: 2.22
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===&gt;------------------------------------] 10.70% | train_error: 2.21
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===&gt;------------------------------------] 10.80% | train_error: 2.20
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===&gt;------------------------------------] 10.90% | train_error: 2.19
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===&gt;------------------------------------] 11.00% | train_error: 2.18
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===&gt;------------------------------------] 11.10% | train_error: 2.17
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===&gt;------------------------------------] 11.20% | train_error: 2.16
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===&gt;------------------------------------] 11.30% | train_error: 2.15
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===&gt;------------------------------------] 11.40% | train_error: 2.14
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===&gt;------------------------------------] 11.50% | train_error: 2.13
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===&gt;------------------------------------] 11.60% | train_error: 2.12
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===&gt;------------------------------------] 11.70% | train_error: 2.11
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===&gt;------------------------------------] 11.80% | train_error: 2.10
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===&gt;------------------------------------] 11.90% | train_error: 2.09
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===&gt;------------------------------------] 12.00% | train_error: 2.08
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===&gt;------------------------------------] 12.10% | train_error: 2.07
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===&gt;------------------------------------] 12.20% | train_error: 2.06
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===&gt;------------------------------------] 12.30% | train_error: 2.05
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===&gt;------------------------------------] 12.40% | train_error: 2.04
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [====&gt;-----------------------------------] 12.50% | train_error: 2.03
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [====&gt;-----------------------------------] 12.60% | train_error: 2.02
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [====&gt;-----------------------------------] 12.70% | train_error: 2.01
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [====&gt;-----------------------------------] 12.80% | train_error: 2.00
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [====&gt;-----------------------------------] 12.90% | train_error: 1.99
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [====&gt;-----------------------------------] 13.00% | train_error: 1.98
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [====&gt;-----------------------------------] 13.10% | train_error: 1.97
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [====&gt;-----------------------------------] 13.20% | train_error: 1.96
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [====&gt;-----------------------------------] 13.30% | train_error: 1.96
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [====&gt;-----------------------------------] 13.40% | train_error: 1.95
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [====&gt;-----------------------------------] 13.50% | train_error: 1.94
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [====&gt;-----------------------------------] 13.60% | train_error: 1.93
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [====&gt;-----------------------------------] 13.70% | train_error: 1.92
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [====&gt;-----------------------------------] 13.80% | train_error: 1.91
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [====&gt;-----------------------------------] 13.90% | train_error: 1.90
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [====&gt;-----------------------------------] 14.00% | train_error: 1.89
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [====&gt;-----------------------------------] 14.10% | train_error: 1.88
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [====&gt;-----------------------------------] 14.20% | train_error: 1.87
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [====&gt;-----------------------------------] 14.30% | train_error: 1.86
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [====&gt;-----------------------------------] 14.40% | train_error: 1.86
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [====&gt;-----------------------------------] 14.50% | train_error: 1.85
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [====&gt;-----------------------------------] 14.60% | train_error: 1.84
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [====&gt;-----------------------------------] 14.70% | train_error: 1.83
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [====&gt;-----------------------------------] 14.80% | train_error: 1.82
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [====&gt;-----------------------------------] 14.90% | train_error: 1.81
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=====&gt;----------------------------------] 15.00% | train_error: 1.80
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=====&gt;----------------------------------] 15.10% | train_error: 1.79
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=====&gt;----------------------------------] 15.20% | train_error: 1.79
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=====&gt;----------------------------------] 15.30% | train_error: 1.78
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=====&gt;----------------------------------] 15.40% | train_error: 1.77
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=====&gt;----------------------------------] 15.50% | train_error: 1.76
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=====&gt;----------------------------------] 15.60% | train_error: 1.75
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=====&gt;----------------------------------] 15.70% | train_error: 1.74
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=====&gt;----------------------------------] 15.80% | train_error: 1.74
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=====&gt;----------------------------------] 15.90% | train_error: 1.73
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=====&gt;----------------------------------] 16.00% | train_error: 1.72
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=====&gt;----------------------------------] 16.10% | train_error: 1.71
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=====&gt;----------------------------------] 16.20% | train_error: 1.70
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=====&gt;----------------------------------] 16.30% | train_error: 1.69
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=====&gt;----------------------------------] 16.40% | train_error: 1.69
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=====&gt;----------------------------------] 16.50% | train_error: 1.68
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=====&gt;----------------------------------] 16.60% | train_error: 1.67
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=====&gt;----------------------------------] 16.70% | train_error: 1.66
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=====&gt;----------------------------------] 16.80% | train_error: 1.65
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=====&gt;----------------------------------] 16.90% | train_error: 1.65
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=====&gt;----------------------------------] 17.00% | train_error: 1.64
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=====&gt;----------------------------------] 17.10% | train_error: 1.63
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=====&gt;----------------------------------] 17.20% | train_error: 1.62
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=====&gt;----------------------------------] 17.30% | train_error: 1.62
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=====&gt;----------------------------------] 17.40% | train_error: 1.61
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [======&gt;---------------------------------] 17.50% | train_error: 1.60
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [======&gt;---------------------------------] 17.60% | train_error: 1.59
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [======&gt;---------------------------------] 17.70% | train_error: 1.59
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [======&gt;---------------------------------] 17.80% | train_error: 1.58
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [======&gt;---------------------------------] 17.90% | train_error: 1.57
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [======&gt;---------------------------------] 18.00% | train_error: 1.56
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [======&gt;---------------------------------] 18.10% | train_error: 1.56
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [======&gt;---------------------------------] 18.20% | train_error: 1.55
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [======&gt;---------------------------------] 18.30% | train_error: 1.54
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [======&gt;---------------------------------] 18.40% | train_error: 1.53
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [======&gt;---------------------------------] 18.50% | train_error: 1.53
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [======&gt;---------------------------------] 18.60% | train_error: 1.52
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [======&gt;---------------------------------] 18.70% | train_error: 1.51
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [======&gt;---------------------------------] 18.80% | train_error: 1.50
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [======&gt;---------------------------------] 18.90% | train_error: 1.50
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [======&gt;---------------------------------] 19.00% | train_error: 1.49
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [======&gt;---------------------------------] 19.10% | train_error: 1.48
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [======&gt;---------------------------------] 19.20% | train_error: 1.48
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [======&gt;---------------------------------] 19.30% | train_error: 1.47
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [======&gt;---------------------------------] 19.40% | train_error: 1.46
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [======&gt;---------------------------------] 19.50% | train_error: 1.46
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [======&gt;---------------------------------] 19.60% | train_error: 1.45
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [======&gt;---------------------------------] 19.70% | train_error: 1.44
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [======&gt;---------------------------------] 19.80% | train_error: 1.43
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [======&gt;---------------------------------] 19.90% | train_error: 1.43
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=======&gt;--------------------------------] 20.00% | train_error: 1.42
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=======&gt;--------------------------------] 20.10% | train_error: 1.41
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=======&gt;--------------------------------] 20.20% | train_error: 1.41
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=======&gt;--------------------------------] 20.30% | train_error: 1.40
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=======&gt;--------------------------------] 20.40% | train_error: 1.39
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=======&gt;--------------------------------] 20.50% | train_error: 1.39
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=======&gt;--------------------------------] 20.60% | train_error: 1.38
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=======&gt;--------------------------------] 20.70% | train_error: 1.37
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=======&gt;--------------------------------] 20.80% | train_error: 1.37
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=======&gt;--------------------------------] 20.90% | train_error: 1.36
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=======&gt;--------------------------------] 21.00% | train_error: 1.35
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=======&gt;--------------------------------] 21.10% | train_error: 1.35
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=======&gt;--------------------------------] 21.20% | train_error: 1.34
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=======&gt;--------------------------------] 21.30% | train_error: 1.34
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=======&gt;--------------------------------] 21.40% | train_error: 1.33
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=======&gt;--------------------------------] 21.50% | train_error: 1.32
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=======&gt;--------------------------------] 21.60% | train_error: 1.32
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=======&gt;--------------------------------] 21.70% | train_error: 1.31
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=======&gt;--------------------------------] 21.80% | train_error: 1.30
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=======&gt;--------------------------------] 21.90% | train_error: 1.30
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=======&gt;--------------------------------] 22.00% | train_error: 1.29
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=======&gt;--------------------------------] 22.10% | train_error: 1.29
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=======&gt;--------------------------------] 22.20% | train_error: 1.28
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=======&gt;--------------------------------] 22.30% | train_error: 1.27
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=======&gt;--------------------------------] 22.40% | train_error: 1.27
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [========&gt;-------------------------------] 22.50% | train_error: 1.26
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [========&gt;-------------------------------] 22.60% | train_error: 1.26
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [========&gt;-------------------------------] 22.70% | train_error: 1.25
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [========&gt;-------------------------------] 22.80% | train_error: 1.24
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [========&gt;-------------------------------] 22.90% | train_error: 1.24
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [========&gt;-------------------------------] 23.00% | train_error: 1.23
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [========&gt;-------------------------------] 23.10% | train_error: 1.23
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [========&gt;-------------------------------] 23.20% | train_error: 1.22
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [========&gt;-------------------------------] 23.30% | train_error: 1.21
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [========&gt;-------------------------------] 23.40% | train_error: 1.21
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [========&gt;-------------------------------] 23.50% | train_error: 1.20
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [========&gt;-------------------------------] 23.60% | train_error: 1.20
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [========&gt;-------------------------------] 23.70% | train_error: 1.19
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [========&gt;-------------------------------] 23.80% | train_error: 1.19
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [========&gt;-------------------------------] 23.90% | train_error: 1.18
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [========&gt;-------------------------------] 24.00% | train_error: 1.17
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [========&gt;-------------------------------] 24.10% | train_error: 1.17
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [========&gt;-------------------------------] 24.20% | train_error: 1.16
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [========&gt;-------------------------------] 24.30% | train_error: 1.16
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [========&gt;-------------------------------] 24.40% | train_error: 1.15
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [========&gt;-------------------------------] 24.50% | train_error: 1.15
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [========&gt;-------------------------------] 24.60% | train_error: 1.14
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [========&gt;-------------------------------] 24.70% | train_error: 1.14
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [========&gt;-------------------------------] 24.80% | train_error: 1.13
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [========&gt;-------------------------------] 24.90% | train_error: 1.13
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=========&gt;------------------------------] 25.00% | train_error: 1.12
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=========&gt;------------------------------] 25.10% | train_error: 1.11
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=========&gt;------------------------------] 25.20% | train_error: 1.11
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=========&gt;------------------------------] 25.30% | train_error: 1.10
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=========&gt;------------------------------] 25.40% | train_error: 1.10
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=========&gt;------------------------------] 25.50% | train_error: 1.09
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=========&gt;------------------------------] 25.60% | train_error: 1.09
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=========&gt;------------------------------] 25.70% | train_error: 1.08
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=========&gt;------------------------------] 25.80% | train_error: 1.08
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=========&gt;------------------------------] 25.90% | train_error: 1.07
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=========&gt;------------------------------] 26.00% | train_error: 1.07
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=========&gt;------------------------------] 26.10% | train_error: 1.06
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=========&gt;------------------------------] 26.20% | train_error: 1.06
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=========&gt;------------------------------] 26.30% | train_error: 1.05
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=========&gt;------------------------------] 26.40% | train_error: 1.05
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=========&gt;------------------------------] 26.50% | train_error: 1.04
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=========&gt;------------------------------] 26.60% | train_error: 1.04
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=========&gt;------------------------------] 26.70% | train_error: 1.03
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=========&gt;------------------------------] 26.80% | train_error: 1.03
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=========&gt;------------------------------] 26.90% | train_error: 1.02
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=========&gt;------------------------------] 27.00% | train_error: 1.02
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=========&gt;------------------------------] 27.10% | train_error: 1.01
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=========&gt;------------------------------] 27.20% | train_error: 1.01
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=========&gt;------------------------------] 27.30% | train_error: 1.00
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=========&gt;------------------------------] 27.40% | train_error: 0.999
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==========&gt;-----------------------------] 27.50% | train_error: 0.994
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==========&gt;-----------------------------] 27.60% | train_error: 0.990
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==========&gt;-----------------------------] 27.70% | train_error: 0.985
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==========&gt;-----------------------------] 27.80% | train_error: 0.980
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==========&gt;-----------------------------] 27.90% | train_error: 0.976
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==========&gt;-----------------------------] 28.00% | train_error: 0.971
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==========&gt;-----------------------------] 28.10% | train_error: 0.966
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==========&gt;-----------------------------] 28.20% | train_error: 0.962
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==========&gt;-----------------------------] 28.30% | train_error: 0.957
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==========&gt;-----------------------------] 28.40% | train_error: 0.953
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==========&gt;-----------------------------] 28.50% | train_error: 0.948
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==========&gt;-----------------------------] 28.60% | train_error: 0.944
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==========&gt;-----------------------------] 28.70% | train_error: 0.939
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==========&gt;-----------------------------] 28.80% | train_error: 0.935
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==========&gt;-----------------------------] 28.90% | train_error: 0.930
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==========&gt;-----------------------------] 29.00% | train_error: 0.926
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==========&gt;-----------------------------] 29.10% | train_error: 0.922
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==========&gt;-----------------------------] 29.20% | train_error: 0.917
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==========&gt;-----------------------------] 29.30% | train_error: 0.913
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==========&gt;-----------------------------] 29.40% | train_error: 0.909
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==========&gt;-----------------------------] 29.50% | train_error: 0.904
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==========&gt;-----------------------------] 29.60% | train_error: 0.900
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==========&gt;-----------------------------] 29.70% | train_error: 0.896
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==========&gt;-----------------------------] 29.80% | train_error: 0.891
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==========&gt;-----------------------------] 29.90% | train_error: 0.887
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===========&gt;----------------------------] 30.00% | train_error: 0.883
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===========&gt;----------------------------] 30.10% | train_error: 0.879
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===========&gt;----------------------------] 30.20% | train_error: 0.875
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===========&gt;----------------------------] 30.30% | train_error: 0.870
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===========&gt;----------------------------] 30.40% | train_error: 0.866
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===========&gt;----------------------------] 30.50% | train_error: 0.862
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===========&gt;----------------------------] 30.60% | train_error: 0.858
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===========&gt;----------------------------] 30.70% | train_error: 0.854
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===========&gt;----------------------------] 30.80% | train_error: 0.850
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===========&gt;----------------------------] 30.90% | train_error: 0.846
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===========&gt;----------------------------] 31.00% | train_error: 0.842
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===========&gt;----------------------------] 31.10% | train_error: 0.838
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===========&gt;----------------------------] 31.20% | train_error: 0.834
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===========&gt;----------------------------] 31.30% | train_error: 0.830
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===========&gt;----------------------------] 31.40% | train_error: 0.826
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===========&gt;----------------------------] 31.50% | train_error: 0.822
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===========&gt;----------------------------] 31.60% | train_error: 0.818
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===========&gt;----------------------------] 31.70% | train_error: 0.814
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===========&gt;----------------------------] 31.80% | train_error: 0.811
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===========&gt;----------------------------] 31.90% | train_error: 0.807
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===========&gt;----------------------------] 32.00% | train_error: 0.803
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===========&gt;----------------------------] 32.10% | train_error: 0.799
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===========&gt;----------------------------] 32.20% | train_error: 0.795
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===========&gt;----------------------------] 32.30% | train_error: 0.792
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===========&gt;----------------------------] 32.40% | train_error: 0.788
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [============&gt;---------------------------] 32.50% | train_error: 0.784
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [============&gt;---------------------------] 32.60% | train_error: 0.780
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [============&gt;---------------------------] 32.70% | train_error: 0.777
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [============&gt;---------------------------] 32.80% | train_error: 0.773
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [============&gt;---------------------------] 32.90% | train_error: 0.769
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [============&gt;---------------------------] 33.00% | train_error: 0.766
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [============&gt;---------------------------] 33.10% | train_error: 0.762
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [============&gt;---------------------------] 33.20% | train_error: 0.759
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [============&gt;---------------------------] 33.30% | train_error: 0.755
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [============&gt;---------------------------] 33.40% | train_error: 0.751
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [============&gt;---------------------------] 33.50% | train_error: 0.748
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [============&gt;---------------------------] 33.60% | train_error: 0.744
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [============&gt;---------------------------] 33.70% | train_error: 0.741
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [============&gt;---------------------------] 33.80% | train_error: 0.737
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [============&gt;---------------------------] 33.90% | train_error: 0.734
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [============&gt;---------------------------] 34.00% | train_error: 0.730
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [============&gt;---------------------------] 34.10% | train_error: 0.727
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [============&gt;---------------------------] 34.20% | train_error: 0.723
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [============&gt;---------------------------] 34.30% | train_error: 0.720
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [============&gt;---------------------------] 34.40% | train_error: 0.717
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [============&gt;---------------------------] 34.50% | train_error: 0.713
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [============&gt;---------------------------] 34.60% | train_error: 0.710
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [============&gt;---------------------------] 34.70% | train_error: 0.706
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [============&gt;---------------------------] 34.80% | train_error: 0.703
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [============&gt;---------------------------] 34.90% | train_error: 0.700
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=============&gt;--------------------------] 35.00% | train_error: 0.696
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=============&gt;--------------------------] 35.10% | train_error: 0.693
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=============&gt;--------------------------] 35.20% | train_error: 0.690
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=============&gt;--------------------------] 35.30% | train_error: 0.687
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=============&gt;--------------------------] 35.40% | train_error: 0.683
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=============&gt;--------------------------] 35.50% | train_error: 0.680
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=============&gt;--------------------------] 35.60% | train_error: 0.677
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=============&gt;--------------------------] 35.70% | train_error: 0.674
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=============&gt;--------------------------] 35.80% | train_error: 0.670
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=============&gt;--------------------------] 35.90% | train_error: 0.667
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=============&gt;--------------------------] 36.00% | train_error: 0.664
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=============&gt;--------------------------] 36.10% | train_error: 0.661
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=============&gt;--------------------------] 36.20% | train_error: 0.658
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=============&gt;--------------------------] 36.30% | train_error: 0.655
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=============&gt;--------------------------] 36.40% | train_error: 0.652
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=============&gt;--------------------------] 36.50% | train_error: 0.649
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=============&gt;--------------------------] 36.60% | train_error: 0.646
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=============&gt;--------------------------] 36.70% | train_error: 0.642
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=============&gt;--------------------------] 36.80% | train_error: 0.639
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=============&gt;--------------------------] 36.90% | train_error: 0.636
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=============&gt;--------------------------] 37.00% | train_error: 0.633
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=============&gt;--------------------------] 37.10% | train_error: 0.630
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=============&gt;--------------------------] 37.20% | train_error: 0.627
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=============&gt;--------------------------] 37.30% | train_error: 0.624
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=============&gt;--------------------------] 37.40% | train_error: 0.622
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==============&gt;-------------------------] 37.50% | train_error: 0.619
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==============&gt;-------------------------] 37.60% | train_error: 0.616
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==============&gt;-------------------------] 37.70% | train_error: 0.613
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==============&gt;-------------------------] 37.80% | train_error: 0.610
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==============&gt;-------------------------] 37.90% | train_error: 0.607
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==============&gt;-------------------------] 38.00% | train_error: 0.604
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==============&gt;-------------------------] 38.10% | train_error: 0.601
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==============&gt;-------------------------] 38.20% | train_error: 0.598
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==============&gt;-------------------------] 38.30% | train_error: 0.596
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==============&gt;-------------------------] 38.40% | train_error: 0.593
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==============&gt;-------------------------] 38.50% | train_error: 0.590
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==============&gt;-------------------------] 38.60% | train_error: 0.587
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==============&gt;-------------------------] 38.70% | train_error: 0.584
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==============&gt;-------------------------] 38.80% | train_error: 0.582
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==============&gt;-------------------------] 38.90% | train_error: 0.579
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==============&gt;-------------------------] 39.00% | train_error: 0.576
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==============&gt;-------------------------] 39.10% | train_error: 0.573
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==============&gt;-------------------------] 39.20% | train_error: 0.571
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==============&gt;-------------------------] 39.30% | train_error: 0.568
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==============&gt;-------------------------] 39.40% | train_error: 0.565
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==============&gt;-------------------------] 39.50% | train_error: 0.563
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==============&gt;-------------------------] 39.60% | train_error: 0.560
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==============&gt;-------------------------] 39.70% | train_error: 0.557
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==============&gt;-------------------------] 39.80% | train_error: 0.555
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==============&gt;-------------------------] 39.90% | train_error: 0.552
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===============&gt;------------------------] 40.00% | train_error: 0.549
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===============&gt;------------------------] 40.10% | train_error: 0.547
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===============&gt;------------------------] 40.20% | train_error: 0.544
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===============&gt;------------------------] 40.30% | train_error: 0.542
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===============&gt;------------------------] 40.40% | train_error: 0.539
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===============&gt;------------------------] 40.50% | train_error: 0.537
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===============&gt;------------------------] 40.60% | train_error: 0.534
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===============&gt;------------------------] 40.70% | train_error: 0.532
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===============&gt;------------------------] 40.80% | train_error: 0.529
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===============&gt;------------------------] 40.90% | train_error: 0.527
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===============&gt;------------------------] 41.00% | train_error: 0.524
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===============&gt;------------------------] 41.10% | train_error: 0.522
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===============&gt;------------------------] 41.20% | train_error: 0.519
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===============&gt;------------------------] 41.30% | train_error: 0.517
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===============&gt;------------------------] 41.40% | train_error: 0.514
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===============&gt;------------------------] 41.50% | train_error: 0.512
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===============&gt;------------------------] 41.60% | train_error: 0.509
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===============&gt;------------------------] 41.70% | train_error: 0.507
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===============&gt;------------------------] 41.80% | train_error: 0.505
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===============&gt;------------------------] 41.90% | train_error: 0.502
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===============&gt;------------------------] 42.00% | train_error: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===============&gt;------------------------] 42.10% | train_error: 0.498
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===============&gt;------------------------] 42.20% | train_error: 0.495
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===============&gt;------------------------] 42.30% | train_error: 0.493
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===============&gt;------------------------] 42.40% | train_error: 0.491
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [================&gt;-----------------------] 42.50% | train_error: 0.488
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [================&gt;-----------------------] 42.60% | train_error: 0.486
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [================&gt;-----------------------] 42.70% | train_error: 0.484
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [================&gt;-----------------------] 42.80% | train_error: 0.481
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [================&gt;-----------------------] 42.90% | train_error: 0.479
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [================&gt;-----------------------] 43.00% | train_error: 0.477
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [================&gt;-----------------------] 43.10% | train_error: 0.475
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [================&gt;-----------------------] 43.20% | train_error: 0.472
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [================&gt;-----------------------] 43.30% | train_error: 0.470
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [================&gt;-----------------------] 43.40% | train_error: 0.468
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [================&gt;-----------------------] 43.50% | train_error: 0.466
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [================&gt;-----------------------] 43.60% | train_error: 0.463
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [================&gt;-----------------------] 43.70% | train_error: 0.461
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [================&gt;-----------------------] 43.80% | train_error: 0.459
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [================&gt;-----------------------] 43.90% | train_error: 0.457
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [================&gt;-----------------------] 44.00% | train_error: 0.455
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [================&gt;-----------------------] 44.10% | train_error: 0.453
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [================&gt;-----------------------] 44.20% | train_error: 0.451
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [================&gt;-----------------------] 44.30% | train_error: 0.448
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [================&gt;-----------------------] 44.40% | train_error: 0.446
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [================&gt;-----------------------] 44.50% | train_error: 0.444
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [================&gt;-----------------------] 44.60% | train_error: 0.442
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [================&gt;-----------------------] 44.70% | train_error: 0.440
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [================&gt;-----------------------] 44.80% | train_error: 0.438
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [================&gt;-----------------------] 44.90% | train_error: 0.436
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=================&gt;----------------------] 45.00% | train_error: 0.434
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=================&gt;----------------------] 45.10% | train_error: 0.432
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=================&gt;----------------------] 45.20% | train_error: 0.430
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=================&gt;----------------------] 45.30% | train_error: 0.428
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=================&gt;----------------------] 45.40% | train_error: 0.426
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=================&gt;----------------------] 45.50% | train_error: 0.424
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=================&gt;----------------------] 45.60% | train_error: 0.422
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=================&gt;----------------------] 45.70% | train_error: 0.420
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=================&gt;----------------------] 45.80% | train_error: 0.418
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=================&gt;----------------------] 45.90% | train_error: 0.416
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=================&gt;----------------------] 46.00% | train_error: 0.414
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=================&gt;----------------------] 46.10% | train_error: 0.412
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=================&gt;----------------------] 46.20% | train_error: 0.410
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=================&gt;----------------------] 46.30% | train_error: 0.408
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=================&gt;----------------------] 46.40% | train_error: 0.406
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=================&gt;----------------------] 46.50% | train_error: 0.404
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=================&gt;----------------------] 46.60% | train_error: 0.402
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=================&gt;----------------------] 46.70% | train_error: 0.400
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=================&gt;----------------------] 46.80% | train_error: 0.398
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=================&gt;----------------------] 46.90% | train_error: 0.397
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=================&gt;----------------------] 47.00% | train_error: 0.395
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=================&gt;----------------------] 47.10% | train_error: 0.393
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=================&gt;----------------------] 47.20% | train_error: 0.391
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=================&gt;----------------------] 47.30% | train_error: 0.389
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=================&gt;----------------------] 47.40% | train_error: 0.387
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==================&gt;---------------------] 47.50% | train_error: 0.386
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==================&gt;---------------------] 47.60% | train_error: 0.384
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==================&gt;---------------------] 47.70% | train_error: 0.382
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==================&gt;---------------------] 47.80% | train_error: 0.380
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==================&gt;---------------------] 47.90% | train_error: 0.378
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==================&gt;---------------------] 48.00% | train_error: 0.377
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==================&gt;---------------------] 48.10% | train_error: 0.375
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==================&gt;---------------------] 48.20% | train_error: 0.373
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==================&gt;---------------------] 48.30% | train_error: 0.371
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==================&gt;---------------------] 48.40% | train_error: 0.370
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==================&gt;---------------------] 48.50% | train_error: 0.368
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==================&gt;---------------------] 48.60% | train_error: 0.366
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==================&gt;---------------------] 48.70% | train_error: 0.364
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==================&gt;---------------------] 48.80% | train_error: 0.363
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==================&gt;---------------------] 48.90% | train_error: 0.361
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==================&gt;---------------------] 49.00% | train_error: 0.359
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==================&gt;---------------------] 49.10% | train_error: 0.358
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==================&gt;---------------------] 49.20% | train_error: 0.356
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==================&gt;---------------------] 49.30% | train_error: 0.354
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==================&gt;---------------------] 49.40% | train_error: 0.353
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==================&gt;---------------------] 49.50% | train_error: 0.351
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==================&gt;---------------------] 49.60% | train_error: 0.349
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==================&gt;---------------------] 49.70% | train_error: 0.348
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==================&gt;---------------------] 49.80% | train_error: 0.346
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==================&gt;---------------------] 49.90% | train_error: 0.344
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===================&gt;--------------------] 50.00% | train_error: 0.343
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===================&gt;--------------------] 50.10% | train_error: 0.341
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===================&gt;--------------------] 50.20% | train_error: 0.339
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===================&gt;--------------------] 50.30% | train_error: 0.338
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===================&gt;--------------------] 50.40% | train_error: 0.336
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===================&gt;--------------------] 50.50% | train_error: 0.335
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===================&gt;--------------------] 50.60% | train_error: 0.333
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===================&gt;--------------------] 50.70% | train_error: 0.332
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===================&gt;--------------------] 50.80% | train_error: 0.330
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===================&gt;--------------------] 50.90% | train_error: 0.328
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===================&gt;--------------------] 51.00% | train_error: 0.327
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===================&gt;--------------------] 51.10% | train_error: 0.325
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===================&gt;--------------------] 51.20% | train_error: 0.324
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===================&gt;--------------------] 51.30% | train_error: 0.322
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===================&gt;--------------------] 51.40% | train_error: 0.321
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===================&gt;--------------------] 51.50% | train_error: 0.319
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===================&gt;--------------------] 51.60% | train_error: 0.318
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===================&gt;--------------------] 51.70% | train_error: 0.316
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===================&gt;--------------------] 51.80% | train_error: 0.315
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===================&gt;--------------------] 51.90% | train_error: 0.313
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===================&gt;--------------------] 52.00% | train_error: 0.312
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===================&gt;--------------------] 52.10% | train_error: 0.310
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===================&gt;--------------------] 52.20% | train_error: 0.309
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===================&gt;--------------------] 52.30% | train_error: 0.308
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===================&gt;--------------------] 52.40% | train_error: 0.306
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [====================&gt;-------------------] 52.50% | train_error: 0.305
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [====================&gt;-------------------] 52.60% | train_error: 0.303
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [====================&gt;-------------------] 52.70% | train_error: 0.302
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [====================&gt;-------------------] 52.80% | train_error: 0.300
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [====================&gt;-------------------] 52.90% | train_error: 0.299
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [====================&gt;-------------------] 53.00% | train_error: 0.298
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [====================&gt;-------------------] 53.10% | train_error: 0.296
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [====================&gt;-------------------] 53.20% | train_error: 0.295
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [====================&gt;-------------------] 53.30% | train_error: 0.293
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [====================&gt;-------------------] 53.40% | train_error: 0.292
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [====================&gt;-------------------] 53.50% | train_error: 0.291
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [====================&gt;-------------------] 53.60% | train_error: 0.289
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [====================&gt;-------------------] 53.70% | train_error: 0.288
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [====================&gt;-------------------] 53.80% | train_error: 0.287
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [====================&gt;-------------------] 53.90% | train_error: 0.285
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [====================&gt;-------------------] 54.00% | train_error: 0.284
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [====================&gt;-------------------] 54.10% | train_error: 0.283
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [====================&gt;-------------------] 54.20% | train_error: 0.281
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [====================&gt;-------------------] 54.30% | train_error: 0.280
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [====================&gt;-------------------] 54.40% | train_error: 0.279
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [====================&gt;-------------------] 54.50% | train_error: 0.277
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [====================&gt;-------------------] 54.60% | train_error: 0.276
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [====================&gt;-------------------] 54.70% | train_error: 0.275
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [====================&gt;-------------------] 54.80% | train_error: 0.273
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [====================&gt;-------------------] 54.90% | train_error: 0.272
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=====================&gt;------------------] 55.00% | train_error: 0.271
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=====================&gt;------------------] 55.10% | train_error: 0.270
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=====================&gt;------------------] 55.20% | train_error: 0.268
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=====================&gt;------------------] 55.30% | train_error: 0.267
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=====================&gt;------------------] 55.40% | train_error: 0.266
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=====================&gt;------------------] 55.50% | train_error: 0.265
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=====================&gt;------------------] 55.60% | train_error: 0.263
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=====================&gt;------------------] 55.70% | train_error: 0.262
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=====================&gt;------------------] 55.80% | train_error: 0.261
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=====================&gt;------------------] 55.90% | train_error: 0.260
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=====================&gt;------------------] 56.00% | train_error: 0.259
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=====================&gt;------------------] 56.10% | train_error: 0.257
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=====================&gt;------------------] 56.20% | train_error: 0.256
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=====================&gt;------------------] 56.30% | train_error: 0.255
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=====================&gt;------------------] 56.40% | train_error: 0.254
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=====================&gt;------------------] 56.50% | train_error: 0.253
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=====================&gt;------------------] 56.60% | train_error: 0.251
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=====================&gt;------------------] 56.70% | train_error: 0.250
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=====================&gt;------------------] 56.80% | train_error: 0.249
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=====================&gt;------------------] 56.90% | train_error: 0.248
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=====================&gt;------------------] 57.00% | train_error: 0.247
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=====================&gt;------------------] 57.10% | train_error: 0.246
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=====================&gt;------------------] 57.20% | train_error: 0.244
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=====================&gt;------------------] 57.30% | train_error: 0.243
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=====================&gt;------------------] 57.40% | train_error: 0.242
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [======================&gt;-----------------] 57.50% | train_error: 0.241
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [======================&gt;-----------------] 57.60% | train_error: 0.240
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [======================&gt;-----------------] 57.70% | train_error: 0.239
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [======================&gt;-----------------] 57.80% | train_error: 0.238
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [======================&gt;-----------------] 57.90% | train_error: 0.237
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [======================&gt;-----------------] 58.00% | train_error: 0.235
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [======================&gt;-----------------] 58.10% | train_error: 0.234
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [======================&gt;-----------------] 58.20% | train_error: 0.233
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [======================&gt;-----------------] 58.30% | train_error: 0.232
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [======================&gt;-----------------] 58.40% | train_error: 0.231
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [======================&gt;-----------------] 58.50% | train_error: 0.230
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [======================&gt;-----------------] 58.60% | train_error: 0.229
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [======================&gt;-----------------] 58.70% | train_error: 0.228
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [======================&gt;-----------------] 58.80% | train_error: 0.227
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [======================&gt;-----------------] 58.90% | train_error: 0.226
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [======================&gt;-----------------] 59.00% | train_error: 0.225
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [======================&gt;-----------------] 59.10% | train_error: 0.224
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [======================&gt;-----------------] 59.20% | train_error: 0.223
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [======================&gt;-----------------] 59.30% | train_error: 0.222
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [======================&gt;-----------------] 59.40% | train_error: 0.221
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [======================&gt;-----------------] 59.50% | train_error: 0.219
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [======================&gt;-----------------] 59.60% | train_error: 0.218
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [======================&gt;-----------------] 59.70% | train_error: 0.217
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [======================&gt;-----------------] 59.80% | train_error: 0.216
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [======================&gt;-----------------] 59.90% | train_error: 0.215
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=======================&gt;----------------] 60.00% | train_error: 0.214
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=======================&gt;----------------] 60.10% | train_error: 0.213
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=======================&gt;----------------] 60.20% | train_error: 0.212
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=======================&gt;----------------] 60.30% | train_error: 0.211
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=======================&gt;----------------] 60.40% | train_error: 0.210
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=======================&gt;----------------] 60.50% | train_error: 0.209
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=======================&gt;----------------] 60.60% | train_error: 0.209
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=======================&gt;----------------] 60.70% | train_error: 0.208
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=======================&gt;----------------] 60.80% | train_error: 0.207
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=======================&gt;----------------] 60.90% | train_error: 0.206
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=======================&gt;----------------] 61.00% | train_error: 0.205
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=======================&gt;----------------] 61.10% | train_error: 0.204
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=======================&gt;----------------] 61.20% | train_error: 0.203
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=======================&gt;----------------] 61.30% | train_error: 0.202
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=======================&gt;----------------] 61.40% | train_error: 0.201
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=======================&gt;----------------] 61.50% | train_error: 0.200
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=======================&gt;----------------] 61.60% | train_error: 0.199
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=======================&gt;----------------] 61.70% | train_error: 0.198
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=======================&gt;----------------] 61.80% | train_error: 0.197
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=======================&gt;----------------] 61.90% | train_error: 0.196
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=======================&gt;----------------] 62.00% | train_error: 0.195
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=======================&gt;----------------] 62.10% | train_error: 0.194
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=======================&gt;----------------] 62.20% | train_error: 0.194
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=======================&gt;----------------] 62.30% | train_error: 0.193
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=======================&gt;----------------] 62.40% | train_error: 0.192
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [========================&gt;---------------] 62.50% | train_error: 0.191
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [========================&gt;---------------] 62.60% | train_error: 0.190
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [========================&gt;---------------] 62.70% | train_error: 0.189
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [========================&gt;---------------] 62.80% | train_error: 0.188
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [========================&gt;---------------] 62.90% | train_error: 0.187
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [========================&gt;---------------] 63.00% | train_error: 0.186
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [========================&gt;---------------] 63.10% | train_error: 0.186
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [========================&gt;---------------] 63.20% | train_error: 0.185
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [========================&gt;---------------] 63.30% | train_error: 0.184
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [========================&gt;---------------] 63.40% | train_error: 0.183
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [========================&gt;---------------] 63.50% | train_error: 0.182
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [========================&gt;---------------] 63.60% | train_error: 0.181
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [========================&gt;---------------] 63.70% | train_error: 0.180
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [========================&gt;---------------] 63.80% | train_error: 0.180
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [========================&gt;---------------] 63.90% | train_error: 0.179
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [========================&gt;---------------] 64.00% | train_error: 0.178
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [========================&gt;---------------] 64.10% | train_error: 0.177
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [========================&gt;---------------] 64.20% | train_error: 0.176
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [========================&gt;---------------] 64.30% | train_error: 0.176
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [========================&gt;---------------] 64.40% | train_error: 0.175
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [========================&gt;---------------] 64.50% | train_error: 0.174
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [========================&gt;---------------] 64.60% | train_error: 0.173
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [========================&gt;---------------] 64.70% | train_error: 0.172
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [========================&gt;---------------] 64.80% | train_error: 0.171
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [========================&gt;---------------] 64.90% | train_error: 0.171
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=========================&gt;--------------] 65.00% | train_error: 0.170
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=========================&gt;--------------] 65.10% | train_error: 0.169
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=========================&gt;--------------] 65.20% | train_error: 0.168
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=========================&gt;--------------] 65.30% | train_error: 0.168
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=========================&gt;--------------] 65.40% | train_error: 0.167
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=========================&gt;--------------] 65.50% | train_error: 0.166
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=========================&gt;--------------] 65.60% | train_error: 0.165
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=========================&gt;--------------] 65.70% | train_error: 0.164
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=========================&gt;--------------] 65.80% | train_error: 0.164
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=========================&gt;--------------] 65.90% | train_error: 0.163
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=========================&gt;--------------] 66.00% | train_error: 0.162
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=========================&gt;--------------] 66.10% | train_error: 0.161
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=========================&gt;--------------] 66.20% | train_error: 0.161
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=========================&gt;--------------] 66.30% | train_error: 0.160
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=========================&gt;--------------] 66.40% | train_error: 0.159
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=========================&gt;--------------] 66.50% | train_error: 0.158
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=========================&gt;--------------] 66.60% | train_error: 0.158
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=========================&gt;--------------] 66.70% | train_error: 0.157
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=========================&gt;--------------] 66.80% | train_error: 0.156
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=========================&gt;--------------] 66.90% | train_error: 0.156
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=========================&gt;--------------] 67.00% | train_error: 0.155
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=========================&gt;--------------] 67.10% | train_error: 0.154
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=========================&gt;--------------] 67.20% | train_error: 0.153
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=========================&gt;--------------] 67.30% | train_error: 0.153
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=========================&gt;--------------] 67.40% | train_error: 0.152
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==========================&gt;-------------] 67.50% | train_error: 0.151
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==========================&gt;-------------] 67.60% | train_error: 0.151
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==========================&gt;-------------] 67.70% | train_error: 0.150
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==========================&gt;-------------] 67.80% | train_error: 0.149
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==========================&gt;-------------] 67.90% | train_error: 0.149
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==========================&gt;-------------] 68.00% | train_error: 0.148
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==========================&gt;-------------] 68.10% | train_error: 0.147
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==========================&gt;-------------] 68.20% | train_error: 0.146
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==========================&gt;-------------] 68.30% | train_error: 0.146
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==========================&gt;-------------] 68.40% | train_error: 0.145
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==========================&gt;-------------] 68.50% | train_error: 0.144
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==========================&gt;-------------] 68.60% | train_error: 0.144
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==========================&gt;-------------] 68.70% | train_error: 0.143
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==========================&gt;-------------] 68.80% | train_error: 0.142
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==========================&gt;-------------] 68.90% | train_error: 0.142
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==========================&gt;-------------] 69.00% | train_error: 0.141
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==========================&gt;-------------] 69.10% | train_error: 0.141
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==========================&gt;-------------] 69.20% | train_error: 0.140
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==========================&gt;-------------] 69.30% | train_error: 0.139
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==========================&gt;-------------] 69.40% | train_error: 0.139
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==========================&gt;-------------] 69.50% | train_error: 0.138
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==========================&gt;-------------] 69.60% | train_error: 0.137
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==========================&gt;-------------] 69.70% | train_error: 0.137
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==========================&gt;-------------] 69.80% | train_error: 0.136
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==========================&gt;-------------] 69.90% | train_error: 0.135
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===========================&gt;------------] 70.00% | train_error: 0.135
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===========================&gt;------------] 70.10% | train_error: 0.134
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===========================&gt;------------] 70.20% | train_error: 0.134
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===========================&gt;------------] 70.30% | train_error: 0.133
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===========================&gt;------------] 70.40% | train_error: 0.132
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===========================&gt;------------] 70.50% | train_error: 0.132
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===========================&gt;------------] 70.60% | train_error: 0.131
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===========================&gt;------------] 70.70% | train_error: 0.131
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===========================&gt;------------] 70.80% | train_error: 0.130
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===========================&gt;------------] 70.90% | train_error: 0.129
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===========================&gt;------------] 71.00% | train_error: 0.129
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===========================&gt;------------] 71.10% | train_error: 0.128
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===========================&gt;------------] 71.20% | train_error: 0.128
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===========================&gt;------------] 71.30% | train_error: 0.127
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===========================&gt;------------] 71.40% | train_error: 0.126
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===========================&gt;------------] 71.50% | train_error: 0.126
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===========================&gt;------------] 71.60% | train_error: 0.125
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===========================&gt;------------] 71.70% | train_error: 0.125
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===========================&gt;------------] 71.80% | train_error: 0.124
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===========================&gt;------------] 71.90% | train_error: 0.124
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===========================&gt;------------] 72.00% | train_error: 0.123
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===========================&gt;------------] 72.10% | train_error: 0.122
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===========================&gt;------------] 72.20% | train_error: 0.122
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===========================&gt;------------] 72.30% | train_error: 0.121
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===========================&gt;------------] 72.40% | train_error: 0.121
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [============================&gt;-----------] 72.50% | train_error: 0.120
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [============================&gt;-----------] 72.60% | train_error: 0.120
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [============================&gt;-----------] 72.70% | train_error: 0.119
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [============================&gt;-----------] 72.80% | train_error: 0.119
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [============================&gt;-----------] 72.90% | train_error: 0.118
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [============================&gt;-----------] 73.00% | train_error: 0.117
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [============================&gt;-----------] 73.10% | train_error: 0.117
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [============================&gt;-----------] 73.20% | train_error: 0.116
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [============================&gt;-----------] 73.30% | train_error: 0.116
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [============================&gt;-----------] 73.40% | train_error: 0.115
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [============================&gt;-----------] 73.50% | train_error: 0.115
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [============================&gt;-----------] 73.60% | train_error: 0.114
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [============================&gt;-----------] 73.70% | train_error: 0.114
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [============================&gt;-----------] 73.80% | train_error: 0.113
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [============================&gt;-----------] 73.90% | train_error: 0.113
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [============================&gt;-----------] 74.00% | train_error: 0.112
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [============================&gt;-----------] 74.10% | train_error: 0.112
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [============================&gt;-----------] 74.20% | train_error: 0.111
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [============================&gt;-----------] 74.30% | train_error: 0.111
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [============================&gt;-----------] 74.40% | train_error: 0.110
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [============================&gt;-----------] 74.50% | train_error: 0.110
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [============================&gt;-----------] 74.60% | train_error: 0.109
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [============================&gt;-----------] 74.70% | train_error: 0.109
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [============================&gt;-----------] 74.80% | train_error: 0.108
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [============================&gt;-----------] 74.90% | train_error: 0.108
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=============================&gt;----------] 75.00% | train_error: 0.107
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=============================&gt;----------] 75.10% | train_error: 0.107
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=============================&gt;----------] 75.20% | train_error: 0.106
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=============================&gt;----------] 75.30% | train_error: 0.106
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=============================&gt;----------] 75.40% | train_error: 0.105
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=============================&gt;----------] 75.50% | train_error: 0.105
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=============================&gt;----------] 75.60% | train_error: 0.104
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=============================&gt;----------] 75.70% | train_error: 0.104
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=============================&gt;----------] 75.80% | train_error: 0.103
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=============================&gt;----------] 75.90% | train_error: 0.103
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=============================&gt;----------] 76.00% | train_error: 0.102
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=============================&gt;----------] 76.10% | train_error: 0.102
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=============================&gt;----------] 76.20% | train_error: 0.101
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=============================&gt;----------] 76.30% | train_error: 0.101
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=============================&gt;----------] 76.40% | train_error: 0.101
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=============================&gt;----------] 76.50% | train_error: 0.100
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=============================&gt;----------] 76.60% | train_error: 0.0996
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=============================&gt;----------] 76.70% | train_error: 0.0992
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=============================&gt;----------] 76.80% | train_error: 0.0987
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=============================&gt;----------] 76.90% | train_error: 0.0983
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=============================&gt;----------] 77.00% | train_error: 0.0978
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=============================&gt;----------] 77.10% | train_error: 0.0974
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=============================&gt;----------] 77.20% | train_error: 0.0969
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=============================&gt;----------] 77.30% | train_error: 0.0965
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=============================&gt;----------] 77.40% | train_error: 0.0961
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==============================&gt;---------] 77.50% | train_error: 0.0956
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==============================&gt;---------] 77.60% | train_error: 0.0952
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==============================&gt;---------] 77.70% | train_error: 0.0948
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==============================&gt;---------] 77.80% | train_error: 0.0943
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==============================&gt;---------] 77.90% | train_error: 0.0939
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==============================&gt;---------] 78.00% | train_error: 0.0935
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==============================&gt;---------] 78.10% | train_error: 0.0930
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==============================&gt;---------] 78.20% | train_error: 0.0926
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==============================&gt;---------] 78.30% | train_error: 0.0922
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==============================&gt;---------] 78.40% | train_error: 0.0918
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==============================&gt;---------] 78.50% | train_error: 0.0914
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==============================&gt;---------] 78.60% | train_error: 0.0910
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==============================&gt;---------] 78.70% | train_error: 0.0905
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==============================&gt;---------] 78.80% | train_error: 0.0901
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==============================&gt;---------] 78.90% | train_error: 0.0897
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==============================&gt;---------] 79.00% | train_error: 0.0893
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==============================&gt;---------] 79.10% | train_error: 0.0889
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==============================&gt;---------] 79.20% | train_error: 0.0885
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==============================&gt;---------] 79.30% | train_error: 0.0881
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==============================&gt;---------] 79.40% | train_error: 0.0877
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==============================&gt;---------] 79.50% | train_error: 0.0873
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==============================&gt;---------] 79.60% | train_error: 0.0869
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==============================&gt;---------] 79.70% | train_error: 0.0865
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==============================&gt;---------] 79.80% | train_error: 0.0861
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==============================&gt;---------] 79.90% | train_error: 0.0858
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===============================&gt;--------] 80.00% | train_error: 0.0854
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===============================&gt;--------] 80.10% | train_error: 0.0850
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===============================&gt;--------] 80.20% | train_error: 0.0846
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===============================&gt;--------] 80.30% | train_error: 0.0842
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===============================&gt;--------] 80.40% | train_error: 0.0838
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===============================&gt;--------] 80.50% | train_error: 0.0835
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===============================&gt;--------] 80.60% | train_error: 0.0831
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===============================&gt;--------] 80.70% | train_error: 0.0827
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===============================&gt;--------] 80.80% | train_error: 0.0823
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===============================&gt;--------] 80.90% | train_error: 0.0820
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===============================&gt;--------] 81.00% | train_error: 0.0816
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===============================&gt;--------] 81.10% | train_error: 0.0812
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===============================&gt;--------] 81.20% | train_error: 0.0809
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===============================&gt;--------] 81.30% | train_error: 0.0805
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===============================&gt;--------] 81.40% | train_error: 0.0801
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===============================&gt;--------] 81.50% | train_error: 0.0798
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===============================&gt;--------] 81.60% | train_error: 0.0794
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===============================&gt;--------] 81.70% | train_error: 0.0791
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===============================&gt;--------] 81.80% | train_error: 0.0787
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===============================&gt;--------] 81.90% | train_error: 0.0784
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===============================&gt;--------] 82.00% | train_error: 0.0780
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===============================&gt;--------] 82.10% | train_error: 0.0776
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===============================&gt;--------] 82.20% | train_error: 0.0773
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===============================&gt;--------] 82.30% | train_error: 0.0770
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===============================&gt;--------] 82.40% | train_error: 0.0766
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [================================&gt;-------] 82.50% | train_error: 0.0763
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [================================&gt;-------] 82.60% | train_error: 0.0759
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [================================&gt;-------] 82.70% | train_error: 0.0756
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [================================&gt;-------] 82.80% | train_error: 0.0752
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [================================&gt;-------] 82.90% | train_error: 0.0749
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [================================&gt;-------] 83.00% | train_error: 0.0746
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [================================&gt;-------] 83.10% | train_error: 0.0742
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [================================&gt;-------] 83.20% | train_error: 0.0739
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [================================&gt;-------] 83.30% | train_error: 0.0736
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [================================&gt;-------] 83.40% | train_error: 0.0732
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [================================&gt;-------] 83.50% | train_error: 0.0729
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [================================&gt;-------] 83.60% | train_error: 0.0726
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [================================&gt;-------] 83.70% | train_error: 0.0723
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [================================&gt;-------] 83.80% | train_error: 0.0719
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [================================&gt;-------] 83.90% | train_error: 0.0716
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [================================&gt;-------] 84.00% | train_error: 0.0713
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [================================&gt;-------] 84.10% | train_error: 0.0710
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [================================&gt;-------] 84.20% | train_error: 0.0707
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [================================&gt;-------] 84.30% | train_error: 0.0703
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [================================&gt;-------] 84.40% | train_error: 0.0700
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [================================&gt;-------] 84.50% | train_error: 0.0697
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [================================&gt;-------] 84.60% | train_error: 0.0694
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [================================&gt;-------] 84.70% | train_error: 0.0691
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [================================&gt;-------] 84.80% | train_error: 0.0688
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [================================&gt;-------] 84.90% | train_error: 0.0685
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=================================&gt;------] 85.00% | train_error: 0.0682
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=================================&gt;------] 85.10% | train_error: 0.0679
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=================================&gt;------] 85.20% | train_error: 0.0676
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=================================&gt;------] 85.30% | train_error: 0.0673
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=================================&gt;------] 85.40% | train_error: 0.0670
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=================================&gt;------] 85.50% | train_error: 0.0667
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=================================&gt;------] 85.60% | train_error: 0.0664
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=================================&gt;------] 85.70% | train_error: 0.0661
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=================================&gt;------] 85.80% | train_error: 0.0658
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=================================&gt;------] 85.90% | train_error: 0.0655
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=================================&gt;------] 86.00% | train_error: 0.0652
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=================================&gt;------] 86.10% | train_error: 0.0649
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=================================&gt;------] 86.20% | train_error: 0.0646
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=================================&gt;------] 86.30% | train_error: 0.0643
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=================================&gt;------] 86.40% | train_error: 0.0641
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=================================&gt;------] 86.50% | train_error: 0.0638
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=================================&gt;------] 86.60% | train_error: 0.0635
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=================================&gt;------] 86.70% | train_error: 0.0632
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=================================&gt;------] 86.80% | train_error: 0.0629
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=================================&gt;------] 86.90% | train_error: 0.0626
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=================================&gt;------] 87.00% | train_error: 0.0624
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=================================&gt;------] 87.10% | train_error: 0.0621
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=================================&gt;------] 87.20% | train_error: 0.0618
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=================================&gt;------] 87.30% | train_error: 0.0615
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=================================&gt;------] 87.40% | train_error: 0.0613
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==================================&gt;-----] 87.50% | train_error: 0.0610
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==================================&gt;-----] 87.60% | train_error: 0.0607
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==================================&gt;-----] 87.70% | train_error: 0.0605
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==================================&gt;-----] 87.80% | train_error: 0.0602
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==================================&gt;-----] 87.90% | train_error: 0.0599
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==================================&gt;-----] 88.00% | train_error: 0.0597
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==================================&gt;-----] 88.10% | train_error: 0.0594
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==================================&gt;-----] 88.20% | train_error: 0.0591
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==================================&gt;-----] 88.30% | train_error: 0.0589
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==================================&gt;-----] 88.40% | train_error: 0.0586
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==================================&gt;-----] 88.50% | train_error: 0.0584
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==================================&gt;-----] 88.60% | train_error: 0.0581
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==================================&gt;-----] 88.70% | train_error: 0.0578
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==================================&gt;-----] 88.80% | train_error: 0.0576
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==================================&gt;-----] 88.90% | train_error: 0.0573
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==================================&gt;-----] 89.00% | train_error: 0.0571
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==================================&gt;-----] 89.10% | train_error: 0.0568
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==================================&gt;-----] 89.20% | train_error: 0.0566
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==================================&gt;-----] 89.30% | train_error: 0.0563
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==================================&gt;-----] 89.40% | train_error: 0.0561
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==================================&gt;-----] 89.50% | train_error: 0.0558
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==================================&gt;-----] 89.60% | train_error: 0.0556
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==================================&gt;-----] 89.70% | train_error: 0.0553
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==================================&gt;-----] 89.80% | train_error: 0.0551
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==================================&gt;-----] 89.90% | train_error: 0.0549
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===================================&gt;----] 90.00% | train_error: 0.0546
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===================================&gt;----] 90.10% | train_error: 0.0544
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===================================&gt;----] 90.20% | train_error: 0.0541
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===================================&gt;----] 90.30% | train_error: 0.0539
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===================================&gt;----] 90.40% | train_error: 0.0537
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===================================&gt;----] 90.50% | train_error: 0.0534
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===================================&gt;----] 90.60% | train_error: 0.0532
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===================================&gt;----] 90.70% | train_error: 0.0530
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===================================&gt;----] 90.80% | train_error: 0.0527
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===================================&gt;----] 90.90% | train_error: 0.0525
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===================================&gt;----] 91.00% | train_error: 0.0523
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===================================&gt;----] 91.10% | train_error: 0.0520
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===================================&gt;----] 91.20% | train_error: 0.0518
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===================================&gt;----] 91.30% | train_error: 0.0516
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===================================&gt;----] 91.40% | train_error: 0.0514
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===================================&gt;----] 91.50% | train_error: 0.0511
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===================================&gt;----] 91.60% | train_error: 0.0509
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===================================&gt;----] 91.70% | train_error: 0.0507
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===================================&gt;----] 91.80% | train_error: 0.0505
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===================================&gt;----] 91.90% | train_error: 0.0503
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===================================&gt;----] 92.00% | train_error: 0.0500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===================================&gt;----] 92.10% | train_error: 0.0498
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===================================&gt;----] 92.20% | train_error: 0.0496
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===================================&gt;----] 92.30% | train_error: 0.0494
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===================================&gt;----] 92.40% | train_error: 0.0492
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [====================================&gt;---] 92.50% | train_error: 0.0490
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [====================================&gt;---] 92.60% | train_error: 0.0487
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [====================================&gt;---] 92.70% | train_error: 0.0485
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [====================================&gt;---] 92.80% | train_error: 0.0483
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [====================================&gt;---] 92.90% | train_error: 0.0481
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [====================================&gt;---] 93.00% | train_error: 0.0479
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [====================================&gt;---] 93.10% | train_error: 0.0477
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [====================================&gt;---] 93.20% | train_error: 0.0475
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [====================================&gt;---] 93.30% | train_error: 0.0473
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [====================================&gt;---] 93.40% | train_error: 0.0471
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [====================================&gt;---] 93.50% | train_error: 0.0469
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [====================================&gt;---] 93.60% | train_error: 0.0467
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [====================================&gt;---] 93.70% | train_error: 0.0465
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [====================================&gt;---] 93.80% | train_error: 0.0463
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [====================================&gt;---] 93.90% | train_error: 0.0461
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [====================================&gt;---] 94.00% | train_error: 0.0459
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [====================================&gt;---] 94.10% | train_error: 0.0457
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [====================================&gt;---] 94.20% | train_error: 0.0455
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [====================================&gt;---] 94.30% | train_error: 0.0453
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [====================================&gt;---] 94.40% | train_error: 0.0451
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [====================================&gt;---] 94.50% | train_error: 0.0449
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [====================================&gt;---] 94.60% | train_error: 0.0447
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [====================================&gt;---] 94.70% | train_error: 0.0445
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [====================================&gt;---] 94.80% | train_error: 0.0443
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [====================================&gt;---] 94.90% | train_error: 0.0441
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=====================================&gt;--] 95.00% | train_error: 0.0439
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=====================================&gt;--] 95.10% | train_error: 0.0437
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=====================================&gt;--] 95.20% | train_error: 0.0435
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=====================================&gt;--] 95.30% | train_error: 0.0434
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=====================================&gt;--] 95.40% | train_error: 0.0432
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=====================================&gt;--] 95.50% | train_error: 0.0430
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=====================================&gt;--] 95.60% | train_error: 0.0428
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=====================================&gt;--] 95.70% | train_error: 0.0426
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=====================================&gt;--] 95.80% | train_error: 0.0424
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=====================================&gt;--] 95.90% | train_error: 0.0423
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=====================================&gt;--] 96.00% | train_error: 0.0421
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=====================================&gt;--] 96.10% | train_error: 0.0419
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=====================================&gt;--] 96.20% | train_error: 0.0417
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=====================================&gt;--] 96.30% | train_error: 0.0415
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=====================================&gt;--] 96.40% | train_error: 0.0414
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=====================================&gt;--] 96.50% | train_error: 0.0412
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=====================================&gt;--] 96.60% | train_error: 0.0410
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=====================================&gt;--] 96.70% | train_error: 0.0408
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=====================================&gt;--] 96.80% | train_error: 0.0407
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=====================================&gt;--] 96.90% | train_error: 0.0405
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=====================================&gt;--] 97.00% | train_error: 0.0403
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=====================================&gt;--] 97.10% | train_error: 0.0401
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=====================================&gt;--] 97.20% | train_error: 0.0400
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=====================================&gt;--] 97.30% | train_error: 0.0398
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=====================================&gt;--] 97.40% | train_error: 0.0396
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [======================================&gt;-] 97.50% | train_error: 0.0395
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [======================================&gt;-] 97.60% | train_error: 0.0393
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [======================================&gt;-] 97.70% | train_error: 0.0391
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [======================================&gt;-] 97.80% | train_error: 0.0389
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [======================================&gt;-] 97.90% | train_error: 0.0388
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [======================================&gt;-] 98.00% | train_error: 0.0386
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [======================================&gt;-] 98.10% | train_error: 0.0385
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [======================================&gt;-] 98.20% | train_error: 0.0383
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [======================================&gt;-] 98.30% | train_error: 0.0381
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [======================================&gt;-] 98.40% | train_error: 0.0380
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [======================================&gt;-] 98.50% | train_error: 0.0378
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [======================================&gt;-] 98.60% | train_error: 0.0376
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [======================================&gt;-] 98.70% | train_error: 0.0375
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [======================================&gt;-] 98.80% | train_error: 0.0373
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [======================================&gt;-] 98.90% | train_error: 0.0372
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [======================================&gt;-] 99.00% | train_error: 0.0370
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [======================================&gt;-] 99.10% | train_error: 0.0369
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [======================================&gt;-] 99.20% | train_error: 0.0367
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [======================================&gt;-] 99.30% | train_error: 0.0365
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [======================================&gt;-] 99.40% | train_error: 0.0364
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [======================================&gt;-] 99.50% | train_error: 0.0362
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [======================================&gt;-] 99.60% | train_error: 0.0361
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [======================================&gt;-] 99.70% | train_error: 0.0359
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [======================================&gt;-] 99.80% | train_error: 0.0358
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [======================================&gt;-] 99.90% | train_error: 0.0356
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=======================================&gt;] 100.0% | train_error: 0.0356
</pre></div>
</div>
</div>
</div>
<p>We see that given more epochs to train on, the regressor reaches a lower MSE.</p>
<p>Let us then switch to a binary classification. We use a binary
classification dataset, and follow a similar setup to the regression
case.</p>
<div class="cell docutils container">
<div class="cell_input docutils container">
<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">sklearn.datasets</span> <span class="kn">import</span> <span class="n">load_breast_cancer</span>
<span class="kn">from</span> <span class="nn">sklearn.preprocessing</span> <span class="kn">import</span> <span class="n">MinMaxScaler</span>
<span class="n">wisconsin</span> <span class="o">=</span> <span class="n">load_breast_cancer</span><span class="p">()</span>
<span class="n">X</span> <span class="o">=</span> <span class="n">wisconsin</span><span class="o">.</span><span class="n">data</span>
<span class="n">target</span> <span class="o">=</span> <span class="n">wisconsin</span><span class="o">.</span><span class="n">target</span>
<span class="n">target</span> <span class="o">=</span> <span class="n">target</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="n">target</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="mi">1</span><span class="p">)</span>
<span class="n">X_train</span><span class="p">,</span> <span class="n">X_val</span><span class="p">,</span> <span class="n">t_train</span><span class="p">,</span> <span class="n">t_val</span> <span class="o">=</span> <span class="n">train_test_split</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">target</span><span class="p">)</span>
<span class="n">scaler</span> <span class="o">=</span> <span class="n">MinMaxScaler</span><span class="p">()</span>
<span class="n">scaler</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X_train</span><span class="p">)</span>
<span class="n">X_train</span> <span class="o">=</span> <span class="n">scaler</span><span class="o">.</span><span class="n">transform</span><span class="p">(</span><span class="n">X_train</span><span class="p">)</span>
<span class="n">X_val</span> <span class="o">=</span> <span class="n">scaler</span><span class="o">.</span><span class="n">transform</span><span class="p">(</span><span class="n">X_val</span><span class="p">)</span>
</pre></div>
</div>
</div>
</div>
<div class="cell docutils container">
<div class="cell_input docutils container">
<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">input_nodes</span> <span class="o">=</span> <span class="n">X_train</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span>
<span class="n">output_nodes</span> <span class="o">=</span> <span class="mi">1</span>
<span class="n">logistic_regression</span> <span class="o">=</span> <span class="n">FFNN</span><span class="p">((</span><span class="n">input_nodes</span><span class="p">,</span> <span class="n">output_nodes</span><span class="p">),</span> <span class="n">output_func</span><span class="o">=</span><span class="n">sigmoid</span><span class="p">,</span> <span class="n">cost_func</span><span class="o">=</span><span class="n">CostLogReg</span><span class="p">,</span> <span class="n">seed</span><span class="o">=</span><span class="mi">2023</span><span class="p">)</span>
</pre></div>
</div>
</div>
</div>
<p>We will now make use of our validation data by passing it into our fit function as a keyword argument</p>
<div class="cell docutils container">
<div class="cell_input docutils container">
<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">logistic_regression</span><span class="o">.</span><span class="n">reset_weights</span><span class="p">()</span> <span class="c1"># reset weights such that previous runs or reruns don&#39;t affect the weights</span>
<span class="n">scheduler</span> <span class="o">=</span> <span class="n">Adam</span><span class="p">(</span><span class="n">eta</span><span class="o">=</span><span class="mf">1e-3</span><span class="p">,</span> <span class="n">rho</span><span class="o">=</span><span class="mf">0.9</span><span class="p">,</span> <span class="n">rho2</span><span class="o">=</span><span class="mf">0.999</span><span class="p">)</span>
<span class="n">scores</span> <span class="o">=</span> <span class="n">logistic_regression</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X_train</span><span class="p">,</span> <span class="n">t_train</span><span class="p">,</span> <span class="n">scheduler</span><span class="p">,</span> <span class="n">epochs</span><span class="o">=</span><span class="mi">1000</span><span class="p">,</span> <span class="n">X_val</span><span class="o">=</span><span class="n">X_val</span><span class="p">,</span> <span class="n">t_val</span><span class="o">=</span><span class="n">t_val</span><span class="p">)</span>
</pre></div>
</div>
</div>
<div class="cell_output docutils container">
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Adam: Eta=0.001, Lambda=0
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [----------------------------------------] 0.000% | train_error: 12.9 | train_acc: 0.376 | val_error: 12.6 | val_acc: 0.392
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [----------------------------------------] 0.1000% | train_error: 12.9 | train_acc: 0.376 | val_error: 12.6 | val_acc: 0.392
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [----------------------------------------] 0.2000% | train_error: 12.9 | train_acc: 0.376 | val_error: 12.6 | val_acc: 0.392
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [----------------------------------------] 0.3000% | train_error: 12.9 | train_acc: 0.376 | val_error: 12.6 | val_acc: 0.392
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [----------------------------------------] 0.4000% | train_error: 12.9 | train_acc: 0.376 | val_error: 12.6 | val_acc: 0.392
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [----------------------------------------] 0.5000% | train_error: 12.9 | train_acc: 0.376 | val_error: 12.6 | val_acc: 0.392
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [----------------------------------------] 0.6000% | train_error: 12.9 | train_acc: 0.376 | val_error: 12.6 | val_acc: 0.392
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [----------------------------------------] 0.7000% | train_error: 12.9 | train_acc: 0.376 | val_error: 12.6 | val_acc: 0.392
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [----------------------------------------] 0.8000% | train_error: 12.9 | train_acc: 0.376 | val_error: 12.6 | val_acc: 0.392
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [----------------------------------------] 0.9000% | train_error: 12.9 | train_acc: 0.376 | val_error: 12.6 | val_acc: 0.392
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [----------------------------------------] 1.000% | train_error: 12.9 | train_acc: 0.376 | val_error: 12.6 | val_acc: 0.392
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [----------------------------------------] 1.100% | train_error: 12.9 | train_acc: 0.376 | val_error: 12.6 | val_acc: 0.392
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [----------------------------------------] 1.200% | train_error: 12.9 | train_acc: 0.376 | val_error: 12.6 | val_acc: 0.392
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [----------------------------------------] 1.300% | train_error: 12.9 | train_acc: 0.376 | val_error: 12.6 | val_acc: 0.392
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [----------------------------------------] 1.400% | train_error: 12.9 | train_acc: 0.376 | val_error: 12.6 | val_acc: 0.392
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [----------------------------------------] 1.500% | train_error: 12.9 | train_acc: 0.376 | val_error: 12.6 | val_acc: 0.392
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [----------------------------------------] 1.600% | train_error: 12.9 | train_acc: 0.376 | val_error: 12.6 | val_acc: 0.392
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [----------------------------------------] 1.700% | train_error: 12.9 | train_acc: 0.376 | val_error: 12.6 | val_acc: 0.392
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [----------------------------------------] 1.800% | train_error: 12.9 | train_acc: 0.376 | val_error: 12.6 | val_acc: 0.392
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [----------------------------------------] 1.900% | train_error: 12.9 | train_acc: 0.376 | val_error: 12.6 | val_acc: 0.392
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [----------------------------------------] 2.000% | train_error: 12.9 | train_acc: 0.376 | val_error: 12.6 | val_acc: 0.392
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [----------------------------------------] 2.100% | train_error: 12.9 | train_acc: 0.376 | val_error: 12.6 | val_acc: 0.392
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [----------------------------------------] 2.200% | train_error: 12.9 | train_acc: 0.376 | val_error: 12.6 | val_acc: 0.392
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [----------------------------------------] 2.300% | train_error: 12.9 | train_acc: 0.376 | val_error: 12.6 | val_acc: 0.392
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [----------------------------------------] 2.400% | train_error: 12.9 | train_acc: 0.376 | val_error: 12.6 | val_acc: 0.392
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [&gt;---------------------------------------] 2.500% | train_error: 12.9 | train_acc: 0.376 | val_error: 12.6 | val_acc: 0.392
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [&gt;---------------------------------------] 2.600% | train_error: 12.9 | train_acc: 0.376 | val_error: 12.6 | val_acc: 0.392
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [&gt;---------------------------------------] 2.700% | train_error: 12.9 | train_acc: 0.376 | val_error: 12.6 | val_acc: 0.392
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [&gt;---------------------------------------] 2.800% | train_error: 12.9 | train_acc: 0.376 | val_error: 12.6 | val_acc: 0.392
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [&gt;---------------------------------------] 2.900% | train_error: 12.9 | train_acc: 0.376 | val_error: 12.6 | val_acc: 0.392
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [&gt;---------------------------------------] 3.000% | train_error: 12.9 | train_acc: 0.376 | val_error: 12.6 | val_acc: 0.392
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [&gt;---------------------------------------] 3.100% | train_error: 12.9 | train_acc: 0.376 | val_error: 12.6 | val_acc: 0.392
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [&gt;---------------------------------------] 3.200% | train_error: 12.9 | train_acc: 0.376 | val_error: 12.6 | val_acc: 0.392
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [&gt;---------------------------------------] 3.300% | train_error: 12.9 | train_acc: 0.376 | val_error: 12.6 | val_acc: 0.392
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [&gt;---------------------------------------] 3.400% | train_error: 12.9 | train_acc: 0.376 | val_error: 12.6 | val_acc: 0.392
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [&gt;---------------------------------------] 3.500% | train_error: 12.9 | train_acc: 0.376 | val_error: 12.6 | val_acc: 0.392
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [&gt;---------------------------------------] 3.600% | train_error: 12.9 | train_acc: 0.376 | val_error: 12.6 | val_acc: 0.392
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [&gt;---------------------------------------] 3.700% | train_error: 12.9 | train_acc: 0.376 | val_error: 12.6 | val_acc: 0.392
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [&gt;---------------------------------------] 3.800% | train_error: 12.9 | train_acc: 0.376 | val_error: 12.6 | val_acc: 0.392
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [&gt;---------------------------------------] 3.900% | train_error: 12.9 | train_acc: 0.376 | val_error: 12.6 | val_acc: 0.392
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [&gt;---------------------------------------] 4.000% | train_error: 12.9 | train_acc: 0.376 | val_error: 12.6 | val_acc: 0.392
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [&gt;---------------------------------------] 4.100% | train_error: 12.9 | train_acc: 0.376 | val_error: 12.6 | val_acc: 0.392
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [&gt;---------------------------------------] 4.200% | train_error: 12.9 | train_acc: 0.376 | val_error: 12.6 | val_acc: 0.392
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [&gt;---------------------------------------] 4.300% | train_error: 12.9 | train_acc: 0.376 | val_error: 12.6 | val_acc: 0.392
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [&gt;---------------------------------------] 4.400% | train_error: 12.9 | train_acc: 0.376 | val_error: 12.6 | val_acc: 0.392
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [&gt;---------------------------------------] 4.500% | train_error: 12.9 | train_acc: 0.376 | val_error: 12.6 | val_acc: 0.392
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [&gt;---------------------------------------] 4.600% | train_error: 12.9 | train_acc: 0.376 | val_error: 12.6 | val_acc: 0.392
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [&gt;---------------------------------------] 4.700% | train_error: 12.9 | train_acc: 0.376 | val_error: 12.6 | val_acc: 0.392
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [&gt;---------------------------------------] 4.800% | train_error: 12.9 | train_acc: 0.376 | val_error: 12.6 | val_acc: 0.392
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [&gt;---------------------------------------] 4.900% | train_error: 12.9 | train_acc: 0.376 | val_error: 12.6 | val_acc: 0.392
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=&gt;--------------------------------------] 5.000% | train_error: 12.9 | train_acc: 0.376 | val_error: 12.6 | val_acc: 0.392
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=&gt;--------------------------------------] 5.100% | train_error: 12.9 | train_acc: 0.376 | val_error: 12.6 | val_acc: 0.392
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=&gt;--------------------------------------] 5.200% | train_error: 12.9 | train_acc: 0.376 | val_error: 12.6 | val_acc: 0.392
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=&gt;--------------------------------------] 5.300% | train_error: 12.9 | train_acc: 0.376 | val_error: 12.6 | val_acc: 0.392
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=&gt;--------------------------------------] 5.400% | train_error: 12.9 | train_acc: 0.376 | val_error: 12.6 | val_acc: 0.392
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=&gt;--------------------------------------] 5.500% | train_error: 12.9 | train_acc: 0.376 | val_error: 12.6 | val_acc: 0.392
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=&gt;--------------------------------------] 5.600% | train_error: 12.9 | train_acc: 0.376 | val_error: 12.6 | val_acc: 0.392
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=&gt;--------------------------------------] 5.700% | train_error: 12.9 | train_acc: 0.376 | val_error: 12.6 | val_acc: 0.392
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=&gt;--------------------------------------] 5.800% | train_error: 12.9 | train_acc: 0.376 | val_error: 12.6 | val_acc: 0.392
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=&gt;--------------------------------------] 5.900% | train_error: 12.9 | train_acc: 0.376 | val_error: 12.6 | val_acc: 0.392
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=&gt;--------------------------------------] 6.000% | train_error: 12.9 | train_acc: 0.376 | val_error: 12.6 | val_acc: 0.392
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=&gt;--------------------------------------] 6.100% | train_error: 12.9 | train_acc: 0.376 | val_error: 12.6 | val_acc: 0.392
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=&gt;--------------------------------------] 6.200% | train_error: 12.9 | train_acc: 0.376 | val_error: 12.6 | val_acc: 0.392
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=&gt;--------------------------------------] 6.300% | train_error: 12.9 | train_acc: 0.376 | val_error: 12.6 | val_acc: 0.392
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=&gt;--------------------------------------] 6.400% | train_error: 12.9 | train_acc: 0.376 | val_error: 12.6 | val_acc: 0.392
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=&gt;--------------------------------------] 6.500% | train_error: 12.9 | train_acc: 0.376 | val_error: 12.6 | val_acc: 0.392
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=&gt;--------------------------------------] 6.600% | train_error: 12.9 | train_acc: 0.376 | val_error: 12.6 | val_acc: 0.392
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=&gt;--------------------------------------] 6.700% | train_error: 12.9 | train_acc: 0.376 | val_error: 12.6 | val_acc: 0.392
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=&gt;--------------------------------------] 6.800% | train_error: 12.9 | train_acc: 0.376 | val_error: 12.6 | val_acc: 0.392
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=&gt;--------------------------------------] 6.900% | train_error: 12.9 | train_acc: 0.376 | val_error: 12.6 | val_acc: 0.392
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=&gt;--------------------------------------] 7.000% | train_error: 12.9 | train_acc: 0.376 | val_error: 12.6 | val_acc: 0.392
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=&gt;--------------------------------------] 7.100% | train_error: 12.9 | train_acc: 0.376 | val_error: 12.8 | val_acc: 0.385
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=&gt;--------------------------------------] 7.200% | train_error: 12.9 | train_acc: 0.376 | val_error: 12.8 | val_acc: 0.385
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=&gt;--------------------------------------] 7.300% | train_error: 12.9 | train_acc: 0.376 | val_error: 12.8 | val_acc: 0.385
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=&gt;--------------------------------------] 7.400% | train_error: 12.9 | train_acc: 0.376 | val_error: 12.8 | val_acc: 0.385
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==&gt;-------------------------------------] 7.500% | train_error: 12.9 | train_acc: 0.376 | val_error: 12.8 | val_acc: 0.385
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==&gt;-------------------------------------] 7.600% | train_error: 12.9 | train_acc: 0.376 | val_error: 12.8 | val_acc: 0.385
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==&gt;-------------------------------------] 7.700% | train_error: 12.9 | train_acc: 0.376 | val_error: 12.8 | val_acc: 0.385
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==&gt;-------------------------------------] 7.800% | train_error: 12.9 | train_acc: 0.376 | val_error: 12.8 | val_acc: 0.385
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==&gt;-------------------------------------] 7.900% | train_error: 12.9 | train_acc: 0.376 | val_error: 12.8 | val_acc: 0.385
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==&gt;-------------------------------------] 8.000% | train_error: 12.9 | train_acc: 0.376 | val_error: 12.8 | val_acc: 0.385
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==&gt;-------------------------------------] 8.100% | train_error: 12.9 | train_acc: 0.376 | val_error: 12.8 | val_acc: 0.385
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==&gt;-------------------------------------] 8.200% | train_error: 12.9 | train_acc: 0.376 | val_error: 12.8 | val_acc: 0.385
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==&gt;-------------------------------------] 8.300% | train_error: 12.9 | train_acc: 0.376 | val_error: 12.8 | val_acc: 0.385
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==&gt;-------------------------------------] 8.400% | train_error: 12.9 | train_acc: 0.376 | val_error: 12.8 | val_acc: 0.385
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==&gt;-------------------------------------] 8.500% | train_error: 12.9 | train_acc: 0.376 | val_error: 12.8 | val_acc: 0.385
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==&gt;-------------------------------------] 8.600% | train_error: 12.9 | train_acc: 0.376 | val_error: 12.8 | val_acc: 0.385
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==&gt;-------------------------------------] 8.700% | train_error: 12.9 | train_acc: 0.376 | val_error: 12.8 | val_acc: 0.385
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==&gt;-------------------------------------] 8.800% | train_error: 12.9 | train_acc: 0.376 | val_error: 12.8 | val_acc: 0.385
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==&gt;-------------------------------------] 8.900% | train_error: 12.9 | train_acc: 0.376 | val_error: 12.8 | val_acc: 0.385
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==&gt;-------------------------------------] 9.000% | train_error: 12.9 | train_acc: 0.376 | val_error: 12.8 | val_acc: 0.385
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==&gt;-------------------------------------] 9.100% | train_error: 12.9 | train_acc: 0.376 | val_error: 12.8 | val_acc: 0.385
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==&gt;-------------------------------------] 9.200% | train_error: 12.9 | train_acc: 0.376 | val_error: 12.8 | val_acc: 0.385
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==&gt;-------------------------------------] 9.300% | train_error: 12.9 | train_acc: 0.376 | val_error: 12.8 | val_acc: 0.385
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==&gt;-------------------------------------] 9.400% | train_error: 12.9 | train_acc: 0.376 | val_error: 12.8 | val_acc: 0.385
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==&gt;-------------------------------------] 9.500% | train_error: 12.9 | train_acc: 0.376 | val_error: 12.8 | val_acc: 0.385
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==&gt;-------------------------------------] 9.600% | train_error: 12.9 | train_acc: 0.376 | val_error: 12.8 | val_acc: 0.385
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==&gt;-------------------------------------] 9.700% | train_error: 12.9 | train_acc: 0.376 | val_error: 12.8 | val_acc: 0.385
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==&gt;-------------------------------------] 9.800% | train_error: 12.9 | train_acc: 0.376 | val_error: 12.8 | val_acc: 0.385
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==&gt;-------------------------------------] 9.900% | train_error: 13.0 | train_acc: 0.373 | val_error: 12.8 | val_acc: 0.385
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===&gt;------------------------------------] 10.00% | train_error: 13.0 | train_acc: 0.373 | val_error: 12.8 | val_acc: 0.385
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===&gt;------------------------------------] 10.10% | train_error: 13.1 | train_acc: 0.366 | val_error: 12.8 | val_acc: 0.385
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===&gt;------------------------------------] 10.20% | train_error: 13.1 | train_acc: 0.366 | val_error: 12.8 | val_acc: 0.385
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===&gt;------------------------------------] 10.30% | train_error: 13.1 | train_acc: 0.366 | val_error: 12.8 | val_acc: 0.385
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===&gt;------------------------------------] 10.40% | train_error: 13.1 | train_acc: 0.366 | val_error: 12.8 | val_acc: 0.385
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===&gt;------------------------------------] 10.50% | train_error: 13.0 | train_acc: 0.371 | val_error: 12.8 | val_acc: 0.385
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===&gt;------------------------------------] 10.60% | train_error: 13.0 | train_acc: 0.371 | val_error: 12.8 | val_acc: 0.385
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===&gt;------------------------------------] 10.70% | train_error: 13.0 | train_acc: 0.371 | val_error: 12.8 | val_acc: 0.385
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===&gt;------------------------------------] 10.80% | train_error: 13.1 | train_acc: 0.369 | val_error: 12.8 | val_acc: 0.385
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===&gt;------------------------------------] 10.90% | train_error: 13.1 | train_acc: 0.369 | val_error: 12.8 | val_acc: 0.385
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===&gt;------------------------------------] 11.00% | train_error: 13.1 | train_acc: 0.369 | val_error: 12.8 | val_acc: 0.385
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===&gt;------------------------------------] 11.10% | train_error: 13.1 | train_acc: 0.369 | val_error: 12.8 | val_acc: 0.385
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===&gt;------------------------------------] 11.20% | train_error: 13.1 | train_acc: 0.369 | val_error: 12.8 | val_acc: 0.385
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===&gt;------------------------------------] 11.30% | train_error: 13.1 | train_acc: 0.369 | val_error: 12.8 | val_acc: 0.385
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===&gt;------------------------------------] 11.40% | train_error: 13.1 | train_acc: 0.369 | val_error: 12.8 | val_acc: 0.385
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===&gt;------------------------------------] 11.50% | train_error: 13.1 | train_acc: 0.369 | val_error: 12.8 | val_acc: 0.385
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===&gt;------------------------------------] 11.60% | train_error: 13.1 | train_acc: 0.369 | val_error: 12.8 | val_acc: 0.385
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===&gt;------------------------------------] 11.70% | train_error: 13.1 | train_acc: 0.369 | val_error: 12.8 | val_acc: 0.385
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===&gt;------------------------------------] 11.80% | train_error: 13.1 | train_acc: 0.369 | val_error: 12.8 | val_acc: 0.385
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===&gt;------------------------------------] 11.90% | train_error: 13.1 | train_acc: 0.366 | val_error: 12.8 | val_acc: 0.385
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===&gt;------------------------------------] 12.00% | train_error: 13.1 | train_acc: 0.366 | val_error: 12.8 | val_acc: 0.385
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===&gt;------------------------------------] 12.10% | train_error: 13.1 | train_acc: 0.366 | val_error: 12.8 | val_acc: 0.385
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===&gt;------------------------------------] 12.20% | train_error: 13.1 | train_acc: 0.366 | val_error: 12.8 | val_acc: 0.385
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===&gt;------------------------------------] 12.30% | train_error: 13.1 | train_acc: 0.366 | val_error: 12.8 | val_acc: 0.385
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===&gt;------------------------------------] 12.40% | train_error: 13.1 | train_acc: 0.366 | val_error: 12.8 | val_acc: 0.385
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [====&gt;-----------------------------------] 12.50% | train_error: 13.1 | train_acc: 0.366 | val_error: 12.8 | val_acc: 0.385
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [====&gt;-----------------------------------] 12.60% | train_error: 13.1 | train_acc: 0.366 | val_error: 12.8 | val_acc: 0.385
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [====&gt;-----------------------------------] 12.70% | train_error: 13.1 | train_acc: 0.366 | val_error: 12.8 | val_acc: 0.385
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [====&gt;-----------------------------------] 12.80% | train_error: 13.1 | train_acc: 0.366 | val_error: 12.8 | val_acc: 0.385
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [====&gt;-----------------------------------] 12.90% | train_error: 13.1 | train_acc: 0.366 | val_error: 12.8 | val_acc: 0.385
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [====&gt;-----------------------------------] 13.00% | train_error: 13.1 | train_acc: 0.366 | val_error: 12.8 | val_acc: 0.385
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [====&gt;-----------------------------------] 13.10% | train_error: 13.2 | train_acc: 0.362 | val_error: 12.8 | val_acc: 0.385
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [====&gt;-----------------------------------] 13.20% | train_error: 13.2 | train_acc: 0.362 | val_error: 12.8 | val_acc: 0.385
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [====&gt;-----------------------------------] 13.30% | train_error: 13.2 | train_acc: 0.362 | val_error: 12.8 | val_acc: 0.385
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [====&gt;-----------------------------------] 13.40% | train_error: 13.2 | train_acc: 0.362 | val_error: 12.8 | val_acc: 0.385
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [====&gt;-----------------------------------] 13.50% | train_error: 13.2 | train_acc: 0.362 | val_error: 12.8 | val_acc: 0.385
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [====&gt;-----------------------------------] 13.60% | train_error: 13.2 | train_acc: 0.362 | val_error: 12.8 | val_acc: 0.385
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [====&gt;-----------------------------------] 13.70% | train_error: 13.2 | train_acc: 0.362 | val_error: 12.8 | val_acc: 0.385
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [====&gt;-----------------------------------] 13.80% | train_error: 13.3 | train_acc: 0.359 | val_error: 12.9 | val_acc: 0.378
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [====&gt;-----------------------------------] 13.90% | train_error: 13.4 | train_acc: 0.354 | val_error: 12.9 | val_acc: 0.378
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [====&gt;-----------------------------------] 14.00% | train_error: 13.4 | train_acc: 0.354 | val_error: 13.0 | val_acc: 0.371
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [====&gt;-----------------------------------] 14.10% | train_error: 13.4 | train_acc: 0.354 | val_error: 13.0 | val_acc: 0.371
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [====&gt;-----------------------------------] 14.20% | train_error: 13.4 | train_acc: 0.354 | val_error: 13.0 | val_acc: 0.371
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [====&gt;-----------------------------------] 14.30% | train_error: 13.4 | train_acc: 0.354 | val_error: 13.0 | val_acc: 0.371
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [====&gt;-----------------------------------] 14.40% | train_error: 13.4 | train_acc: 0.354 | val_error: 13.0 | val_acc: 0.371
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [====&gt;-----------------------------------] 14.50% | train_error: 13.4 | train_acc: 0.354 | val_error: 13.0 | val_acc: 0.371
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [====&gt;-----------------------------------] 14.60% | train_error: 13.4 | train_acc: 0.354 | val_error: 13.2 | val_acc: 0.364
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [====&gt;-----------------------------------] 14.70% | train_error: 13.4 | train_acc: 0.354 | val_error: 13.2 | val_acc: 0.364
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [====&gt;-----------------------------------] 14.80% | train_error: 13.4 | train_acc: 0.352 | val_error: 13.2 | val_acc: 0.364
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [====&gt;-----------------------------------] 14.90% | train_error: 13.4 | train_acc: 0.352 | val_error: 13.2 | val_acc: 0.364
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=====&gt;----------------------------------] 15.00% | train_error: 13.5 | train_acc: 0.347 | val_error: 13.2 | val_acc: 0.364
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=====&gt;----------------------------------] 15.10% | train_error: 13.6 | train_acc: 0.345 | val_error: 13.2 | val_acc: 0.364
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=====&gt;----------------------------------] 15.20% | train_error: 13.6 | train_acc: 0.345 | val_error: 13.2 | val_acc: 0.364
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=====&gt;----------------------------------] 15.30% | train_error: 13.8 | train_acc: 0.336 | val_error: 13.2 | val_acc: 0.364
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=====&gt;----------------------------------] 15.40% | train_error: 13.8 | train_acc: 0.336 | val_error: 13.2 | val_acc: 0.364
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=====&gt;----------------------------------] 15.50% | train_error: 13.8 | train_acc: 0.336 | val_error: 13.2 | val_acc: 0.364
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=====&gt;----------------------------------] 15.60% | train_error: 13.8 | train_acc: 0.333 | val_error: 13.2 | val_acc: 0.364
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=====&gt;----------------------------------] 15.70% | train_error: 13.8 | train_acc: 0.333 | val_error: 13.2 | val_acc: 0.364
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=====&gt;----------------------------------] 15.80% | train_error: 13.8 | train_acc: 0.333 | val_error: 13.2 | val_acc: 0.364
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=====&gt;----------------------------------] 15.90% | train_error: 13.8 | train_acc: 0.333 | val_error: 13.2 | val_acc: 0.364
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=====&gt;----------------------------------] 16.00% | train_error: 13.8 | train_acc: 0.333 | val_error: 13.2 | val_acc: 0.364
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=====&gt;----------------------------------] 16.10% | train_error: 13.8 | train_acc: 0.333 | val_error: 13.2 | val_acc: 0.364
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=====&gt;----------------------------------] 16.20% | train_error: 13.8 | train_acc: 0.333 | val_error: 13.2 | val_acc: 0.364
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=====&gt;----------------------------------] 16.30% | train_error: 14.0 | train_acc: 0.326 | val_error: 13.2 | val_acc: 0.364
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=====&gt;----------------------------------] 16.40% | train_error: 14.0 | train_acc: 0.326 | val_error: 13.2 | val_acc: 0.364
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=====&gt;----------------------------------] 16.50% | train_error: 14.0 | train_acc: 0.326 | val_error: 13.2 | val_acc: 0.364
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=====&gt;----------------------------------] 16.60% | train_error: 14.0 | train_acc: 0.326 | val_error: 13.3 | val_acc: 0.357
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=====&gt;----------------------------------] 16.70% | train_error: 14.1 | train_acc: 0.322 | val_error: 13.5 | val_acc: 0.350
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=====&gt;----------------------------------] 16.80% | train_error: 14.1 | train_acc: 0.322 | val_error: 13.5 | val_acc: 0.350
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=====&gt;----------------------------------] 16.90% | train_error: 14.1 | train_acc: 0.319 | val_error: 13.6 | val_acc: 0.343
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=====&gt;----------------------------------] 17.00% | train_error: 14.1 | train_acc: 0.319 | val_error: 13.6 | val_acc: 0.343
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=====&gt;----------------------------------] 17.10% | train_error: 14.1 | train_acc: 0.319 | val_error: 13.6 | val_acc: 0.343
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=====&gt;----------------------------------] 17.20% | train_error: 14.2 | train_acc: 0.317 | val_error: 13.6 | val_acc: 0.343
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=====&gt;----------------------------------] 17.30% | train_error: 14.2 | train_acc: 0.317 | val_error: 13.6 | val_acc: 0.343
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=====&gt;----------------------------------] 17.40% | train_error: 14.2 | train_acc: 0.317 | val_error: 13.6 | val_acc: 0.343
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [======&gt;---------------------------------] 17.50% | train_error: 14.2 | train_acc: 0.317 | val_error: 13.6 | val_acc: 0.343
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [======&gt;---------------------------------] 17.60% | train_error: 14.3 | train_acc: 0.312 | val_error: 13.6 | val_acc: 0.343
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [======&gt;---------------------------------] 17.70% | train_error: 14.4 | train_acc: 0.308 | val_error: 13.6 | val_acc: 0.343
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [======&gt;---------------------------------] 17.80% | train_error: 14.4 | train_acc: 0.308 | val_error: 13.6 | val_acc: 0.343
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [======&gt;---------------------------------] 17.90% | train_error: 14.4 | train_acc: 0.308 | val_error: 13.6 | val_acc: 0.343
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [======&gt;---------------------------------] 18.00% | train_error: 14.4 | train_acc: 0.308 | val_error: 13.6 | val_acc: 0.343
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [======&gt;---------------------------------] 18.10% | train_error: 14.4 | train_acc: 0.305 | val_error: 13.6 | val_acc: 0.343
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [======&gt;---------------------------------] 18.20% | train_error: 14.4 | train_acc: 0.305 | val_error: 13.6 | val_acc: 0.343
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [======&gt;---------------------------------] 18.30% | train_error: 14.4 | train_acc: 0.305 | val_error: 13.6 | val_acc: 0.343
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [======&gt;---------------------------------] 18.40% | train_error: 14.5 | train_acc: 0.298 | val_error: 13.6 | val_acc: 0.343
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [======&gt;---------------------------------] 18.50% | train_error: 14.5 | train_acc: 0.298 | val_error: 13.6 | val_acc: 0.343
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [======&gt;---------------------------------] 18.60% | train_error: 14.6 | train_acc: 0.296 | val_error: 13.6 | val_acc: 0.343
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [======&gt;---------------------------------] 18.70% | train_error: 14.6 | train_acc: 0.293 | val_error: 13.6 | val_acc: 0.343
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [======&gt;---------------------------------] 18.80% | train_error: 14.7 | train_acc: 0.291 | val_error: 13.6 | val_acc: 0.343
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [======&gt;---------------------------------] 18.90% | train_error: 14.7 | train_acc: 0.291 | val_error: 13.6 | val_acc: 0.343
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [======&gt;---------------------------------] 19.00% | train_error: 14.7 | train_acc: 0.291 | val_error: 13.6 | val_acc: 0.343
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [======&gt;---------------------------------] 19.10% | train_error: 14.7 | train_acc: 0.291 | val_error: 13.6 | val_acc: 0.343
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [======&gt;---------------------------------] 19.20% | train_error: 14.7 | train_acc: 0.291 | val_error: 13.5 | val_acc: 0.350
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [======&gt;---------------------------------] 19.30% | train_error: 14.8 | train_acc: 0.286 | val_error: 13.5 | val_acc: 0.350
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [======&gt;---------------------------------] 19.40% | train_error: 14.8 | train_acc: 0.284 | val_error: 13.5 | val_acc: 0.350
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [======&gt;---------------------------------] 19.50% | train_error: 14.8 | train_acc: 0.284 | val_error: 13.5 | val_acc: 0.350
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [======&gt;---------------------------------] 19.60% | train_error: 14.9 | train_acc: 0.282 | val_error: 13.5 | val_acc: 0.350
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [======&gt;---------------------------------] 19.70% | train_error: 14.9 | train_acc: 0.282 | val_error: 13.5 | val_acc: 0.350
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [======&gt;---------------------------------] 19.80% | train_error: 14.9 | train_acc: 0.282 | val_error: 13.5 | val_acc: 0.350
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [======&gt;---------------------------------] 19.90% | train_error: 14.9 | train_acc: 0.282 | val_error: 13.5 | val_acc: 0.350
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=======&gt;--------------------------------] 20.00% | train_error: 14.9 | train_acc: 0.279 | val_error: 13.6 | val_acc: 0.343
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=======&gt;--------------------------------] 20.10% | train_error: 15.1 | train_acc: 0.272 | val_error: 13.6 | val_acc: 0.343
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=======&gt;--------------------------------] 20.20% | train_error: 15.1 | train_acc: 0.272 | val_error: 13.6 | val_acc: 0.343
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=======&gt;--------------------------------] 20.30% | train_error: 15.1 | train_acc: 0.272 | val_error: 13.6 | val_acc: 0.343
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=======&gt;--------------------------------] 20.40% | train_error: 15.1 | train_acc: 0.270 | val_error: 13.6 | val_acc: 0.343
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=======&gt;--------------------------------] 20.50% | train_error: 15.1 | train_acc: 0.270 | val_error: 13.6 | val_acc: 0.343
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=======&gt;--------------------------------] 20.60% | train_error: 15.4 | train_acc: 0.258 | val_error: 13.6 | val_acc: 0.343
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=======&gt;--------------------------------] 20.70% | train_error: 15.4 | train_acc: 0.258 | val_error: 13.8 | val_acc: 0.336
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=======&gt;--------------------------------] 20.80% | train_error: 15.4 | train_acc: 0.258 | val_error: 13.8 | val_acc: 0.336
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=======&gt;--------------------------------] 20.90% | train_error: 15.4 | train_acc: 0.258 | val_error: 13.8 | val_acc: 0.336
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=======&gt;--------------------------------] 21.00% | train_error: 15.4 | train_acc: 0.258 | val_error: 13.8 | val_acc: 0.336
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=======&gt;--------------------------------] 21.10% | train_error: 15.4 | train_acc: 0.258 | val_error: 13.8 | val_acc: 0.336
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=======&gt;--------------------------------] 21.20% | train_error: 15.3 | train_acc: 0.261 | val_error: 13.9 | val_acc: 0.329
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=======&gt;--------------------------------] 21.30% | train_error: 15.5 | train_acc: 0.254 | val_error: 13.9 | val_acc: 0.329
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=======&gt;--------------------------------] 21.40% | train_error: 15.5 | train_acc: 0.254 | val_error: 14.2 | val_acc: 0.315
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=======&gt;--------------------------------] 21.50% | train_error: 15.5 | train_acc: 0.254 | val_error: 14.2 | val_acc: 0.315
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=======&gt;--------------------------------] 21.60% | train_error: 15.5 | train_acc: 0.254 | val_error: 14.2 | val_acc: 0.315
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=======&gt;--------------------------------] 21.70% | train_error: 15.4 | train_acc: 0.256 | val_error: 14.2 | val_acc: 0.315
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=======&gt;--------------------------------] 21.80% | train_error: 15.4 | train_acc: 0.256 | val_error: 14.2 | val_acc: 0.315
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=======&gt;--------------------------------] 21.90% | train_error: 15.4 | train_acc: 0.256 | val_error: 14.2 | val_acc: 0.315
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=======&gt;--------------------------------] 22.00% | train_error: 15.4 | train_acc: 0.256 | val_error: 14.2 | val_acc: 0.315
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=======&gt;--------------------------------] 22.10% | train_error: 15.6 | train_acc: 0.249 | val_error: 14.2 | val_acc: 0.315
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=======&gt;--------------------------------] 22.20% | train_error: 15.6 | train_acc: 0.249 | val_error: 14.2 | val_acc: 0.315
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=======&gt;--------------------------------] 22.30% | train_error: 15.6 | train_acc: 0.249 | val_error: 14.2 | val_acc: 0.315
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=======&gt;--------------------------------] 22.40% | train_error: 15.7 | train_acc: 0.244 | val_error: 14.3 | val_acc: 0.308
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [========&gt;-------------------------------] 22.50% | train_error: 15.7 | train_acc: 0.242 | val_error: 14.3 | val_acc: 0.308
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [========&gt;-------------------------------] 22.60% | train_error: 15.7 | train_acc: 0.242 | val_error: 14.3 | val_acc: 0.308
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [========&gt;-------------------------------] 22.70% | train_error: 15.7 | train_acc: 0.242 | val_error: 14.5 | val_acc: 0.301
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [========&gt;-------------------------------] 22.80% | train_error: 15.7 | train_acc: 0.242 | val_error: 14.5 | val_acc: 0.301
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [========&gt;-------------------------------] 22.90% | train_error: 15.8 | train_acc: 0.239 | val_error: 14.5 | val_acc: 0.301
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [========&gt;-------------------------------] 23.00% | train_error: 15.9 | train_acc: 0.235 | val_error: 14.5 | val_acc: 0.301
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [========&gt;-------------------------------] 23.10% | train_error: 15.9 | train_acc: 0.235 | val_error: 14.5 | val_acc: 0.301
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [========&gt;-------------------------------] 23.20% | train_error: 15.9 | train_acc: 0.232 | val_error: 14.6 | val_acc: 0.294
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [========&gt;-------------------------------] 23.30% | train_error: 15.9 | train_acc: 0.232 | val_error: 14.6 | val_acc: 0.294
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [========&gt;-------------------------------] 23.40% | train_error: 15.9 | train_acc: 0.232 | val_error: 14.6 | val_acc: 0.294
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [========&gt;-------------------------------] 23.50% | train_error: 15.9 | train_acc: 0.232 | val_error: 14.9 | val_acc: 0.280
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [========&gt;-------------------------------] 23.60% | train_error: 15.9 | train_acc: 0.232 | val_error: 14.8 | val_acc: 0.287
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [========&gt;-------------------------------] 23.70% | train_error: 15.9 | train_acc: 0.232 | val_error: 14.8 | val_acc: 0.287
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [========&gt;-------------------------------] 23.80% | train_error: 16.0 | train_acc: 0.230 | val_error: 14.8 | val_acc: 0.287
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [========&gt;-------------------------------] 23.90% | train_error: 16.0 | train_acc: 0.230 | val_error: 14.9 | val_acc: 0.280
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [========&gt;-------------------------------] 24.00% | train_error: 16.0 | train_acc: 0.230 | val_error: 14.9 | val_acc: 0.280
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [========&gt;-------------------------------] 24.10% | train_error: 16.0 | train_acc: 0.230 | val_error: 15.1 | val_acc: 0.273
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [========&gt;-------------------------------] 24.20% | train_error: 16.0 | train_acc: 0.230 | val_error: 15.1 | val_acc: 0.273
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [========&gt;-------------------------------] 24.30% | train_error: 16.0 | train_acc: 0.230 | val_error: 15.1 | val_acc: 0.273
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [========&gt;-------------------------------] 24.40% | train_error: 16.0 | train_acc: 0.228 | val_error: 15.2 | val_acc: 0.266
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [========&gt;-------------------------------] 24.50% | train_error: 16.0 | train_acc: 0.228 | val_error: 15.4 | val_acc: 0.259
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [========&gt;-------------------------------] 24.60% | train_error: 16.0 | train_acc: 0.228 | val_error: 15.4 | val_acc: 0.259
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [========&gt;-------------------------------] 24.70% | train_error: 16.0 | train_acc: 0.228 | val_error: 15.4 | val_acc: 0.259
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [========&gt;-------------------------------] 24.80% | train_error: 16.0 | train_acc: 0.228 | val_error: 15.5 | val_acc: 0.252
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [========&gt;-------------------------------] 24.90% | train_error: 16.0 | train_acc: 0.228 | val_error: 15.5 | val_acc: 0.252
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=========&gt;------------------------------] 25.00% | train_error: 16.0 | train_acc: 0.228 | val_error: 15.5 | val_acc: 0.252
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=========&gt;------------------------------] 25.10% | train_error: 16.0 | train_acc: 0.228 | val_error: 15.5 | val_acc: 0.252
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=========&gt;------------------------------] 25.20% | train_error: 16.0 | train_acc: 0.228 | val_error: 15.5 | val_acc: 0.252
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=========&gt;------------------------------] 25.30% | train_error: 16.0 | train_acc: 0.228 | val_error: 15.5 | val_acc: 0.252
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=========&gt;------------------------------] 25.40% | train_error: 16.0 | train_acc: 0.228 | val_error: 15.7 | val_acc: 0.245
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=========&gt;------------------------------] 25.50% | train_error: 15.8 | train_acc: 0.237 | val_error: 15.8 | val_acc: 0.238
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=========&gt;------------------------------] 25.60% | train_error: 15.8 | train_acc: 0.237 | val_error: 15.8 | val_acc: 0.238
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=========&gt;------------------------------] 25.70% | train_error: 15.8 | train_acc: 0.237 | val_error: 15.8 | val_acc: 0.238
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=========&gt;------------------------------] 25.80% | train_error: 15.8 | train_acc: 0.237 | val_error: 15.8 | val_acc: 0.238
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=========&gt;------------------------------] 25.90% | train_error: 15.8 | train_acc: 0.237 | val_error: 15.8 | val_acc: 0.238
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=========&gt;------------------------------] 26.00% | train_error: 15.8 | train_acc: 0.237 | val_error: 15.8 | val_acc: 0.238
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=========&gt;------------------------------] 26.10% | train_error: 15.8 | train_acc: 0.237 | val_error: 15.8 | val_acc: 0.238
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=========&gt;------------------------------] 26.20% | train_error: 15.8 | train_acc: 0.237 | val_error: 15.8 | val_acc: 0.238
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=========&gt;------------------------------] 26.30% | train_error: 15.8 | train_acc: 0.237 | val_error: 15.8 | val_acc: 0.238
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=========&gt;------------------------------] 26.40% | train_error: 15.8 | train_acc: 0.237 | val_error: 15.8 | val_acc: 0.238
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=========&gt;------------------------------] 26.50% | train_error: 15.9 | train_acc: 0.235 | val_error: 15.8 | val_acc: 0.238
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=========&gt;------------------------------] 26.60% | train_error: 15.8 | train_acc: 0.237 | val_error: 15.8 | val_acc: 0.238
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=========&gt;------------------------------] 26.70% | train_error: 15.8 | train_acc: 0.237 | val_error: 15.8 | val_acc: 0.238
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=========&gt;------------------------------] 26.80% | train_error: 15.8 | train_acc: 0.237 | val_error: 15.8 | val_acc: 0.238
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=========&gt;------------------------------] 26.90% | train_error: 15.8 | train_acc: 0.237 | val_error: 15.8 | val_acc: 0.238
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=========&gt;------------------------------] 27.00% | train_error: 15.8 | train_acc: 0.237 | val_error: 15.8 | val_acc: 0.238
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=========&gt;------------------------------] 27.10% | train_error: 15.8 | train_acc: 0.237 | val_error: 15.8 | val_acc: 0.238
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=========&gt;------------------------------] 27.20% | train_error: 15.8 | train_acc: 0.237 | val_error: 15.7 | val_acc: 0.245
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=========&gt;------------------------------] 27.30% | train_error: 15.8 | train_acc: 0.237 | val_error: 15.7 | val_acc: 0.245
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=========&gt;------------------------------] 27.40% | train_error: 15.8 | train_acc: 0.237 | val_error: 15.7 | val_acc: 0.245
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==========&gt;-----------------------------] 27.50% | train_error: 15.8 | train_acc: 0.237 | val_error: 15.7 | val_acc: 0.245
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==========&gt;-----------------------------] 27.60% | train_error: 15.8 | train_acc: 0.237 | val_error: 15.7 | val_acc: 0.245
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==========&gt;-----------------------------] 27.70% | train_error: 15.8 | train_acc: 0.237 | val_error: 15.7 | val_acc: 0.245
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==========&gt;-----------------------------] 27.80% | train_error: 15.8 | train_acc: 0.237 | val_error: 15.7 | val_acc: 0.245
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==========&gt;-----------------------------] 27.90% | train_error: 15.8 | train_acc: 0.237 | val_error: 15.7 | val_acc: 0.245
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==========&gt;-----------------------------] 28.00% | train_error: 15.8 | train_acc: 0.237 | val_error: 15.7 | val_acc: 0.245
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==========&gt;-----------------------------] 28.10% | train_error: 15.8 | train_acc: 0.237 | val_error: 15.7 | val_acc: 0.245
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==========&gt;-----------------------------] 28.20% | train_error: 15.8 | train_acc: 0.237 | val_error: 15.7 | val_acc: 0.245
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==========&gt;-----------------------------] 28.30% | train_error: 15.8 | train_acc: 0.237 | val_error: 15.7 | val_acc: 0.245
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==========&gt;-----------------------------] 28.40% | train_error: 15.8 | train_acc: 0.237 | val_error: 15.7 | val_acc: 0.245
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==========&gt;-----------------------------] 28.50% | train_error: 15.8 | train_acc: 0.237 | val_error: 15.7 | val_acc: 0.245
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==========&gt;-----------------------------] 28.60% | train_error: 15.8 | train_acc: 0.237 | val_error: 15.7 | val_acc: 0.245
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==========&gt;-----------------------------] 28.70% | train_error: 15.8 | train_acc: 0.237 | val_error: 15.7 | val_acc: 0.245
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==========&gt;-----------------------------] 28.80% | train_error: 15.8 | train_acc: 0.237 | val_error: 15.7 | val_acc: 0.245
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==========&gt;-----------------------------] 28.90% | train_error: 15.8 | train_acc: 0.237 | val_error: 15.7 | val_acc: 0.245
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==========&gt;-----------------------------] 29.00% | train_error: 15.8 | train_acc: 0.237 | val_error: 15.7 | val_acc: 0.245
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==========&gt;-----------------------------] 29.10% | train_error: 15.8 | train_acc: 0.237 | val_error: 15.7 | val_acc: 0.245
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==========&gt;-----------------------------] 29.20% | train_error: 15.8 | train_acc: 0.237 | val_error: 15.7 | val_acc: 0.245
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==========&gt;-----------------------------] 29.30% | train_error: 16.0 | train_acc: 0.230 | val_error: 15.7 | val_acc: 0.245
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==========&gt;-----------------------------] 29.40% | train_error: 15.8 | train_acc: 0.237 | val_error: 15.7 | val_acc: 0.245
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==========&gt;-----------------------------] 29.50% | train_error: 15.8 | train_acc: 0.237 | val_error: 15.7 | val_acc: 0.245
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==========&gt;-----------------------------] 29.60% | train_error: 15.8 | train_acc: 0.237 | val_error: 15.7 | val_acc: 0.245
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==========&gt;-----------------------------] 29.70% | train_error: 15.8 | train_acc: 0.237 | val_error: 15.7 | val_acc: 0.245
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==========&gt;-----------------------------] 29.80% | train_error: 15.8 | train_acc: 0.237 | val_error: 15.7 | val_acc: 0.245
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==========&gt;-----------------------------] 29.90% | train_error: 15.8 | train_acc: 0.237 | val_error: 15.7 | val_acc: 0.245
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===========&gt;----------------------------] 30.00% | train_error: 15.8 | train_acc: 0.237 | val_error: 15.7 | val_acc: 0.245
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===========&gt;----------------------------] 30.10% | train_error: 15.8 | train_acc: 0.237 | val_error: 15.7 | val_acc: 0.245
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===========&gt;----------------------------] 30.20% | train_error: 15.8 | train_acc: 0.237 | val_error: 15.7 | val_acc: 0.245
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===========&gt;----------------------------] 30.30% | train_error: 15.9 | train_acc: 0.235 | val_error: 15.7 | val_acc: 0.245
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===========&gt;----------------------------] 30.40% | train_error: 15.9 | train_acc: 0.235 | val_error: 15.7 | val_acc: 0.245
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===========&gt;----------------------------] 30.50% | train_error: 15.9 | train_acc: 0.235 | val_error: 15.7 | val_acc: 0.245
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===========&gt;----------------------------] 30.60% | train_error: 15.9 | train_acc: 0.235 | val_error: 15.7 | val_acc: 0.245
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===========&gt;----------------------------] 30.70% | train_error: 15.7 | train_acc: 0.242 | val_error: 15.7 | val_acc: 0.245
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===========&gt;----------------------------] 30.80% | train_error: 15.7 | train_acc: 0.242 | val_error: 15.7 | val_acc: 0.245
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===========&gt;----------------------------] 30.90% | train_error: 15.7 | train_acc: 0.242 | val_error: 15.8 | val_acc: 0.238
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===========&gt;----------------------------] 31.00% | train_error: 15.7 | train_acc: 0.242 | val_error: 15.7 | val_acc: 0.245
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===========&gt;----------------------------] 31.10% | train_error: 15.7 | train_acc: 0.242 | val_error: 15.7 | val_acc: 0.245
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===========&gt;----------------------------] 31.20% | train_error: 15.7 | train_acc: 0.242 | val_error: 15.7 | val_acc: 0.245
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===========&gt;----------------------------] 31.30% | train_error: 15.7 | train_acc: 0.242 | val_error: 15.7 | val_acc: 0.245
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===========&gt;----------------------------] 31.40% | train_error: 15.7 | train_acc: 0.242 | val_error: 15.7 | val_acc: 0.245
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===========&gt;----------------------------] 31.50% | train_error: 15.7 | train_acc: 0.242 | val_error: 15.7 | val_acc: 0.245
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===========&gt;----------------------------] 31.60% | train_error: 15.7 | train_acc: 0.244 | val_error: 15.7 | val_acc: 0.245
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===========&gt;----------------------------] 31.70% | train_error: 15.5 | train_acc: 0.251 | val_error: 15.7 | val_acc: 0.245
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===========&gt;----------------------------] 31.80% | train_error: 15.5 | train_acc: 0.251 | val_error: 15.7 | val_acc: 0.245
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===========&gt;----------------------------] 31.90% | train_error: 15.6 | train_acc: 0.246 | val_error: 15.7 | val_acc: 0.245
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===========&gt;----------------------------] 32.00% | train_error: 15.6 | train_acc: 0.246 | val_error: 15.7 | val_acc: 0.245
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===========&gt;----------------------------] 32.10% | train_error: 15.6 | train_acc: 0.246 | val_error: 15.7 | val_acc: 0.245
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===========&gt;----------------------------] 32.20% | train_error: 15.6 | train_acc: 0.246 | val_error: 15.7 | val_acc: 0.245
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===========&gt;----------------------------] 32.30% | train_error: 15.6 | train_acc: 0.246 | val_error: 15.7 | val_acc: 0.245
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===========&gt;----------------------------] 32.40% | train_error: 15.6 | train_acc: 0.246 | val_error: 15.7 | val_acc: 0.245
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [============&gt;---------------------------] 32.50% | train_error: 15.6 | train_acc: 0.246 | val_error: 15.7 | val_acc: 0.245
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [============&gt;---------------------------] 32.60% | train_error: 15.8 | train_acc: 0.237 | val_error: 15.7 | val_acc: 0.245
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [============&gt;---------------------------] 32.70% | train_error: 15.8 | train_acc: 0.237 | val_error: 15.7 | val_acc: 0.245
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [============&gt;---------------------------] 32.80% | train_error: 15.8 | train_acc: 0.237 | val_error: 15.7 | val_acc: 0.245
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [============&gt;---------------------------] 32.90% | train_error: 15.8 | train_acc: 0.237 | val_error: 15.7 | val_acc: 0.245
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [============&gt;---------------------------] 33.00% | train_error: 15.9 | train_acc: 0.235 | val_error: 15.7 | val_acc: 0.245
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [============&gt;---------------------------] 33.10% | train_error: 15.9 | train_acc: 0.235 | val_error: 15.7 | val_acc: 0.245
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [============&gt;---------------------------] 33.20% | train_error: 15.9 | train_acc: 0.235 | val_error: 15.7 | val_acc: 0.245
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [============&gt;---------------------------] 33.30% | train_error: 15.9 | train_acc: 0.235 | val_error: 15.7 | val_acc: 0.245
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [============&gt;---------------------------] 33.40% | train_error: 15.9 | train_acc: 0.235 | val_error: 15.7 | val_acc: 0.245
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [============&gt;---------------------------] 33.50% | train_error: 15.9 | train_acc: 0.235 | val_error: 15.7 | val_acc: 0.245
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [============&gt;---------------------------] 33.60% | train_error: 15.9 | train_acc: 0.235 | val_error: 15.7 | val_acc: 0.245
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [============&gt;---------------------------] 33.70% | train_error: 15.9 | train_acc: 0.235 | val_error: 15.7 | val_acc: 0.245
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [============&gt;---------------------------] 33.80% | train_error: 15.9 | train_acc: 0.235 | val_error: 15.7 | val_acc: 0.245
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [============&gt;---------------------------] 33.90% | train_error: 15.9 | train_acc: 0.235 | val_error: 15.7 | val_acc: 0.245
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [============&gt;---------------------------] 34.00% | train_error: 15.9 | train_acc: 0.235 | val_error: 15.5 | val_acc: 0.252
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [============&gt;---------------------------] 34.10% | train_error: 15.9 | train_acc: 0.235 | val_error: 15.5 | val_acc: 0.252
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [============&gt;---------------------------] 34.20% | train_error: 15.9 | train_acc: 0.235 | val_error: 15.5 | val_acc: 0.252
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [============&gt;---------------------------] 34.30% | train_error: 15.9 | train_acc: 0.235 | val_error: 15.5 | val_acc: 0.252
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [============&gt;---------------------------] 34.40% | train_error: 15.9 | train_acc: 0.235 | val_error: 15.5 | val_acc: 0.252
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [============&gt;---------------------------] 34.50% | train_error: 15.9 | train_acc: 0.235 | val_error: 15.5 | val_acc: 0.252
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [============&gt;---------------------------] 34.60% | train_error: 15.9 | train_acc: 0.235 | val_error: 15.5 | val_acc: 0.252
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [============&gt;---------------------------] 34.70% | train_error: 15.9 | train_acc: 0.235 | val_error: 15.5 | val_acc: 0.252
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [============&gt;---------------------------] 34.80% | train_error: 15.9 | train_acc: 0.235 | val_error: 15.5 | val_acc: 0.252
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [============&gt;---------------------------] 34.90% | train_error: 15.9 | train_acc: 0.235 | val_error: 15.5 | val_acc: 0.252
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=============&gt;--------------------------] 35.00% | train_error: 15.8 | train_acc: 0.237 | val_error: 15.5 | val_acc: 0.252
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=============&gt;--------------------------] 35.10% | train_error: 15.8 | train_acc: 0.237 | val_error: 15.5 | val_acc: 0.252
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=============&gt;--------------------------] 35.20% | train_error: 15.8 | train_acc: 0.237 | val_error: 15.5 | val_acc: 0.252
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=============&gt;--------------------------] 35.30% | train_error: 15.8 | train_acc: 0.237 | val_error: 15.5 | val_acc: 0.252
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=============&gt;--------------------------] 35.40% | train_error: 15.8 | train_acc: 0.237 | val_error: 15.5 | val_acc: 0.252
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=============&gt;--------------------------] 35.50% | train_error: 15.8 | train_acc: 0.237 | val_error: 15.5 | val_acc: 0.252
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=============&gt;--------------------------] 35.60% | train_error: 15.8 | train_acc: 0.237 | val_error: 15.5 | val_acc: 0.252
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=============&gt;--------------------------] 35.70% | train_error: 15.8 | train_acc: 0.237 | val_error: 15.5 | val_acc: 0.252
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=============&gt;--------------------------] 35.80% | train_error: 15.8 | train_acc: 0.237 | val_error: 15.5 | val_acc: 0.252
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=============&gt;--------------------------] 35.90% | train_error: 15.8 | train_acc: 0.237 | val_error: 15.5 | val_acc: 0.252
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=============&gt;--------------------------] 36.00% | train_error: 15.7 | train_acc: 0.244 | val_error: 15.5 | val_acc: 0.252
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=============&gt;--------------------------] 36.10% | train_error: 15.7 | train_acc: 0.244 | val_error: 15.5 | val_acc: 0.252
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=============&gt;--------------------------] 36.20% | train_error: 15.7 | train_acc: 0.244 | val_error: 15.5 | val_acc: 0.252
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=============&gt;--------------------------] 36.30% | train_error: 15.7 | train_acc: 0.244 | val_error: 15.4 | val_acc: 0.259
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=============&gt;--------------------------] 36.40% | train_error: 15.7 | train_acc: 0.244 | val_error: 15.4 | val_acc: 0.259
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=============&gt;--------------------------] 36.50% | train_error: 15.7 | train_acc: 0.244 | val_error: 15.4 | val_acc: 0.259
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=============&gt;--------------------------] 36.60% | train_error: 15.7 | train_acc: 0.244 | val_error: 15.4 | val_acc: 0.259
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=============&gt;--------------------------] 36.70% | train_error: 15.7 | train_acc: 0.244 | val_error: 15.4 | val_acc: 0.259
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=============&gt;--------------------------] 36.80% | train_error: 15.7 | train_acc: 0.244 | val_error: 15.4 | val_acc: 0.259
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=============&gt;--------------------------] 36.90% | train_error: 15.7 | train_acc: 0.244 | val_error: 15.4 | val_acc: 0.259
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=============&gt;--------------------------] 37.00% | train_error: 15.7 | train_acc: 0.244 | val_error: 15.4 | val_acc: 0.259
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=============&gt;--------------------------] 37.10% | train_error: 15.7 | train_acc: 0.244 | val_error: 15.4 | val_acc: 0.259
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=============&gt;--------------------------] 37.20% | train_error: 15.7 | train_acc: 0.244 | val_error: 15.4 | val_acc: 0.259
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=============&gt;--------------------------] 37.30% | train_error: 15.7 | train_acc: 0.244 | val_error: 15.4 | val_acc: 0.259
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=============&gt;--------------------------] 37.40% | train_error: 15.6 | train_acc: 0.249 | val_error: 15.4 | val_acc: 0.259
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==============&gt;-------------------------] 37.50% | train_error: 15.6 | train_acc: 0.249 | val_error: 15.2 | val_acc: 0.266
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==============&gt;-------------------------] 37.60% | train_error: 15.6 | train_acc: 0.249 | val_error: 15.2 | val_acc: 0.266
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==============&gt;-------------------------] 37.70% | train_error: 15.6 | train_acc: 0.249 | val_error: 15.2 | val_acc: 0.266
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==============&gt;-------------------------] 37.80% | train_error: 15.6 | train_acc: 0.249 | val_error: 15.2 | val_acc: 0.266
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==============&gt;-------------------------] 37.90% | train_error: 15.6 | train_acc: 0.249 | val_error: 15.2 | val_acc: 0.266
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==============&gt;-------------------------] 38.00% | train_error: 15.6 | train_acc: 0.249 | val_error: 15.2 | val_acc: 0.266
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==============&gt;-------------------------] 38.10% | train_error: 15.6 | train_acc: 0.249 | val_error: 15.2 | val_acc: 0.266
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==============&gt;-------------------------] 38.20% | train_error: 15.6 | train_acc: 0.249 | val_error: 15.2 | val_acc: 0.266
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==============&gt;-------------------------] 38.30% | train_error: 15.6 | train_acc: 0.249 | val_error: 15.2 | val_acc: 0.266
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==============&gt;-------------------------] 38.40% | train_error: 15.6 | train_acc: 0.249 | val_error: 15.2 | val_acc: 0.266
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==============&gt;-------------------------] 38.50% | train_error: 15.6 | train_acc: 0.249 | val_error: 15.2 | val_acc: 0.266
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==============&gt;-------------------------] 38.60% | train_error: 15.4 | train_acc: 0.256 | val_error: 15.2 | val_acc: 0.266
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==============&gt;-------------------------] 38.70% | train_error: 15.3 | train_acc: 0.261 | val_error: 15.2 | val_acc: 0.266
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==============&gt;-------------------------] 38.80% | train_error: 15.3 | train_acc: 0.261 | val_error: 15.2 | val_acc: 0.266
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==============&gt;-------------------------] 38.90% | train_error: 15.4 | train_acc: 0.258 | val_error: 15.2 | val_acc: 0.266
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==============&gt;-------------------------] 39.00% | train_error: 15.4 | train_acc: 0.258 | val_error: 15.2 | val_acc: 0.266
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==============&gt;-------------------------] 39.10% | train_error: 15.4 | train_acc: 0.258 | val_error: 15.2 | val_acc: 0.266
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==============&gt;-------------------------] 39.20% | train_error: 15.4 | train_acc: 0.258 | val_error: 15.2 | val_acc: 0.266
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==============&gt;-------------------------] 39.30% | train_error: 15.2 | train_acc: 0.268 | val_error: 15.2 | val_acc: 0.266
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==============&gt;-------------------------] 39.40% | train_error: 15.2 | train_acc: 0.268 | val_error: 15.2 | val_acc: 0.266
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==============&gt;-------------------------] 39.50% | train_error: 15.2 | train_acc: 0.268 | val_error: 15.2 | val_acc: 0.266
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==============&gt;-------------------------] 39.60% | train_error: 15.2 | train_acc: 0.268 | val_error: 15.2 | val_acc: 0.266
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==============&gt;-------------------------] 39.70% | train_error: 15.2 | train_acc: 0.268 | val_error: 15.1 | val_acc: 0.273
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==============&gt;-------------------------] 39.80% | train_error: 15.2 | train_acc: 0.268 | val_error: 15.1 | val_acc: 0.273
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==============&gt;-------------------------] 39.90% | train_error: 15.1 | train_acc: 0.272 | val_error: 15.1 | val_acc: 0.273
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===============&gt;------------------------] 40.00% | train_error: 15.1 | train_acc: 0.272 | val_error: 15.1 | val_acc: 0.273
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===============&gt;------------------------] 40.10% | train_error: 15.1 | train_acc: 0.272 | val_error: 15.1 | val_acc: 0.273
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===============&gt;------------------------] 40.20% | train_error: 15.1 | train_acc: 0.272 | val_error: 15.1 | val_acc: 0.273
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===============&gt;------------------------] 40.30% | train_error: 15.1 | train_acc: 0.272 | val_error: 15.1 | val_acc: 0.273
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===============&gt;------------------------] 40.40% | train_error: 15.1 | train_acc: 0.272 | val_error: 15.1 | val_acc: 0.273
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===============&gt;------------------------] 40.50% | train_error: 15.0 | train_acc: 0.277 | val_error: 15.1 | val_acc: 0.273
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===============&gt;------------------------] 40.60% | train_error: 15.0 | train_acc: 0.277 | val_error: 15.1 | val_acc: 0.273
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===============&gt;------------------------] 40.70% | train_error: 15.0 | train_acc: 0.277 | val_error: 15.1 | val_acc: 0.273
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===============&gt;------------------------] 40.80% | train_error: 15.0 | train_acc: 0.277 | val_error: 15.1 | val_acc: 0.273
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===============&gt;------------------------] 40.90% | train_error: 15.0 | train_acc: 0.277 | val_error: 14.9 | val_acc: 0.280
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===============&gt;------------------------] 41.00% | train_error: 15.0 | train_acc: 0.277 | val_error: 14.9 | val_acc: 0.280
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===============&gt;------------------------] 41.10% | train_error: 14.9 | train_acc: 0.279 | val_error: 14.9 | val_acc: 0.280
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===============&gt;------------------------] 41.20% | train_error: 14.9 | train_acc: 0.279 | val_error: 14.6 | val_acc: 0.294
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===============&gt;------------------------] 41.30% | train_error: 14.9 | train_acc: 0.279 | val_error: 14.5 | val_acc: 0.301
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===============&gt;------------------------] 41.40% | train_error: 14.9 | train_acc: 0.279 | val_error: 14.5 | val_acc: 0.301
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===============&gt;------------------------] 41.50% | train_error: 14.9 | train_acc: 0.279 | val_error: 14.3 | val_acc: 0.308
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===============&gt;------------------------] 41.60% | train_error: 15.0 | train_acc: 0.277 | val_error: 14.3 | val_acc: 0.308
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===============&gt;------------------------] 41.70% | train_error: 15.0 | train_acc: 0.277 | val_error: 14.3 | val_acc: 0.308
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===============&gt;------------------------] 41.80% | train_error: 14.9 | train_acc: 0.279 | val_error: 14.3 | val_acc: 0.308
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===============&gt;------------------------] 41.90% | train_error: 14.9 | train_acc: 0.279 | val_error: 14.2 | val_acc: 0.315
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===============&gt;------------------------] 42.00% | train_error: 14.9 | train_acc: 0.279 | val_error: 14.2 | val_acc: 0.315
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===============&gt;------------------------] 42.10% | train_error: 14.9 | train_acc: 0.279 | val_error: 14.2 | val_acc: 0.315
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===============&gt;------------------------] 42.20% | train_error: 14.9 | train_acc: 0.279 | val_error: 14.2 | val_acc: 0.315
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===============&gt;------------------------] 42.30% | train_error: 14.9 | train_acc: 0.279 | val_error: 14.2 | val_acc: 0.315
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===============&gt;------------------------] 42.40% | train_error: 14.9 | train_acc: 0.279 | val_error: 14.2 | val_acc: 0.315
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [================&gt;-----------------------] 42.50% | train_error: 14.9 | train_acc: 0.279 | val_error: 14.2 | val_acc: 0.315
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [================&gt;-----------------------] 42.60% | train_error: 14.9 | train_acc: 0.279 | val_error: 14.2 | val_acc: 0.315
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [================&gt;-----------------------] 42.70% | train_error: 14.9 | train_acc: 0.279 | val_error: 14.2 | val_acc: 0.315
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [================&gt;-----------------------] 42.80% | train_error: 14.9 | train_acc: 0.279 | val_error: 14.2 | val_acc: 0.315
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [================&gt;-----------------------] 42.90% | train_error: 14.9 | train_acc: 0.279 | val_error: 14.2 | val_acc: 0.315
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [================&gt;-----------------------] 43.00% | train_error: 14.9 | train_acc: 0.282 | val_error: 14.1 | val_acc: 0.322
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [================&gt;-----------------------] 43.10% | train_error: 14.9 | train_acc: 0.282 | val_error: 14.1 | val_acc: 0.322
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [================&gt;-----------------------] 43.20% | train_error: 14.9 | train_acc: 0.282 | val_error: 14.2 | val_acc: 0.315
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [================&gt;-----------------------] 43.30% | train_error: 14.9 | train_acc: 0.282 | val_error: 14.2 | val_acc: 0.315
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [================&gt;-----------------------] 43.40% | train_error: 14.9 | train_acc: 0.282 | val_error: 14.2 | val_acc: 0.315
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [================&gt;-----------------------] 43.50% | train_error: 14.9 | train_acc: 0.282 | val_error: 14.2 | val_acc: 0.315
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [================&gt;-----------------------] 43.60% | train_error: 14.9 | train_acc: 0.282 | val_error: 14.2 | val_acc: 0.315
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [================&gt;-----------------------] 43.70% | train_error: 14.9 | train_acc: 0.282 | val_error: 14.2 | val_acc: 0.315
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [================&gt;-----------------------] 43.80% | train_error: 14.9 | train_acc: 0.282 | val_error: 14.2 | val_acc: 0.315
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [================&gt;-----------------------] 43.90% | train_error: 14.9 | train_acc: 0.282 | val_error: 14.2 | val_acc: 0.315
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [================&gt;-----------------------] 44.00% | train_error: 14.8 | train_acc: 0.284 | val_error: 14.1 | val_acc: 0.322
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [================&gt;-----------------------] 44.10% | train_error: 14.8 | train_acc: 0.284 | val_error: 14.1 | val_acc: 0.322
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [================&gt;-----------------------] 44.20% | train_error: 14.7 | train_acc: 0.289 | val_error: 14.1 | val_acc: 0.322
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [================&gt;-----------------------] 44.30% | train_error: 14.7 | train_acc: 0.289 | val_error: 14.2 | val_acc: 0.315
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [================&gt;-----------------------] 44.40% | train_error: 14.7 | train_acc: 0.289 | val_error: 14.2 | val_acc: 0.315
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [================&gt;-----------------------] 44.50% | train_error: 14.7 | train_acc: 0.289 | val_error: 14.2 | val_acc: 0.315
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [================&gt;-----------------------] 44.60% | train_error: 14.7 | train_acc: 0.289 | val_error: 14.2 | val_acc: 0.315
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [================&gt;-----------------------] 44.70% | train_error: 14.7 | train_acc: 0.289 | val_error: 14.2 | val_acc: 0.315
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [================&gt;-----------------------] 44.80% | train_error: 14.7 | train_acc: 0.289 | val_error: 14.2 | val_acc: 0.315
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [================&gt;-----------------------] 44.90% | train_error: 14.7 | train_acc: 0.289 | val_error: 14.2 | val_acc: 0.315
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=================&gt;----------------------] 45.00% | train_error: 14.7 | train_acc: 0.289 | val_error: 14.2 | val_acc: 0.315
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=================&gt;----------------------] 45.10% | train_error: 14.7 | train_acc: 0.291 | val_error: 14.1 | val_acc: 0.322
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=================&gt;----------------------] 45.20% | train_error: 14.6 | train_acc: 0.296 | val_error: 14.1 | val_acc: 0.322
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=================&gt;----------------------] 45.30% | train_error: 14.6 | train_acc: 0.296 | val_error: 14.1 | val_acc: 0.322
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=================&gt;----------------------] 45.40% | train_error: 14.5 | train_acc: 0.300 | val_error: 14.1 | val_acc: 0.322
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=================&gt;----------------------] 45.50% | train_error: 14.5 | train_acc: 0.300 | val_error: 14.1 | val_acc: 0.322
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=================&gt;----------------------] 45.60% | train_error: 14.4 | train_acc: 0.308 | val_error: 14.1 | val_acc: 0.322
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=================&gt;----------------------] 45.70% | train_error: 14.4 | train_acc: 0.308 | val_error: 14.1 | val_acc: 0.322
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=================&gt;----------------------] 45.80% | train_error: 14.4 | train_acc: 0.308 | val_error: 13.9 | val_acc: 0.329
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=================&gt;----------------------] 45.90% | train_error: 14.4 | train_acc: 0.308 | val_error: 13.9 | val_acc: 0.329
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=================&gt;----------------------] 46.00% | train_error: 14.3 | train_acc: 0.310 | val_error: 13.9 | val_acc: 0.329
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=================&gt;----------------------] 46.10% | train_error: 14.3 | train_acc: 0.310 | val_error: 13.9 | val_acc: 0.329
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=================&gt;----------------------] 46.20% | train_error: 14.3 | train_acc: 0.312 | val_error: 13.9 | val_acc: 0.329
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=================&gt;----------------------] 46.30% | train_error: 14.2 | train_acc: 0.315 | val_error: 13.9 | val_acc: 0.329
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=================&gt;----------------------] 46.40% | train_error: 14.2 | train_acc: 0.315 | val_error: 13.9 | val_acc: 0.329
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=================&gt;----------------------] 46.50% | train_error: 14.2 | train_acc: 0.315 | val_error: 13.9 | val_acc: 0.329
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=================&gt;----------------------] 46.60% | train_error: 14.2 | train_acc: 0.315 | val_error: 13.9 | val_acc: 0.329
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=================&gt;----------------------] 46.70% | train_error: 14.2 | train_acc: 0.315 | val_error: 13.9 | val_acc: 0.329
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=================&gt;----------------------] 46.80% | train_error: 14.1 | train_acc: 0.319 | val_error: 13.8 | val_acc: 0.336
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=================&gt;----------------------] 46.90% | train_error: 14.1 | train_acc: 0.319 | val_error: 13.8 | val_acc: 0.336
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=================&gt;----------------------] 47.00% | train_error: 14.1 | train_acc: 0.322 | val_error: 13.8 | val_acc: 0.336
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=================&gt;----------------------] 47.10% | train_error: 14.1 | train_acc: 0.322 | val_error: 13.8 | val_acc: 0.336
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=================&gt;----------------------] 47.20% | train_error: 14.1 | train_acc: 0.322 | val_error: 13.8 | val_acc: 0.336
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=================&gt;----------------------] 47.30% | train_error: 14.0 | train_acc: 0.324 | val_error: 13.8 | val_acc: 0.336
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=================&gt;----------------------] 47.40% | train_error: 14.0 | train_acc: 0.324 | val_error: 13.8 | val_acc: 0.336
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==================&gt;---------------------] 47.50% | train_error: 14.0 | train_acc: 0.324 | val_error: 13.8 | val_acc: 0.336
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==================&gt;---------------------] 47.60% | train_error: 14.0 | train_acc: 0.326 | val_error: 13.6 | val_acc: 0.343
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==================&gt;---------------------] 47.70% | train_error: 13.9 | train_acc: 0.329 | val_error: 13.5 | val_acc: 0.350
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==================&gt;---------------------] 47.80% | train_error: 13.9 | train_acc: 0.329 | val_error: 13.5 | val_acc: 0.350
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==================&gt;---------------------] 47.90% | train_error: 13.7 | train_acc: 0.338 | val_error: 13.5 | val_acc: 0.350
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==================&gt;---------------------] 48.00% | train_error: 13.6 | train_acc: 0.343 | val_error: 13.3 | val_acc: 0.357
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==================&gt;---------------------] 48.10% | train_error: 13.6 | train_acc: 0.343 | val_error: 13.3 | val_acc: 0.357
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==================&gt;---------------------] 48.20% | train_error: 13.6 | train_acc: 0.343 | val_error: 13.3 | val_acc: 0.357
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==================&gt;---------------------] 48.30% | train_error: 13.6 | train_acc: 0.343 | val_error: 13.3 | val_acc: 0.357
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==================&gt;---------------------] 48.40% | train_error: 13.6 | train_acc: 0.343 | val_error: 13.3 | val_acc: 0.357
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==================&gt;---------------------] 48.50% | train_error: 13.5 | train_acc: 0.347 | val_error: 13.3 | val_acc: 0.357
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==================&gt;---------------------] 48.60% | train_error: 13.5 | train_acc: 0.347 | val_error: 13.3 | val_acc: 0.357
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==================&gt;---------------------] 48.70% | train_error: 13.5 | train_acc: 0.347 | val_error: 13.3 | val_acc: 0.357
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==================&gt;---------------------] 48.80% | train_error: 13.5 | train_acc: 0.347 | val_error: 13.2 | val_acc: 0.364
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==================&gt;---------------------] 48.90% | train_error: 13.4 | train_acc: 0.352 | val_error: 12.9 | val_acc: 0.378
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==================&gt;---------------------] 49.00% | train_error: 13.4 | train_acc: 0.352 | val_error: 12.9 | val_acc: 0.378
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==================&gt;---------------------] 49.10% | train_error: 13.4 | train_acc: 0.352 | val_error: 12.9 | val_acc: 0.378
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==================&gt;---------------------] 49.20% | train_error: 13.4 | train_acc: 0.352 | val_error: 12.9 | val_acc: 0.378
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==================&gt;---------------------] 49.30% | train_error: 13.2 | train_acc: 0.362 | val_error: 12.9 | val_acc: 0.378
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==================&gt;---------------------] 49.40% | train_error: 13.2 | train_acc: 0.362 | val_error: 12.9 | val_acc: 0.378
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==================&gt;---------------------] 49.50% | train_error: 13.2 | train_acc: 0.362 | val_error: 12.9 | val_acc: 0.378
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==================&gt;---------------------] 49.60% | train_error: 13.2 | train_acc: 0.362 | val_error: 12.9 | val_acc: 0.378
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==================&gt;---------------------] 49.70% | train_error: 13.2 | train_acc: 0.362 | val_error: 12.9 | val_acc: 0.378
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==================&gt;---------------------] 49.80% | train_error: 13.2 | train_acc: 0.362 | val_error: 12.9 | val_acc: 0.378
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==================&gt;---------------------] 49.90% | train_error: 13.2 | train_acc: 0.362 | val_error: 12.9 | val_acc: 0.378
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===================&gt;--------------------] 50.00% | train_error: 13.2 | train_acc: 0.364 | val_error: 12.6 | val_acc: 0.392
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===================&gt;--------------------] 50.10% | train_error: 13.2 | train_acc: 0.364 | val_error: 12.5 | val_acc: 0.399
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===================&gt;--------------------] 50.20% | train_error: 13.0 | train_acc: 0.373 | val_error: 12.5 | val_acc: 0.399
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===================&gt;--------------------] 50.30% | train_error: 13.0 | train_acc: 0.373 | val_error: 12.5 | val_acc: 0.399
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===================&gt;--------------------] 50.40% | train_error: 13.0 | train_acc: 0.371 | val_error: 12.5 | val_acc: 0.399
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===================&gt;--------------------] 50.50% | train_error: 13.0 | train_acc: 0.371 | val_error: 12.5 | val_acc: 0.399
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===================&gt;--------------------] 50.60% | train_error: 13.0 | train_acc: 0.371 | val_error: 12.5 | val_acc: 0.399
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===================&gt;--------------------] 50.70% | train_error: 12.8 | train_acc: 0.380 | val_error: 12.5 | val_acc: 0.399
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===================&gt;--------------------] 50.80% | train_error: 12.8 | train_acc: 0.383 | val_error: 12.5 | val_acc: 0.399
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===================&gt;--------------------] 50.90% | train_error: 12.8 | train_acc: 0.383 | val_error: 12.5 | val_acc: 0.399
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===================&gt;--------------------] 51.00% | train_error: 12.8 | train_acc: 0.383 | val_error: 12.6 | val_acc: 0.392
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===================&gt;--------------------] 51.10% | train_error: 12.7 | train_acc: 0.385 | val_error: 12.6 | val_acc: 0.392
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===================&gt;--------------------] 51.20% | train_error: 12.7 | train_acc: 0.385 | val_error: 12.6 | val_acc: 0.392
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===================&gt;--------------------] 51.30% | train_error: 12.7 | train_acc: 0.385 | val_error: 12.6 | val_acc: 0.392
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===================&gt;--------------------] 51.40% | train_error: 12.7 | train_acc: 0.385 | val_error: 12.6 | val_acc: 0.392
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===================&gt;--------------------] 51.50% | train_error: 12.6 | train_acc: 0.390 | val_error: 12.6 | val_acc: 0.392
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===================&gt;--------------------] 51.60% | train_error: 12.6 | train_acc: 0.390 | val_error: 12.6 | val_acc: 0.392
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===================&gt;--------------------] 51.70% | train_error: 12.6 | train_acc: 0.390 | val_error: 12.5 | val_acc: 0.399
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===================&gt;--------------------] 51.80% | train_error: 12.8 | train_acc: 0.380 | val_error: 12.6 | val_acc: 0.392
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===================&gt;--------------------] 51.90% | train_error: 12.8 | train_acc: 0.380 | val_error: 12.6 | val_acc: 0.392
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===================&gt;--------------------] 52.00% | train_error: 12.8 | train_acc: 0.380 | val_error: 12.6 | val_acc: 0.392
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===================&gt;--------------------] 52.10% | train_error: 12.8 | train_acc: 0.380 | val_error: 12.6 | val_acc: 0.392
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===================&gt;--------------------] 52.20% | train_error: 12.8 | train_acc: 0.383 | val_error: 12.5 | val_acc: 0.399
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===================&gt;--------------------] 52.30% | train_error: 12.8 | train_acc: 0.383 | val_error: 12.5 | val_acc: 0.399
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===================&gt;--------------------] 52.40% | train_error: 12.8 | train_acc: 0.383 | val_error: 12.5 | val_acc: 0.399
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [====================&gt;-------------------] 52.50% | train_error: 12.8 | train_acc: 0.383 | val_error: 12.5 | val_acc: 0.399
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [====================&gt;-------------------] 52.60% | train_error: 12.6 | train_acc: 0.390 | val_error: 12.5 | val_acc: 0.399
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [====================&gt;-------------------] 52.70% | train_error: 12.6 | train_acc: 0.390 | val_error: 12.5 | val_acc: 0.399
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [====================&gt;-------------------] 52.80% | train_error: 12.6 | train_acc: 0.390 | val_error: 12.5 | val_acc: 0.399
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [====================&gt;-------------------] 52.90% | train_error: 12.6 | train_acc: 0.390 | val_error: 12.5 | val_acc: 0.399
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [====================&gt;-------------------] 53.00% | train_error: 12.6 | train_acc: 0.390 | val_error: 12.5 | val_acc: 0.399
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [====================&gt;-------------------] 53.10% | train_error: 12.6 | train_acc: 0.390 | val_error: 12.5 | val_acc: 0.399
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [====================&gt;-------------------] 53.20% | train_error: 12.6 | train_acc: 0.392 | val_error: 12.5 | val_acc: 0.399
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [====================&gt;-------------------] 53.30% | train_error: 12.6 | train_acc: 0.392 | val_error: 12.5 | val_acc: 0.399
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [====================&gt;-------------------] 53.40% | train_error: 12.6 | train_acc: 0.394 | val_error: 12.5 | val_acc: 0.399
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [====================&gt;-------------------] 53.50% | train_error: 12.6 | train_acc: 0.394 | val_error: 12.5 | val_acc: 0.399
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [====================&gt;-------------------] 53.60% | train_error: 12.6 | train_acc: 0.394 | val_error: 12.5 | val_acc: 0.399
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [====================&gt;-------------------] 53.70% | train_error: 12.6 | train_acc: 0.394 | val_error: 12.5 | val_acc: 0.399
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [====================&gt;-------------------] 53.80% | train_error: 12.6 | train_acc: 0.394 | val_error: 12.5 | val_acc: 0.399
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [====================&gt;-------------------] 53.90% | train_error: 12.6 | train_acc: 0.394 | val_error: 12.5 | val_acc: 0.399
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [====================&gt;-------------------] 54.00% | train_error: 12.6 | train_acc: 0.394 | val_error: 12.5 | val_acc: 0.399
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [====================&gt;-------------------] 54.10% | train_error: 12.5 | train_acc: 0.397 | val_error: 12.3 | val_acc: 0.406
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [====================&gt;-------------------] 54.20% | train_error: 12.5 | train_acc: 0.397 | val_error: 12.3 | val_acc: 0.406
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [====================&gt;-------------------] 54.30% | train_error: 12.5 | train_acc: 0.397 | val_error: 12.3 | val_acc: 0.406
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [====================&gt;-------------------] 54.40% | train_error: 12.5 | train_acc: 0.397 | val_error: 12.3 | val_acc: 0.406
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [====================&gt;-------------------] 54.50% | train_error: 12.5 | train_acc: 0.397 | val_error: 12.3 | val_acc: 0.406
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [====================&gt;-------------------] 54.60% | train_error: 12.4 | train_acc: 0.401 | val_error: 12.3 | val_acc: 0.406
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [====================&gt;-------------------] 54.70% | train_error: 12.4 | train_acc: 0.404 | val_error: 12.3 | val_acc: 0.406
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [====================&gt;-------------------] 54.80% | train_error: 12.2 | train_acc: 0.411 | val_error: 12.3 | val_acc: 0.406
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [====================&gt;-------------------] 54.90% | train_error: 12.1 | train_acc: 0.415 | val_error: 12.2 | val_acc: 0.413
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=====================&gt;------------------] 55.00% | train_error: 12.1 | train_acc: 0.415 | val_error: 12.0 | val_acc: 0.420
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=====================&gt;------------------] 55.10% | train_error: 12.1 | train_acc: 0.415 | val_error: 12.0 | val_acc: 0.420
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=====================&gt;------------------] 55.20% | train_error: 12.1 | train_acc: 0.415 | val_error: 12.0 | val_acc: 0.420
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=====================&gt;------------------] 55.30% | train_error: 12.1 | train_acc: 0.418 | val_error: 12.2 | val_acc: 0.413
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=====================&gt;------------------] 55.40% | train_error: 11.7 | train_acc: 0.437 | val_error: 12.0 | val_acc: 0.420
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=====================&gt;------------------] 55.50% | train_error: 11.7 | train_acc: 0.434 | val_error: 12.0 | val_acc: 0.420
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=====================&gt;------------------] 55.60% | train_error: 11.7 | train_acc: 0.434 | val_error: 12.0 | val_acc: 0.420
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=====================&gt;------------------] 55.70% | train_error: 11.7 | train_acc: 0.437 | val_error: 12.0 | val_acc: 0.420
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=====================&gt;------------------] 55.80% | train_error: 11.7 | train_acc: 0.437 | val_error: 11.9 | val_acc: 0.427
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=====================&gt;------------------] 55.90% | train_error: 11.7 | train_acc: 0.437 | val_error: 11.9 | val_acc: 0.427
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=====================&gt;------------------] 56.00% | train_error: 11.7 | train_acc: 0.437 | val_error: 11.9 | val_acc: 0.427
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=====================&gt;------------------] 56.10% | train_error: 11.7 | train_acc: 0.437 | val_error: 11.7 | val_acc: 0.434
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=====================&gt;------------------] 56.20% | train_error: 11.7 | train_acc: 0.437 | val_error: 11.7 | val_acc: 0.434
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=====================&gt;------------------] 56.30% | train_error: 11.7 | train_acc: 0.437 | val_error: 11.7 | val_acc: 0.434
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=====================&gt;------------------] 56.40% | train_error: 11.5 | train_acc: 0.444 | val_error: 11.7 | val_acc: 0.434
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=====================&gt;------------------] 56.50% | train_error: 11.5 | train_acc: 0.444 | val_error: 11.7 | val_acc: 0.434
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=====================&gt;------------------] 56.60% | train_error: 11.6 | train_acc: 0.441 | val_error: 11.7 | val_acc: 0.434
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=====================&gt;------------------] 56.70% | train_error: 11.3 | train_acc: 0.453 | val_error: 11.7 | val_acc: 0.434
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=====================&gt;------------------] 56.80% | train_error: 11.3 | train_acc: 0.453 | val_error: 11.6 | val_acc: 0.441
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=====================&gt;------------------] 56.90% | train_error: 11.3 | train_acc: 0.453 | val_error: 11.6 | val_acc: 0.441
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=====================&gt;------------------] 57.00% | train_error: 11.3 | train_acc: 0.453 | val_error: 11.6 | val_acc: 0.441
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=====================&gt;------------------] 57.10% | train_error: 11.3 | train_acc: 0.453 | val_error: 11.6 | val_acc: 0.441
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=====================&gt;------------------] 57.20% | train_error: 11.3 | train_acc: 0.453 | val_error: 11.6 | val_acc: 0.441
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=====================&gt;------------------] 57.30% | train_error: 11.3 | train_acc: 0.453 | val_error: 11.6 | val_acc: 0.441
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=====================&gt;------------------] 57.40% | train_error: 11.3 | train_acc: 0.453 | val_error: 11.6 | val_acc: 0.441
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [======================&gt;-----------------] 57.50% | train_error: 11.4 | train_acc: 0.451 | val_error: 11.6 | val_acc: 0.441
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [======================&gt;-----------------] 57.60% | train_error: 11.4 | train_acc: 0.451 | val_error: 11.4 | val_acc: 0.448
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [======================&gt;-----------------] 57.70% | train_error: 11.4 | train_acc: 0.451 | val_error: 11.4 | val_acc: 0.448
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [======================&gt;-----------------] 57.80% | train_error: 11.4 | train_acc: 0.451 | val_error: 11.4 | val_acc: 0.448
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [======================&gt;-----------------] 57.90% | train_error: 11.3 | train_acc: 0.455 | val_error: 11.4 | val_acc: 0.448
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [======================&gt;-----------------] 58.00% | train_error: 11.2 | train_acc: 0.460 | val_error: 11.4 | val_acc: 0.448
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [======================&gt;-----------------] 58.10% | train_error: 10.9 | train_acc: 0.472 | val_error: 11.4 | val_acc: 0.448
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [======================&gt;-----------------] 58.20% | train_error: 10.9 | train_acc: 0.472 | val_error: 11.4 | val_acc: 0.448
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [======================&gt;-----------------] 58.30% | train_error: 10.9 | train_acc: 0.472 | val_error: 11.4 | val_acc: 0.448
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [======================&gt;-----------------] 58.40% | train_error: 10.9 | train_acc: 0.472 | val_error: 11.4 | val_acc: 0.448
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [======================&gt;-----------------] 58.50% | train_error: 10.9 | train_acc: 0.472 | val_error: 11.4 | val_acc: 0.448
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [======================&gt;-----------------] 58.60% | train_error: 10.9 | train_acc: 0.472 | val_error: 11.4 | val_acc: 0.448
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [======================&gt;-----------------] 58.70% | train_error: 10.8 | train_acc: 0.477 | val_error: 11.4 | val_acc: 0.448
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [======================&gt;-----------------] 58.80% | train_error: 10.8 | train_acc: 0.477 | val_error: 11.4 | val_acc: 0.448
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [======================&gt;-----------------] 58.90% | train_error: 10.8 | train_acc: 0.477 | val_error: 11.4 | val_acc: 0.448
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [======================&gt;-----------------] 59.00% | train_error: 10.7 | train_acc: 0.484 | val_error: 11.4 | val_acc: 0.448
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [======================&gt;-----------------] 59.10% | train_error: 10.7 | train_acc: 0.484 | val_error: 11.4 | val_acc: 0.448
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [======================&gt;-----------------] 59.20% | train_error: 10.7 | train_acc: 0.486 | val_error: 11.4 | val_acc: 0.448
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [======================&gt;-----------------] 59.30% | train_error: 10.7 | train_acc: 0.486 | val_error: 11.4 | val_acc: 0.448
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [======================&gt;-----------------] 59.40% | train_error: 10.7 | train_acc: 0.486 | val_error: 11.6 | val_acc: 0.441
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [======================&gt;-----------------] 59.50% | train_error: 10.7 | train_acc: 0.486 | val_error: 11.6 | val_acc: 0.441
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [======================&gt;-----------------] 59.60% | train_error: 10.7 | train_acc: 0.486 | val_error: 11.6 | val_acc: 0.441
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [======================&gt;-----------------] 59.70% | train_error: 10.6 | train_acc: 0.488 | val_error: 11.6 | val_acc: 0.441
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [======================&gt;-----------------] 59.80% | train_error: 10.6 | train_acc: 0.488 | val_error: 11.6 | val_acc: 0.441
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [======================&gt;-----------------] 59.90% | train_error: 10.6 | train_acc: 0.491 | val_error: 11.6 | val_acc: 0.441
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=======================&gt;----------------] 60.00% | train_error: 10.5 | train_acc: 0.493 | val_error: 11.6 | val_acc: 0.441
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=======================&gt;----------------] 60.10% | train_error: 10.5 | train_acc: 0.493 | val_error: 11.4 | val_acc: 0.448
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=======================&gt;----------------] 60.20% | train_error: 10.5 | train_acc: 0.493 | val_error: 11.3 | val_acc: 0.455
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=======================&gt;----------------] 60.30% | train_error: 10.5 | train_acc: 0.493 | val_error: 11.0 | val_acc: 0.469
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=======================&gt;----------------] 60.40% | train_error: 10.5 | train_acc: 0.493 | val_error: 11.0 | val_acc: 0.469
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=======================&gt;----------------] 60.50% | train_error: 10.5 | train_acc: 0.493 | val_error: 11.0 | val_acc: 0.469
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=======================&gt;----------------] 60.60% | train_error: 10.3 | train_acc: 0.502 | val_error: 11.0 | val_acc: 0.469
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=======================&gt;----------------] 60.70% | train_error: 10.3 | train_acc: 0.502 | val_error: 10.7 | val_acc: 0.483
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=======================&gt;----------------] 60.80% | train_error: 10.3 | train_acc: 0.502 | val_error: 10.7 | val_acc: 0.483
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=======================&gt;----------------] 60.90% | train_error: 10.3 | train_acc: 0.502 | val_error: 10.7 | val_acc: 0.483
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=======================&gt;----------------] 61.00% | train_error: 10.2 | train_acc: 0.507 | val_error: 10.7 | val_acc: 0.483
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=======================&gt;----------------] 61.10% | train_error: 10.2 | train_acc: 0.507 | val_error: 10.7 | val_acc: 0.483
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=======================&gt;----------------] 61.20% | train_error: 10.2 | train_acc: 0.509 | val_error: 10.6 | val_acc: 0.490
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=======================&gt;----------------] 61.30% | train_error: 10.2 | train_acc: 0.509 | val_error: 10.6 | val_acc: 0.490
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=======================&gt;----------------] 61.40% | train_error: 10.2 | train_acc: 0.509 | val_error: 10.6 | val_acc: 0.490
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=======================&gt;----------------] 61.50% | train_error: 10.2 | train_acc: 0.509 | val_error: 10.6 | val_acc: 0.490
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=======================&gt;----------------] 61.60% | train_error: 10.1 | train_acc: 0.512 | val_error: 10.6 | val_acc: 0.490
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=======================&gt;----------------] 61.70% | train_error: 10.1 | train_acc: 0.512 | val_error: 10.6 | val_acc: 0.490
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=======================&gt;----------------] 61.80% | train_error: 10.1 | train_acc: 0.512 | val_error: 10.6 | val_acc: 0.490
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=======================&gt;----------------] 61.90% | train_error: 10.1 | train_acc: 0.512 | val_error: 10.6 | val_acc: 0.490
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=======================&gt;----------------] 62.00% | train_error: 10.0 | train_acc: 0.516 | val_error: 10.6 | val_acc: 0.490
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=======================&gt;----------------] 62.10% | train_error: 9.97 | train_acc: 0.519 | val_error: 10.6 | val_acc: 0.490
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=======================&gt;----------------] 62.20% | train_error: 9.97 | train_acc: 0.519 | val_error: 10.6 | val_acc: 0.490
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=======================&gt;----------------] 62.30% | train_error: 9.88 | train_acc: 0.523 | val_error: 10.4 | val_acc: 0.497
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=======================&gt;----------------] 62.40% | train_error: 9.88 | train_acc: 0.523 | val_error: 10.4 | val_acc: 0.497
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [========================&gt;---------------] 62.50% | train_error: 9.78 | train_acc: 0.528 | val_error: 10.4 | val_acc: 0.497
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [========================&gt;---------------] 62.60% | train_error: 9.68 | train_acc: 0.533 | val_error: 10.3 | val_acc: 0.503
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [========================&gt;---------------] 62.70% | train_error: 9.68 | train_acc: 0.533 | val_error: 10.00 | val_acc: 0.517
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [========================&gt;---------------] 62.80% | train_error: 9.53 | train_acc: 0.540 | val_error: 10.00 | val_acc: 0.517
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [========================&gt;---------------] 62.90% | train_error: 9.44 | train_acc: 0.545 | val_error: 10.00 | val_acc: 0.517
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [========================&gt;---------------] 63.00% | train_error: 9.39 | train_acc: 0.547 | val_error: 10.00 | val_acc: 0.517
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [========================&gt;---------------] 63.10% | train_error: 9.29 | train_acc: 0.552 | val_error: 10.00 | val_acc: 0.517
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [========================&gt;---------------] 63.20% | train_error: 9.29 | train_acc: 0.552 | val_error: 10.00 | val_acc: 0.517
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [========================&gt;---------------] 63.30% | train_error: 9.24 | train_acc: 0.554 | val_error: 9.85 | val_acc: 0.524
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [========================&gt;---------------] 63.40% | train_error: 9.24 | train_acc: 0.554 | val_error: 9.85 | val_acc: 0.524
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [========================&gt;---------------] 63.50% | train_error: 8.85 | train_acc: 0.573 | val_error: 9.85 | val_acc: 0.524
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [========================&gt;---------------] 63.60% | train_error: 8.85 | train_acc: 0.573 | val_error: 9.85 | val_acc: 0.524
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [========================&gt;---------------] 63.70% | train_error: 8.76 | train_acc: 0.577 | val_error: 9.56 | val_acc: 0.538
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [========================&gt;---------------] 63.80% | train_error: 8.76 | train_acc: 0.577 | val_error: 9.56 | val_acc: 0.538
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [========================&gt;---------------] 63.90% | train_error: 8.76 | train_acc: 0.577 | val_error: 9.56 | val_acc: 0.538
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [========================&gt;---------------] 64.00% | train_error: 8.76 | train_acc: 0.577 | val_error: 9.56 | val_acc: 0.538
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [========================&gt;---------------] 64.10% | train_error: 8.71 | train_acc: 0.580 | val_error: 9.56 | val_acc: 0.538
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [========================&gt;---------------] 64.20% | train_error: 8.71 | train_acc: 0.580 | val_error: 9.42 | val_acc: 0.545
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [========================&gt;---------------] 64.30% | train_error: 8.71 | train_acc: 0.580 | val_error: 9.42 | val_acc: 0.545
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [========================&gt;---------------] 64.40% | train_error: 8.56 | train_acc: 0.587 | val_error: 9.42 | val_acc: 0.545
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [========================&gt;---------------] 64.50% | train_error: 8.56 | train_acc: 0.587 | val_error: 9.42 | val_acc: 0.545
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [========================&gt;---------------] 64.60% | train_error: 8.51 | train_acc: 0.589 | val_error: 9.42 | val_acc: 0.545
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [========================&gt;---------------] 64.70% | train_error: 8.46 | train_acc: 0.592 | val_error: 9.42 | val_acc: 0.545
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [========================&gt;---------------] 64.80% | train_error: 8.32 | train_acc: 0.599 | val_error: 9.42 | val_acc: 0.545
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [========================&gt;---------------] 64.90% | train_error: 8.22 | train_acc: 0.603 | val_error: 9.42 | val_acc: 0.545
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=========================&gt;--------------] 65.00% | train_error: 8.12 | train_acc: 0.608 | val_error: 9.42 | val_acc: 0.545
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=========================&gt;--------------] 65.10% | train_error: 8.08 | train_acc: 0.610 | val_error: 9.27 | val_acc: 0.552
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=========================&gt;--------------] 65.20% | train_error: 8.03 | train_acc: 0.613 | val_error: 9.27 | val_acc: 0.552
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=========================&gt;--------------] 65.30% | train_error: 8.03 | train_acc: 0.613 | val_error: 9.27 | val_acc: 0.552
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=========================&gt;--------------] 65.40% | train_error: 8.03 | train_acc: 0.613 | val_error: 9.13 | val_acc: 0.559
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=========================&gt;--------------] 65.50% | train_error: 8.03 | train_acc: 0.613 | val_error: 9.13 | val_acc: 0.559
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=========================&gt;--------------] 65.60% | train_error: 8.03 | train_acc: 0.613 | val_error: 8.98 | val_acc: 0.566
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=========================&gt;--------------] 65.70% | train_error: 8.03 | train_acc: 0.613 | val_error: 8.98 | val_acc: 0.566
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=========================&gt;--------------] 65.80% | train_error: 8.03 | train_acc: 0.613 | val_error: 8.84 | val_acc: 0.573
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=========================&gt;--------------] 65.90% | train_error: 7.98 | train_acc: 0.615 | val_error: 8.70 | val_acc: 0.580
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=========================&gt;--------------] 66.00% | train_error: 7.98 | train_acc: 0.615 | val_error: 8.55 | val_acc: 0.587
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=========================&gt;--------------] 66.10% | train_error: 7.88 | train_acc: 0.620 | val_error: 8.55 | val_acc: 0.587
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=========================&gt;--------------] 66.20% | train_error: 7.88 | train_acc: 0.620 | val_error: 8.55 | val_acc: 0.587
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=========================&gt;--------------] 66.30% | train_error: 7.88 | train_acc: 0.620 | val_error: 8.70 | val_acc: 0.580
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=========================&gt;--------------] 66.40% | train_error: 7.88 | train_acc: 0.620 | val_error: 8.70 | val_acc: 0.580
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=========================&gt;--------------] 66.50% | train_error: 7.88 | train_acc: 0.620 | val_error: 8.70 | val_acc: 0.580
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=========================&gt;--------------] 66.60% | train_error: 7.73 | train_acc: 0.627 | val_error: 8.70 | val_acc: 0.580
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=========================&gt;--------------] 66.70% | train_error: 7.73 | train_acc: 0.627 | val_error: 8.70 | val_acc: 0.580
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=========================&gt;--------------] 66.80% | train_error: 7.73 | train_acc: 0.627 | val_error: 8.70 | val_acc: 0.580
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=========================&gt;--------------] 66.90% | train_error: 7.73 | train_acc: 0.627 | val_error: 8.70 | val_acc: 0.580
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=========================&gt;--------------] 67.00% | train_error: 7.69 | train_acc: 0.629 | val_error: 8.55 | val_acc: 0.587
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=========================&gt;--------------] 67.10% | train_error: 7.69 | train_acc: 0.629 | val_error: 8.55 | val_acc: 0.587
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=========================&gt;--------------] 67.20% | train_error: 7.69 | train_acc: 0.629 | val_error: 8.41 | val_acc: 0.594
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=========================&gt;--------------] 67.30% | train_error: 7.69 | train_acc: 0.629 | val_error: 8.41 | val_acc: 0.594
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=========================&gt;--------------] 67.40% | train_error: 7.69 | train_acc: 0.629 | val_error: 8.26 | val_acc: 0.601
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==========================&gt;-------------] 67.50% | train_error: 7.69 | train_acc: 0.629 | val_error: 8.12 | val_acc: 0.608
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==========================&gt;-------------] 67.60% | train_error: 7.69 | train_acc: 0.629 | val_error: 8.12 | val_acc: 0.608
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==========================&gt;-------------] 67.70% | train_error: 7.69 | train_acc: 0.629 | val_error: 7.97 | val_acc: 0.615
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==========================&gt;-------------] 67.80% | train_error: 7.54 | train_acc: 0.636 | val_error: 7.97 | val_acc: 0.615
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==========================&gt;-------------] 67.90% | train_error: 7.54 | train_acc: 0.636 | val_error: 7.97 | val_acc: 0.615
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==========================&gt;-------------] 68.00% | train_error: 7.54 | train_acc: 0.636 | val_error: 7.97 | val_acc: 0.615
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==========================&gt;-------------] 68.10% | train_error: 7.44 | train_acc: 0.641 | val_error: 7.97 | val_acc: 0.615
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==========================&gt;-------------] 68.20% | train_error: 7.44 | train_acc: 0.641 | val_error: 7.97 | val_acc: 0.615
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==========================&gt;-------------] 68.30% | train_error: 7.30 | train_acc: 0.648 | val_error: 7.97 | val_acc: 0.615
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==========================&gt;-------------] 68.40% | train_error: 7.30 | train_acc: 0.648 | val_error: 7.97 | val_acc: 0.615
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==========================&gt;-------------] 68.50% | train_error: 7.30 | train_acc: 0.648 | val_error: 7.83 | val_acc: 0.622
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==========================&gt;-------------] 68.60% | train_error: 7.10 | train_acc: 0.657 | val_error: 7.83 | val_acc: 0.622
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==========================&gt;-------------] 68.70% | train_error: 7.01 | train_acc: 0.662 | val_error: 7.83 | val_acc: 0.622
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==========================&gt;-------------] 68.80% | train_error: 6.91 | train_acc: 0.667 | val_error: 7.97 | val_acc: 0.615
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==========================&gt;-------------] 68.90% | train_error: 6.91 | train_acc: 0.667 | val_error: 7.97 | val_acc: 0.615
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==========================&gt;-------------] 69.00% | train_error: 6.81 | train_acc: 0.671 | val_error: 7.83 | val_acc: 0.622
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==========================&gt;-------------] 69.10% | train_error: 6.81 | train_acc: 0.671 | val_error: 7.68 | val_acc: 0.629
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==========================&gt;-------------] 69.20% | train_error: 6.81 | train_acc: 0.671 | val_error: 7.68 | val_acc: 0.629
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==========================&gt;-------------] 69.30% | train_error: 6.81 | train_acc: 0.671 | val_error: 7.68 | val_acc: 0.629
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==========================&gt;-------------] 69.40% | train_error: 6.76 | train_acc: 0.674 | val_error: 7.68 | val_acc: 0.629
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==========================&gt;-------------] 69.50% | train_error: 6.76 | train_acc: 0.674 | val_error: 7.68 | val_acc: 0.629
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==========================&gt;-------------] 69.60% | train_error: 6.71 | train_acc: 0.676 | val_error: 7.68 | val_acc: 0.629
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==========================&gt;-------------] 69.70% | train_error: 6.71 | train_acc: 0.676 | val_error: 7.68 | val_acc: 0.629
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==========================&gt;-------------] 69.80% | train_error: 6.71 | train_acc: 0.676 | val_error: 7.68 | val_acc: 0.629
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==========================&gt;-------------] 69.90% | train_error: 6.71 | train_acc: 0.676 | val_error: 7.68 | val_acc: 0.629
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===========================&gt;------------] 70.00% | train_error: 6.91 | train_acc: 0.667 | val_error: 7.68 | val_acc: 0.629
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===========================&gt;------------] 70.10% | train_error: 6.91 | train_acc: 0.667 | val_error: 7.68 | val_acc: 0.629
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===========================&gt;------------] 70.20% | train_error: 6.86 | train_acc: 0.669 | val_error: 7.68 | val_acc: 0.629
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===========================&gt;------------] 70.30% | train_error: 6.86 | train_acc: 0.669 | val_error: 7.68 | val_acc: 0.629
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===========================&gt;------------] 70.40% | train_error: 6.86 | train_acc: 0.669 | val_error: 7.54 | val_acc: 0.636
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===========================&gt;------------] 70.50% | train_error: 6.71 | train_acc: 0.676 | val_error: 7.54 | val_acc: 0.636
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===========================&gt;------------] 70.60% | train_error: 6.71 | train_acc: 0.676 | val_error: 7.54 | val_acc: 0.636
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===========================&gt;------------] 70.70% | train_error: 6.71 | train_acc: 0.676 | val_error: 7.54 | val_acc: 0.636
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===========================&gt;------------] 70.80% | train_error: 6.71 | train_acc: 0.676 | val_error: 7.54 | val_acc: 0.636
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===========================&gt;------------] 70.90% | train_error: 6.71 | train_acc: 0.676 | val_error: 7.54 | val_acc: 0.636
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===========================&gt;------------] 71.00% | train_error: 6.71 | train_acc: 0.676 | val_error: 7.54 | val_acc: 0.636
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===========================&gt;------------] 71.10% | train_error: 6.66 | train_acc: 0.678 | val_error: 7.54 | val_acc: 0.636
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===========================&gt;------------] 71.20% | train_error: 6.57 | train_acc: 0.683 | val_error: 7.54 | val_acc: 0.636
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===========================&gt;------------] 71.30% | train_error: 6.57 | train_acc: 0.683 | val_error: 7.39 | val_acc: 0.643
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===========================&gt;------------] 71.40% | train_error: 6.57 | train_acc: 0.683 | val_error: 7.39 | val_acc: 0.643
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===========================&gt;------------] 71.50% | train_error: 6.57 | train_acc: 0.683 | val_error: 7.39 | val_acc: 0.643
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===========================&gt;------------] 71.60% | train_error: 6.57 | train_acc: 0.683 | val_error: 7.39 | val_acc: 0.643
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===========================&gt;------------] 71.70% | train_error: 6.57 | train_acc: 0.683 | val_error: 7.25 | val_acc: 0.650
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===========================&gt;------------] 71.80% | train_error: 6.52 | train_acc: 0.685 | val_error: 7.25 | val_acc: 0.650
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===========================&gt;------------] 71.90% | train_error: 6.52 | train_acc: 0.685 | val_error: 7.25 | val_acc: 0.650
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===========================&gt;------------] 72.00% | train_error: 6.52 | train_acc: 0.685 | val_error: 7.25 | val_acc: 0.650
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===========================&gt;------------] 72.10% | train_error: 6.52 | train_acc: 0.685 | val_error: 7.25 | val_acc: 0.650
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===========================&gt;------------] 72.20% | train_error: 6.47 | train_acc: 0.688 | val_error: 7.25 | val_acc: 0.650
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===========================&gt;------------] 72.30% | train_error: 6.42 | train_acc: 0.690 | val_error: 7.10 | val_acc: 0.657
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===========================&gt;------------] 72.40% | train_error: 6.42 | train_acc: 0.690 | val_error: 7.10 | val_acc: 0.657
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [============================&gt;-----------] 72.50% | train_error: 6.42 | train_acc: 0.690 | val_error: 7.10 | val_acc: 0.657
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [============================&gt;-----------] 72.60% | train_error: 6.42 | train_acc: 0.690 | val_error: 6.81 | val_acc: 0.671
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [============================&gt;-----------] 72.70% | train_error: 6.37 | train_acc: 0.692 | val_error: 6.81 | val_acc: 0.671
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [============================&gt;-----------] 72.80% | train_error: 6.32 | train_acc: 0.695 | val_error: 6.81 | val_acc: 0.671
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [============================&gt;-----------] 72.90% | train_error: 6.32 | train_acc: 0.695 | val_error: 6.81 | val_acc: 0.671
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [============================&gt;-----------] 73.00% | train_error: 6.32 | train_acc: 0.695 | val_error: 6.81 | val_acc: 0.671
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [============================&gt;-----------] 73.10% | train_error: 6.32 | train_acc: 0.695 | val_error: 6.81 | val_acc: 0.671
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [============================&gt;-----------] 73.20% | train_error: 6.32 | train_acc: 0.695 | val_error: 6.81 | val_acc: 0.671
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [============================&gt;-----------] 73.30% | train_error: 6.32 | train_acc: 0.695 | val_error: 6.81 | val_acc: 0.671
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [============================&gt;-----------] 73.40% | train_error: 6.32 | train_acc: 0.695 | val_error: 6.81 | val_acc: 0.671
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [============================&gt;-----------] 73.50% | train_error: 6.23 | train_acc: 0.700 | val_error: 6.81 | val_acc: 0.671
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [============================&gt;-----------] 73.60% | train_error: 6.23 | train_acc: 0.700 | val_error: 6.81 | val_acc: 0.671
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [============================&gt;-----------] 73.70% | train_error: 6.23 | train_acc: 0.700 | val_error: 6.67 | val_acc: 0.678
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [============================&gt;-----------] 73.80% | train_error: 6.23 | train_acc: 0.700 | val_error: 6.67 | val_acc: 0.678
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [============================&gt;-----------] 73.90% | train_error: 6.18 | train_acc: 0.702 | val_error: 6.67 | val_acc: 0.678
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [============================&gt;-----------] 74.00% | train_error: 5.98 | train_acc: 0.711 | val_error: 6.67 | val_acc: 0.678
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [============================&gt;-----------] 74.10% | train_error: 5.93 | train_acc: 0.714 | val_error: 6.67 | val_acc: 0.678
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [============================&gt;-----------] 74.20% | train_error: 5.84 | train_acc: 0.718 | val_error: 6.67 | val_acc: 0.678
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [============================&gt;-----------] 74.30% | train_error: 5.84 | train_acc: 0.718 | val_error: 6.67 | val_acc: 0.678
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [============================&gt;-----------] 74.40% | train_error: 5.84 | train_acc: 0.718 | val_error: 6.67 | val_acc: 0.678
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [============================&gt;-----------] 74.50% | train_error: 5.79 | train_acc: 0.721 | val_error: 6.67 | val_acc: 0.678
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [============================&gt;-----------] 74.60% | train_error: 5.64 | train_acc: 0.728 | val_error: 6.67 | val_acc: 0.678
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [============================&gt;-----------] 74.70% | train_error: 5.64 | train_acc: 0.728 | val_error: 6.67 | val_acc: 0.678
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [============================&gt;-----------] 74.80% | train_error: 5.59 | train_acc: 0.730 | val_error: 6.67 | val_acc: 0.678
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [============================&gt;-----------] 74.90% | train_error: 5.59 | train_acc: 0.730 | val_error: 6.38 | val_acc: 0.692
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=============================&gt;----------] 75.00% | train_error: 5.59 | train_acc: 0.730 | val_error: 6.38 | val_acc: 0.692
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=============================&gt;----------] 75.10% | train_error: 5.50 | train_acc: 0.735 | val_error: 6.23 | val_acc: 0.699
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=============================&gt;----------] 75.20% | train_error: 5.30 | train_acc: 0.744 | val_error: 6.23 | val_acc: 0.699
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=============================&gt;----------] 75.30% | train_error: 5.30 | train_acc: 0.744 | val_error: 6.23 | val_acc: 0.699
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=============================&gt;----------] 75.40% | train_error: 5.30 | train_acc: 0.744 | val_error: 6.23 | val_acc: 0.699
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=============================&gt;----------] 75.50% | train_error: 5.16 | train_acc: 0.751 | val_error: 6.23 | val_acc: 0.699
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=============================&gt;----------] 75.60% | train_error: 5.11 | train_acc: 0.754 | val_error: 6.09 | val_acc: 0.706
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=============================&gt;----------] 75.70% | train_error: 5.06 | train_acc: 0.756 | val_error: 5.94 | val_acc: 0.713
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=============================&gt;----------] 75.80% | train_error: 5.06 | train_acc: 0.756 | val_error: 5.94 | val_acc: 0.713
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=============================&gt;----------] 75.90% | train_error: 5.06 | train_acc: 0.756 | val_error: 5.94 | val_acc: 0.713
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=============================&gt;----------] 76.00% | train_error: 5.06 | train_acc: 0.756 | val_error: 5.94 | val_acc: 0.713
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=============================&gt;----------] 76.10% | train_error: 5.01 | train_acc: 0.758 | val_error: 5.94 | val_acc: 0.713
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=============================&gt;----------] 76.20% | train_error: 5.01 | train_acc: 0.758 | val_error: 5.94 | val_acc: 0.713
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=============================&gt;----------] 76.30% | train_error: 4.91 | train_acc: 0.763 | val_error: 5.80 | val_acc: 0.720
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=============================&gt;----------] 76.40% | train_error: 4.91 | train_acc: 0.763 | val_error: 5.80 | val_acc: 0.720
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=============================&gt;----------] 76.50% | train_error: 4.91 | train_acc: 0.763 | val_error: 5.80 | val_acc: 0.720
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=============================&gt;----------] 76.60% | train_error: 4.91 | train_acc: 0.763 | val_error: 5.80 | val_acc: 0.720
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=============================&gt;----------] 76.70% | train_error: 4.91 | train_acc: 0.763 | val_error: 5.80 | val_acc: 0.720
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=============================&gt;----------] 76.80% | train_error: 4.91 | train_acc: 0.763 | val_error: 5.80 | val_acc: 0.720
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=============================&gt;----------] 76.90% | train_error: 4.67 | train_acc: 0.775 | val_error: 5.80 | val_acc: 0.720
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=============================&gt;----------] 77.00% | train_error: 4.67 | train_acc: 0.775 | val_error: 5.80 | val_acc: 0.720
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=============================&gt;----------] 77.10% | train_error: 4.67 | train_acc: 0.775 | val_error: 5.80 | val_acc: 0.720
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=============================&gt;----------] 77.20% | train_error: 4.67 | train_acc: 0.775 | val_error: 5.65 | val_acc: 0.727
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=============================&gt;----------] 77.30% | train_error: 4.67 | train_acc: 0.775 | val_error: 5.65 | val_acc: 0.727
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=============================&gt;----------] 77.40% | train_error: 4.67 | train_acc: 0.775 | val_error: 5.65 | val_acc: 0.727
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==============================&gt;---------] 77.50% | train_error: 4.67 | train_acc: 0.775 | val_error: 5.65 | val_acc: 0.727
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==============================&gt;---------] 77.60% | train_error: 4.67 | train_acc: 0.775 | val_error: 5.65 | val_acc: 0.727
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==============================&gt;---------] 77.70% | train_error: 4.67 | train_acc: 0.775 | val_error: 5.36 | val_acc: 0.741
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==============================&gt;---------] 77.80% | train_error: 4.67 | train_acc: 0.775 | val_error: 5.36 | val_acc: 0.741
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==============================&gt;---------] 77.90% | train_error: 4.67 | train_acc: 0.775 | val_error: 5.36 | val_acc: 0.741
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==============================&gt;---------] 78.00% | train_error: 4.67 | train_acc: 0.775 | val_error: 5.36 | val_acc: 0.741
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==============================&gt;---------] 78.10% | train_error: 4.67 | train_acc: 0.775 | val_error: 5.36 | val_acc: 0.741
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==============================&gt;---------] 78.20% | train_error: 4.62 | train_acc: 0.777 | val_error: 5.36 | val_acc: 0.741
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==============================&gt;---------] 78.30% | train_error: 4.57 | train_acc: 0.779 | val_error: 5.22 | val_acc: 0.748
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==============================&gt;---------] 78.40% | train_error: 4.57 | train_acc: 0.779 | val_error: 5.22 | val_acc: 0.748
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==============================&gt;---------] 78.50% | train_error: 4.57 | train_acc: 0.779 | val_error: 5.22 | val_acc: 0.748
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==============================&gt;---------] 78.60% | train_error: 4.57 | train_acc: 0.779 | val_error: 5.22 | val_acc: 0.748
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==============================&gt;---------] 78.70% | train_error: 4.57 | train_acc: 0.779 | val_error: 5.22 | val_acc: 0.748
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==============================&gt;---------] 78.80% | train_error: 4.57 | train_acc: 0.779 | val_error: 5.22 | val_acc: 0.748
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==============================&gt;---------] 78.90% | train_error: 4.57 | train_acc: 0.779 | val_error: 5.07 | val_acc: 0.755
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==============================&gt;---------] 79.00% | train_error: 4.52 | train_acc: 0.782 | val_error: 5.07 | val_acc: 0.755
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==============================&gt;---------] 79.10% | train_error: 4.52 | train_acc: 0.782 | val_error: 4.93 | val_acc: 0.762
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==============================&gt;---------] 79.20% | train_error: 4.43 | train_acc: 0.786 | val_error: 4.78 | val_acc: 0.769
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==============================&gt;---------] 79.30% | train_error: 4.38 | train_acc: 0.789 | val_error: 4.78 | val_acc: 0.769
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==============================&gt;---------] 79.40% | train_error: 4.13 | train_acc: 0.800 | val_error: 4.78 | val_acc: 0.769
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==============================&gt;---------] 79.50% | train_error: 4.13 | train_acc: 0.800 | val_error: 4.78 | val_acc: 0.769
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==============================&gt;---------] 79.60% | train_error: 4.13 | train_acc: 0.800 | val_error: 4.78 | val_acc: 0.769
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==============================&gt;---------] 79.70% | train_error: 4.09 | train_acc: 0.803 | val_error: 4.78 | val_acc: 0.769
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==============================&gt;---------] 79.80% | train_error: 4.09 | train_acc: 0.803 | val_error: 4.78 | val_acc: 0.769
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==============================&gt;---------] 79.90% | train_error: 4.09 | train_acc: 0.803 | val_error: 4.64 | val_acc: 0.776
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===============================&gt;--------] 80.00% | train_error: 4.09 | train_acc: 0.803 | val_error: 4.64 | val_acc: 0.776
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===============================&gt;--------] 80.10% | train_error: 4.09 | train_acc: 0.803 | val_error: 4.49 | val_acc: 0.783
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===============================&gt;--------] 80.20% | train_error: 4.04 | train_acc: 0.805 | val_error: 4.49 | val_acc: 0.783
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===============================&gt;--------] 80.30% | train_error: 4.09 | train_acc: 0.803 | val_error: 4.49 | val_acc: 0.783
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===============================&gt;--------] 80.40% | train_error: 4.09 | train_acc: 0.803 | val_error: 4.49 | val_acc: 0.783
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===============================&gt;--------] 80.50% | train_error: 4.28 | train_acc: 0.793 | val_error: 4.35 | val_acc: 0.790
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===============================&gt;--------] 80.60% | train_error: 4.28 | train_acc: 0.793 | val_error: 4.35 | val_acc: 0.790
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===============================&gt;--------] 80.70% | train_error: 4.28 | train_acc: 0.793 | val_error: 4.35 | val_acc: 0.790
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===============================&gt;--------] 80.80% | train_error: 4.28 | train_acc: 0.793 | val_error: 4.35 | val_acc: 0.790
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===============================&gt;--------] 80.90% | train_error: 4.28 | train_acc: 0.793 | val_error: 4.35 | val_acc: 0.790
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===============================&gt;--------] 81.00% | train_error: 4.28 | train_acc: 0.793 | val_error: 4.20 | val_acc: 0.797
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===============================&gt;--------] 81.10% | train_error: 4.28 | train_acc: 0.793 | val_error: 4.20 | val_acc: 0.797
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===============================&gt;--------] 81.20% | train_error: 4.28 | train_acc: 0.793 | val_error: 4.20 | val_acc: 0.797
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===============================&gt;--------] 81.30% | train_error: 4.28 | train_acc: 0.793 | val_error: 4.20 | val_acc: 0.797
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===============================&gt;--------] 81.40% | train_error: 4.28 | train_acc: 0.793 | val_error: 4.20 | val_acc: 0.797
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===============================&gt;--------] 81.50% | train_error: 4.28 | train_acc: 0.793 | val_error: 4.20 | val_acc: 0.797
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===============================&gt;--------] 81.60% | train_error: 4.28 | train_acc: 0.793 | val_error: 4.20 | val_acc: 0.797
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===============================&gt;--------] 81.70% | train_error: 4.28 | train_acc: 0.793 | val_error: 4.20 | val_acc: 0.797
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===============================&gt;--------] 81.80% | train_error: 4.28 | train_acc: 0.793 | val_error: 4.06 | val_acc: 0.804
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===============================&gt;--------] 81.90% | train_error: 4.28 | train_acc: 0.793 | val_error: 4.06 | val_acc: 0.804
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===============================&gt;--------] 82.00% | train_error: 4.28 | train_acc: 0.793 | val_error: 4.06 | val_acc: 0.804
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===============================&gt;--------] 82.10% | train_error: 4.23 | train_acc: 0.796 | val_error: 4.06 | val_acc: 0.804
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===============================&gt;--------] 82.20% | train_error: 4.23 | train_acc: 0.796 | val_error: 4.06 | val_acc: 0.804
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===============================&gt;--------] 82.30% | train_error: 4.23 | train_acc: 0.796 | val_error: 4.06 | val_acc: 0.804
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===============================&gt;--------] 82.40% | train_error: 4.23 | train_acc: 0.796 | val_error: 4.06 | val_acc: 0.804
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [================================&gt;-------] 82.50% | train_error: 4.23 | train_acc: 0.796 | val_error: 4.06 | val_acc: 0.804
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [================================&gt;-------] 82.60% | train_error: 4.23 | train_acc: 0.796 | val_error: 4.06 | val_acc: 0.804
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [================================&gt;-------] 82.70% | train_error: 4.23 | train_acc: 0.796 | val_error: 4.06 | val_acc: 0.804
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [================================&gt;-------] 82.80% | train_error: 4.23 | train_acc: 0.796 | val_error: 4.06 | val_acc: 0.804
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [================================&gt;-------] 82.90% | train_error: 4.23 | train_acc: 0.796 | val_error: 4.20 | val_acc: 0.797
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [================================&gt;-------] 83.00% | train_error: 4.23 | train_acc: 0.796 | val_error: 4.20 | val_acc: 0.797
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [================================&gt;-------] 83.10% | train_error: 4.23 | train_acc: 0.796 | val_error: 4.20 | val_acc: 0.797
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [================================&gt;-------] 83.20% | train_error: 4.23 | train_acc: 0.796 | val_error: 4.20 | val_acc: 0.797
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [================================&gt;-------] 83.30% | train_error: 4.23 | train_acc: 0.796 | val_error: 4.20 | val_acc: 0.797
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [================================&gt;-------] 83.40% | train_error: 4.23 | train_acc: 0.796 | val_error: 4.20 | val_acc: 0.797
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [================================&gt;-------] 83.50% | train_error: 4.23 | train_acc: 0.796 | val_error: 4.20 | val_acc: 0.797
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [================================&gt;-------] 83.60% | train_error: 4.23 | train_acc: 0.796 | val_error: 4.06 | val_acc: 0.804
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [================================&gt;-------] 83.70% | train_error: 4.23 | train_acc: 0.796 | val_error: 4.06 | val_acc: 0.804
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [================================&gt;-------] 83.80% | train_error: 4.23 | train_acc: 0.796 | val_error: 4.20 | val_acc: 0.797
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [================================&gt;-------] 83.90% | train_error: 4.23 | train_acc: 0.796 | val_error: 4.20 | val_acc: 0.797
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [================================&gt;-------] 84.00% | train_error: 4.23 | train_acc: 0.796 | val_error: 4.06 | val_acc: 0.804
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [================================&gt;-------] 84.10% | train_error: 4.23 | train_acc: 0.796 | val_error: 4.06 | val_acc: 0.804
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [================================&gt;-------] 84.20% | train_error: 4.33 | train_acc: 0.791 | val_error: 4.06 | val_acc: 0.804
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [================================&gt;-------] 84.30% | train_error: 4.33 | train_acc: 0.791 | val_error: 4.06 | val_acc: 0.804
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [================================&gt;-------] 84.40% | train_error: 4.33 | train_acc: 0.791 | val_error: 4.06 | val_acc: 0.804
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [================================&gt;-------] 84.50% | train_error: 4.33 | train_acc: 0.791 | val_error: 4.06 | val_acc: 0.804
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [================================&gt;-------] 84.60% | train_error: 4.23 | train_acc: 0.796 | val_error: 4.06 | val_acc: 0.804
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [================================&gt;-------] 84.70% | train_error: 4.23 | train_acc: 0.796 | val_error: 4.06 | val_acc: 0.804
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [================================&gt;-------] 84.80% | train_error: 4.23 | train_acc: 0.796 | val_error: 4.06 | val_acc: 0.804
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [================================&gt;-------] 84.90% | train_error: 4.23 | train_acc: 0.796 | val_error: 4.06 | val_acc: 0.804
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=================================&gt;------] 85.00% | train_error: 4.23 | train_acc: 0.796 | val_error: 4.06 | val_acc: 0.804
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=================================&gt;------] 85.10% | train_error: 4.23 | train_acc: 0.796 | val_error: 4.06 | val_acc: 0.804
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=================================&gt;------] 85.20% | train_error: 4.18 | train_acc: 0.798 | val_error: 4.06 | val_acc: 0.804
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=================================&gt;------] 85.30% | train_error: 4.18 | train_acc: 0.798 | val_error: 4.06 | val_acc: 0.804
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=================================&gt;------] 85.40% | train_error: 4.18 | train_acc: 0.798 | val_error: 4.06 | val_acc: 0.804
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=================================&gt;------] 85.50% | train_error: 4.18 | train_acc: 0.798 | val_error: 4.06 | val_acc: 0.804
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=================================&gt;------] 85.60% | train_error: 4.13 | train_acc: 0.800 | val_error: 4.06 | val_acc: 0.804
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=================================&gt;------] 85.70% | train_error: 4.13 | train_acc: 0.800 | val_error: 4.06 | val_acc: 0.804
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=================================&gt;------] 85.80% | train_error: 4.09 | train_acc: 0.803 | val_error: 4.06 | val_acc: 0.804
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=================================&gt;------] 85.90% | train_error: 4.09 | train_acc: 0.803 | val_error: 4.06 | val_acc: 0.804
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=================================&gt;------] 86.00% | train_error: 4.09 | train_acc: 0.803 | val_error: 4.20 | val_acc: 0.797
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=================================&gt;------] 86.10% | train_error: 4.09 | train_acc: 0.803 | val_error: 4.20 | val_acc: 0.797
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=================================&gt;------] 86.20% | train_error: 4.09 | train_acc: 0.803 | val_error: 4.20 | val_acc: 0.797
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=================================&gt;------] 86.30% | train_error: 4.09 | train_acc: 0.803 | val_error: 4.20 | val_acc: 0.797
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=================================&gt;------] 86.40% | train_error: 4.09 | train_acc: 0.803 | val_error: 4.20 | val_acc: 0.797
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=================================&gt;------] 86.50% | train_error: 4.09 | train_acc: 0.803 | val_error: 4.20 | val_acc: 0.797
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=================================&gt;------] 86.60% | train_error: 4.04 | train_acc: 0.805 | val_error: 4.20 | val_acc: 0.797
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=================================&gt;------] 86.70% | train_error: 4.04 | train_acc: 0.805 | val_error: 4.20 | val_acc: 0.797
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=================================&gt;------] 86.80% | train_error: 4.04 | train_acc: 0.805 | val_error: 4.20 | val_acc: 0.797
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=================================&gt;------] 86.90% | train_error: 3.99 | train_acc: 0.808 | val_error: 4.20 | val_acc: 0.797
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=================================&gt;------] 87.00% | train_error: 3.89 | train_acc: 0.812 | val_error: 4.20 | val_acc: 0.797
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=================================&gt;------] 87.10% | train_error: 3.89 | train_acc: 0.812 | val_error: 4.20 | val_acc: 0.797
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=================================&gt;------] 87.20% | train_error: 3.89 | train_acc: 0.812 | val_error: 4.20 | val_acc: 0.797
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=================================&gt;------] 87.30% | train_error: 3.89 | train_acc: 0.812 | val_error: 4.20 | val_acc: 0.797
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=================================&gt;------] 87.40% | train_error: 3.79 | train_acc: 0.817 | val_error: 4.20 | val_acc: 0.797
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==================================&gt;-----] 87.50% | train_error: 3.79 | train_acc: 0.817 | val_error: 4.20 | val_acc: 0.797
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==================================&gt;-----] 87.60% | train_error: 3.89 | train_acc: 0.812 | val_error: 4.20 | val_acc: 0.797
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==================================&gt;-----] 87.70% | train_error: 3.84 | train_acc: 0.815 | val_error: 4.20 | val_acc: 0.797
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==================================&gt;-----] 87.80% | train_error: 3.84 | train_acc: 0.815 | val_error: 4.20 | val_acc: 0.797
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==================================&gt;-----] 87.90% | train_error: 3.84 | train_acc: 0.815 | val_error: 4.20 | val_acc: 0.797
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==================================&gt;-----] 88.00% | train_error: 3.84 | train_acc: 0.815 | val_error: 4.20 | val_acc: 0.797
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==================================&gt;-----] 88.10% | train_error: 3.84 | train_acc: 0.815 | val_error: 4.20 | val_acc: 0.797
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==================================&gt;-----] 88.20% | train_error: 3.84 | train_acc: 0.815 | val_error: 4.06 | val_acc: 0.804
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==================================&gt;-----] 88.30% | train_error: 3.84 | train_acc: 0.815 | val_error: 4.06 | val_acc: 0.804
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==================================&gt;-----] 88.40% | train_error: 3.84 | train_acc: 0.815 | val_error: 4.06 | val_acc: 0.804
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==================================&gt;-----] 88.50% | train_error: 3.70 | train_acc: 0.822 | val_error: 4.06 | val_acc: 0.804
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==================================&gt;-----] 88.60% | train_error: 3.70 | train_acc: 0.822 | val_error: 4.06 | val_acc: 0.804
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==================================&gt;-----] 88.70% | train_error: 3.70 | train_acc: 0.822 | val_error: 4.06 | val_acc: 0.804
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==================================&gt;-----] 88.80% | train_error: 3.70 | train_acc: 0.822 | val_error: 4.06 | val_acc: 0.804
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==================================&gt;-----] 88.90% | train_error: 3.70 | train_acc: 0.822 | val_error: 4.06 | val_acc: 0.804
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==================================&gt;-----] 89.00% | train_error: 3.70 | train_acc: 0.822 | val_error: 4.06 | val_acc: 0.804
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==================================&gt;-----] 89.10% | train_error: 3.70 | train_acc: 0.822 | val_error: 3.91 | val_acc: 0.811
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==================================&gt;-----] 89.20% | train_error: 3.70 | train_acc: 0.822 | val_error: 3.91 | val_acc: 0.811
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==================================&gt;-----] 89.30% | train_error: 3.70 | train_acc: 0.822 | val_error: 3.91 | val_acc: 0.811
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==================================&gt;-----] 89.40% | train_error: 3.70 | train_acc: 0.822 | val_error: 3.91 | val_acc: 0.811
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==================================&gt;-----] 89.50% | train_error: 3.70 | train_acc: 0.822 | val_error: 3.91 | val_acc: 0.811
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==================================&gt;-----] 89.60% | train_error: 3.70 | train_acc: 0.822 | val_error: 3.91 | val_acc: 0.811
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==================================&gt;-----] 89.70% | train_error: 3.70 | train_acc: 0.822 | val_error: 3.91 | val_acc: 0.811
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==================================&gt;-----] 89.80% | train_error: 3.65 | train_acc: 0.824 | val_error: 3.91 | val_acc: 0.811
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==================================&gt;-----] 89.90% | train_error: 3.60 | train_acc: 0.826 | val_error: 3.91 | val_acc: 0.811
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===================================&gt;----] 90.00% | train_error: 3.60 | train_acc: 0.826 | val_error: 3.91 | val_acc: 0.811
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===================================&gt;----] 90.10% | train_error: 3.60 | train_acc: 0.826 | val_error: 3.91 | val_acc: 0.811
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===================================&gt;----] 90.20% | train_error: 3.60 | train_acc: 0.826 | val_error: 3.91 | val_acc: 0.811
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===================================&gt;----] 90.30% | train_error: 3.60 | train_acc: 0.826 | val_error: 3.91 | val_acc: 0.811
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===================================&gt;----] 90.40% | train_error: 3.60 | train_acc: 0.826 | val_error: 3.77 | val_acc: 0.818
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===================================&gt;----] 90.50% | train_error: 3.60 | train_acc: 0.826 | val_error: 3.77 | val_acc: 0.818
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===================================&gt;----] 90.60% | train_error: 3.60 | train_acc: 0.826 | val_error: 3.77 | val_acc: 0.818
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===================================&gt;----] 90.70% | train_error: 3.60 | train_acc: 0.826 | val_error: 3.77 | val_acc: 0.818
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===================================&gt;----] 90.80% | train_error: 3.60 | train_acc: 0.826 | val_error: 3.77 | val_acc: 0.818
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===================================&gt;----] 90.90% | train_error: 3.60 | train_acc: 0.826 | val_error: 3.77 | val_acc: 0.818
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===================================&gt;----] 91.00% | train_error: 3.60 | train_acc: 0.826 | val_error: 3.77 | val_acc: 0.818
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===================================&gt;----] 91.10% | train_error: 3.60 | train_acc: 0.826 | val_error: 3.77 | val_acc: 0.818
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===================================&gt;----] 91.20% | train_error: 3.60 | train_acc: 0.826 | val_error: 3.77 | val_acc: 0.818
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===================================&gt;----] 91.30% | train_error: 3.60 | train_acc: 0.826 | val_error: 3.77 | val_acc: 0.818
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===================================&gt;----] 91.40% | train_error: 3.60 | train_acc: 0.826 | val_error: 3.77 | val_acc: 0.818
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===================================&gt;----] 91.50% | train_error: 3.60 | train_acc: 0.826 | val_error: 3.77 | val_acc: 0.818
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===================================&gt;----] 91.60% | train_error: 3.60 | train_acc: 0.826 | val_error: 3.77 | val_acc: 0.818
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===================================&gt;----] 91.70% | train_error: 3.60 | train_acc: 0.826 | val_error: 3.77 | val_acc: 0.818
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===================================&gt;----] 91.80% | train_error: 3.60 | train_acc: 0.826 | val_error: 3.77 | val_acc: 0.818
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===================================&gt;----] 91.90% | train_error: 3.60 | train_acc: 0.826 | val_error: 3.77 | val_acc: 0.818
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===================================&gt;----] 92.00% | train_error: 3.60 | train_acc: 0.826 | val_error: 3.77 | val_acc: 0.818
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===================================&gt;----] 92.10% | train_error: 3.60 | train_acc: 0.826 | val_error: 3.77 | val_acc: 0.818
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===================================&gt;----] 92.20% | train_error: 3.60 | train_acc: 0.826 | val_error: 3.77 | val_acc: 0.818
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===================================&gt;----] 92.30% | train_error: 3.60 | train_acc: 0.826 | val_error: 3.77 | val_acc: 0.818
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===================================&gt;----] 92.40% | train_error: 3.55 | train_acc: 0.829 | val_error: 3.62 | val_acc: 0.825
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [====================================&gt;---] 92.50% | train_error: 3.55 | train_acc: 0.829 | val_error: 3.62 | val_acc: 0.825
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [====================================&gt;---] 92.60% | train_error: 3.55 | train_acc: 0.829 | val_error: 3.62 | val_acc: 0.825
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [====================================&gt;---] 92.70% | train_error: 3.55 | train_acc: 0.829 | val_error: 3.62 | val_acc: 0.825
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [====================================&gt;---] 92.80% | train_error: 3.55 | train_acc: 0.829 | val_error: 3.62 | val_acc: 0.825
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [====================================&gt;---] 92.90% | train_error: 3.55 | train_acc: 0.829 | val_error: 3.62 | val_acc: 0.825
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [====================================&gt;---] 93.00% | train_error: 3.55 | train_acc: 0.829 | val_error: 3.62 | val_acc: 0.825
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [====================================&gt;---] 93.10% | train_error: 3.55 | train_acc: 0.829 | val_error: 3.62 | val_acc: 0.825
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [====================================&gt;---] 93.20% | train_error: 3.55 | train_acc: 0.829 | val_error: 3.62 | val_acc: 0.825
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [====================================&gt;---] 93.30% | train_error: 3.55 | train_acc: 0.829 | val_error: 3.62 | val_acc: 0.825
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [====================================&gt;---] 93.40% | train_error: 3.55 | train_acc: 0.829 | val_error: 3.62 | val_acc: 0.825
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [====================================&gt;---] 93.50% | train_error: 3.55 | train_acc: 0.829 | val_error: 3.62 | val_acc: 0.825
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [====================================&gt;---] 93.60% | train_error: 3.55 | train_acc: 0.829 | val_error: 3.62 | val_acc: 0.825
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [====================================&gt;---] 93.70% | train_error: 3.55 | train_acc: 0.829 | val_error: 3.62 | val_acc: 0.825
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [====================================&gt;---] 93.80% | train_error: 3.55 | train_acc: 0.829 | val_error: 3.62 | val_acc: 0.825
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [====================================&gt;---] 93.90% | train_error: 3.55 | train_acc: 0.829 | val_error: 3.62 | val_acc: 0.825
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [====================================&gt;---] 94.00% | train_error: 3.55 | train_acc: 0.829 | val_error: 3.62 | val_acc: 0.825
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [====================================&gt;---] 94.10% | train_error: 3.55 | train_acc: 0.829 | val_error: 3.62 | val_acc: 0.825
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [====================================&gt;---] 94.20% | train_error: 3.55 | train_acc: 0.829 | val_error: 3.62 | val_acc: 0.825
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [====================================&gt;---] 94.30% | train_error: 3.55 | train_acc: 0.829 | val_error: 3.62 | val_acc: 0.825
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [====================================&gt;---] 94.40% | train_error: 3.55 | train_acc: 0.829 | val_error: 3.62 | val_acc: 0.825
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [====================================&gt;---] 94.50% | train_error: 3.55 | train_acc: 0.829 | val_error: 3.62 | val_acc: 0.825
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [====================================&gt;---] 94.60% | train_error: 3.55 | train_acc: 0.829 | val_error: 3.62 | val_acc: 0.825
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [====================================&gt;---] 94.70% | train_error: 3.65 | train_acc: 0.824 | val_error: 3.62 | val_acc: 0.825
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [====================================&gt;---] 94.80% | train_error: 3.65 | train_acc: 0.824 | val_error: 3.62 | val_acc: 0.825
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [====================================&gt;---] 94.90% | train_error: 3.65 | train_acc: 0.824 | val_error: 3.62 | val_acc: 0.825
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=====================================&gt;--] 95.00% | train_error: 3.65 | train_acc: 0.824 | val_error: 3.62 | val_acc: 0.825
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=====================================&gt;--] 95.10% | train_error: 3.65 | train_acc: 0.824 | val_error: 3.62 | val_acc: 0.825
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=====================================&gt;--] 95.20% | train_error: 3.65 | train_acc: 0.824 | val_error: 3.62 | val_acc: 0.825
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=====================================&gt;--] 95.30% | train_error: 3.65 | train_acc: 0.824 | val_error: 3.62 | val_acc: 0.825
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=====================================&gt;--] 95.40% | train_error: 3.65 | train_acc: 0.824 | val_error: 3.62 | val_acc: 0.825
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=====================================&gt;--] 95.50% | train_error: 3.65 | train_acc: 0.824 | val_error: 3.62 | val_acc: 0.825
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=====================================&gt;--] 95.60% | train_error: 3.65 | train_acc: 0.824 | val_error: 3.48 | val_acc: 0.832
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=====================================&gt;--] 95.70% | train_error: 3.65 | train_acc: 0.824 | val_error: 3.48 | val_acc: 0.832
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=====================================&gt;--] 95.80% | train_error: 3.65 | train_acc: 0.824 | val_error: 3.48 | val_acc: 0.832
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=====================================&gt;--] 95.90% | train_error: 3.55 | train_acc: 0.829 | val_error: 3.48 | val_acc: 0.832
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=====================================&gt;--] 96.00% | train_error: 3.55 | train_acc: 0.829 | val_error: 3.48 | val_acc: 0.832
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=====================================&gt;--] 96.10% | train_error: 3.55 | train_acc: 0.829 | val_error: 3.48 | val_acc: 0.832
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=====================================&gt;--] 96.20% | train_error: 3.55 | train_acc: 0.829 | val_error: 3.48 | val_acc: 0.832
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=====================================&gt;--] 96.30% | train_error: 3.55 | train_acc: 0.829 | val_error: 3.48 | val_acc: 0.832
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=====================================&gt;--] 96.40% | train_error: 3.55 | train_acc: 0.829 | val_error: 3.48 | val_acc: 0.832
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=====================================&gt;--] 96.50% | train_error: 3.50 | train_acc: 0.831 | val_error: 3.48 | val_acc: 0.832
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=====================================&gt;--] 96.60% | train_error: 3.50 | train_acc: 0.831 | val_error: 3.48 | val_acc: 0.832
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=====================================&gt;--] 96.70% | train_error: 3.50 | train_acc: 0.831 | val_error: 3.48 | val_acc: 0.832
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=====================================&gt;--] 96.80% | train_error: 3.50 | train_acc: 0.831 | val_error: 3.48 | val_acc: 0.832
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=====================================&gt;--] 96.90% | train_error: 3.50 | train_acc: 0.831 | val_error: 3.48 | val_acc: 0.832
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=====================================&gt;--] 97.00% | train_error: 3.50 | train_acc: 0.831 | val_error: 3.48 | val_acc: 0.832
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=====================================&gt;--] 97.10% | train_error: 3.50 | train_acc: 0.831 | val_error: 3.48 | val_acc: 0.832
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=====================================&gt;--] 97.20% | train_error: 3.50 | train_acc: 0.831 | val_error: 3.48 | val_acc: 0.832
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=====================================&gt;--] 97.30% | train_error: 3.50 | train_acc: 0.831 | val_error: 3.48 | val_acc: 0.832
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=====================================&gt;--] 97.40% | train_error: 3.50 | train_acc: 0.831 | val_error: 3.48 | val_acc: 0.832
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [======================================&gt;-] 97.50% | train_error: 3.50 | train_acc: 0.831 | val_error: 3.48 | val_acc: 0.832
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [======================================&gt;-] 97.60% | train_error: 3.50 | train_acc: 0.831 | val_error: 3.48 | val_acc: 0.832
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [======================================&gt;-] 97.70% | train_error: 3.50 | train_acc: 0.831 | val_error: 3.48 | val_acc: 0.832
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [======================================&gt;-] 97.80% | train_error: 3.50 | train_acc: 0.831 | val_error: 3.48 | val_acc: 0.832
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [======================================&gt;-] 97.90% | train_error: 3.50 | train_acc: 0.831 | val_error: 3.48 | val_acc: 0.832
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [======================================&gt;-] 98.00% | train_error: 3.50 | train_acc: 0.831 | val_error: 3.48 | val_acc: 0.832
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [======================================&gt;-] 98.10% | train_error: 3.50 | train_acc: 0.831 | val_error: 3.48 | val_acc: 0.832
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [======================================&gt;-] 98.20% | train_error: 3.45 | train_acc: 0.833 | val_error: 3.48 | val_acc: 0.832
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [======================================&gt;-] 98.30% | train_error: 3.45 | train_acc: 0.833 | val_error: 3.48 | val_acc: 0.832
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [======================================&gt;-] 98.40% | train_error: 3.41 | train_acc: 0.836 | val_error: 3.48 | val_acc: 0.832
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [======================================&gt;-] 98.50% | train_error: 3.36 | train_acc: 0.838 | val_error: 3.48 | val_acc: 0.832
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [======================================&gt;-] 98.60% | train_error: 3.26 | train_acc: 0.843 | val_error: 3.48 | val_acc: 0.832
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [======================================&gt;-] 98.70% | train_error: 3.26 | train_acc: 0.843 | val_error: 3.62 | val_acc: 0.825
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [======================================&gt;-] 98.80% | train_error: 3.26 | train_acc: 0.843 | val_error: 3.62 | val_acc: 0.825
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [======================================&gt;-] 98.90% | train_error: 3.26 | train_acc: 0.843 | val_error: 3.62 | val_acc: 0.825
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [======================================&gt;-] 99.00% | train_error: 3.26 | train_acc: 0.843 | val_error: 3.62 | val_acc: 0.825
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [======================================&gt;-] 99.10% | train_error: 3.26 | train_acc: 0.843 | val_error: 3.62 | val_acc: 0.825
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [======================================&gt;-] 99.20% | train_error: 3.26 | train_acc: 0.843 | val_error: 3.62 | val_acc: 0.825
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [======================================&gt;-] 99.30% | train_error: 3.26 | train_acc: 0.843 | val_error: 3.62 | val_acc: 0.825
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [======================================&gt;-] 99.40% | train_error: 3.26 | train_acc: 0.843 | val_error: 3.62 | val_acc: 0.825
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [======================================&gt;-] 99.50% | train_error: 3.26 | train_acc: 0.843 | val_error: 3.62 | val_acc: 0.825
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [======================================&gt;-] 99.60% | train_error: 3.26 | train_acc: 0.843 | val_error: 3.62 | val_acc: 0.825
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [======================================&gt;-] 99.70% | train_error: 3.26 | train_acc: 0.843 | val_error: 3.62 | val_acc: 0.825
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [======================================&gt;-] 99.80% | train_error: 3.26 | train_acc: 0.843 | val_error: 3.77 | val_acc: 0.818
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [======================================&gt;-] 99.90% | train_error: 3.21 | train_acc: 0.845 | val_error: 3.77 | val_acc: 0.818
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=======================================&gt;] 100.0% | train_error: 3.21 | train_acc: 0.845 | val_error: 3.77 | val_acc: 0.818
</pre></div>
</div>
</div>
</div>
<p>Finally, we will create a neural network with 2 hidden layers with activation functions.</p>
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<div class="cell_input docutils container">
<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">input_nodes</span> <span class="o">=</span> <span class="n">X_train</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span>
<span class="n">hidden_nodes1</span> <span class="o">=</span> <span class="mi">100</span>
<span class="n">hidden_nodes2</span> <span class="o">=</span> <span class="mi">30</span>
<span class="n">output_nodes</span> <span class="o">=</span> <span class="mi">1</span>
<span class="n">dims</span> <span class="o">=</span> <span class="p">(</span><span class="n">input_nodes</span><span class="p">,</span> <span class="n">hidden_nodes1</span><span class="p">,</span> <span class="n">hidden_nodes2</span><span class="p">,</span> <span class="n">output_nodes</span><span class="p">)</span>
<span class="n">neural_network</span> <span class="o">=</span> <span class="n">FFNN</span><span class="p">(</span><span class="n">dims</span><span class="p">,</span> <span class="n">hidden_func</span><span class="o">=</span><span class="n">RELU</span><span class="p">,</span> <span class="n">output_func</span><span class="o">=</span><span class="n">sigmoid</span><span class="p">,</span> <span class="n">cost_func</span><span class="o">=</span><span class="n">CostLogReg</span><span class="p">,</span> <span class="n">seed</span><span class="o">=</span><span class="mi">2023</span><span class="p">)</span>
</pre></div>
</div>
</div>
</div>
<div class="cell docutils container">
<div class="cell_input docutils container">
<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">neural_network</span><span class="o">.</span><span class="n">reset_weights</span><span class="p">()</span> <span class="c1"># reset weights such that previous runs or reruns don&#39;t affect the weights</span>
<span class="n">scheduler</span> <span class="o">=</span> <span class="n">Adam</span><span class="p">(</span><span class="n">eta</span><span class="o">=</span><span class="mf">1e-4</span><span class="p">,</span> <span class="n">rho</span><span class="o">=</span><span class="mf">0.9</span><span class="p">,</span> <span class="n">rho2</span><span class="o">=</span><span class="mf">0.999</span><span class="p">)</span>
<span class="n">scores</span> <span class="o">=</span> <span class="n">neural_network</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X_train</span><span class="p">,</span> <span class="n">t_train</span><span class="p">,</span> <span class="n">scheduler</span><span class="p">,</span> <span class="n">epochs</span><span class="o">=</span><span class="mi">1000</span><span class="p">,</span> <span class="n">X_val</span><span class="o">=</span><span class="n">X_val</span><span class="p">,</span> <span class="n">t_val</span><span class="o">=</span><span class="n">t_val</span><span class="p">)</span>
</pre></div>
</div>
</div>
<div class="cell_output docutils container">
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Adam: Eta=0.0001, Lambda=0
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [----------------------------------------] 0.000% | train_error: 11.3 | train_acc: 0.453 | val_error: 10.4 | val_acc: 0.497
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [----------------------------------------] 0.1000% | train_error: 11.4 | train_acc: 0.448 | val_error: 10.4 | val_acc: 0.497
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [----------------------------------------] 0.2000% | train_error: 11.4 | train_acc: 0.448 | val_error: 10.4 | val_acc: 0.497
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [----------------------------------------] 0.3000% | train_error: 11.4 | train_acc: 0.448 | val_error: 10.6 | val_acc: 0.490
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [----------------------------------------] 0.4000% | train_error: 11.4 | train_acc: 0.448 | val_error: 10.6 | val_acc: 0.490
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [----------------------------------------] 0.5000% | train_error: 11.4 | train_acc: 0.448 | val_error: 10.6 | val_acc: 0.490
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [----------------------------------------] 0.6000% | train_error: 11.4 | train_acc: 0.448 | val_error: 10.6 | val_acc: 0.490
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [----------------------------------------] 0.7000% | train_error: 11.5 | train_acc: 0.444 | val_error: 10.6 | val_acc: 0.490
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [----------------------------------------] 0.8000% | train_error: 11.5 | train_acc: 0.444 | val_error: 10.6 | val_acc: 0.490
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [----------------------------------------] 0.9000% | train_error: 11.3 | train_acc: 0.453 | val_error: 10.6 | val_acc: 0.490
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [----------------------------------------] 1.000% | train_error: 11.3 | train_acc: 0.453 | val_error: 10.6 | val_acc: 0.490
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [----------------------------------------] 1.100% | train_error: 11.3 | train_acc: 0.453 | val_error: 10.6 | val_acc: 0.490
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [----------------------------------------] 1.200% | train_error: 11.3 | train_acc: 0.453 | val_error: 10.4 | val_acc: 0.497
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [----------------------------------------] 1.300% | train_error: 11.3 | train_acc: 0.453 | val_error: 10.4 | val_acc: 0.497
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [----------------------------------------] 1.400% | train_error: 11.4 | train_acc: 0.448 | val_error: 10.4 | val_acc: 0.497
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [----------------------------------------] 1.500% | train_error: 11.4 | train_acc: 0.451 | val_error: 10.4 | val_acc: 0.497
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [----------------------------------------] 1.600% | train_error: 11.5 | train_acc: 0.444 | val_error: 10.4 | val_acc: 0.497
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [----------------------------------------] 1.700% | train_error: 11.5 | train_acc: 0.444 | val_error: 10.4 | val_acc: 0.497
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [----------------------------------------] 1.800% | train_error: 11.6 | train_acc: 0.441 | val_error: 10.4 | val_acc: 0.497
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [----------------------------------------] 1.900% | train_error: 11.6 | train_acc: 0.441 | val_error: 10.4 | val_acc: 0.497
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [----------------------------------------] 2.000% | train_error: 11.6 | train_acc: 0.441 | val_error: 10.6 | val_acc: 0.490
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [----------------------------------------] 2.100% | train_error: 11.6 | train_acc: 0.439 | val_error: 10.6 | val_acc: 0.490
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [----------------------------------------] 2.200% | train_error: 11.6 | train_acc: 0.439 | val_error: 10.6 | val_acc: 0.490
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [----------------------------------------] 2.300% | train_error: 11.8 | train_acc: 0.430 | val_error: 10.6 | val_acc: 0.490
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [----------------------------------------] 2.400% | train_error: 11.9 | train_acc: 0.425 | val_error: 10.6 | val_acc: 0.490
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [&gt;---------------------------------------] 2.500% | train_error: 11.9 | train_acc: 0.425 | val_error: 10.6 | val_acc: 0.490
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [&gt;---------------------------------------] 2.600% | train_error: 11.9 | train_acc: 0.427 | val_error: 10.6 | val_acc: 0.490
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [&gt;---------------------------------------] 2.700% | train_error: 11.9 | train_acc: 0.427 | val_error: 10.6 | val_acc: 0.490
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [&gt;---------------------------------------] 2.800% | train_error: 11.9 | train_acc: 0.427 | val_error: 10.7 | val_acc: 0.483
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [&gt;---------------------------------------] 2.900% | train_error: 11.9 | train_acc: 0.427 | val_error: 10.7 | val_acc: 0.483
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [&gt;---------------------------------------] 3.000% | train_error: 11.9 | train_acc: 0.427 | val_error: 10.7 | val_acc: 0.483
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [&gt;---------------------------------------] 3.100% | train_error: 11.9 | train_acc: 0.427 | val_error: 10.7 | val_acc: 0.483
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [&gt;---------------------------------------] 3.200% | train_error: 11.9 | train_acc: 0.427 | val_error: 10.7 | val_acc: 0.483
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [&gt;---------------------------------------] 3.300% | train_error: 11.9 | train_acc: 0.425 | val_error: 10.9 | val_acc: 0.476
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [&gt;---------------------------------------] 3.400% | train_error: 11.9 | train_acc: 0.427 | val_error: 10.9 | val_acc: 0.476
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [&gt;---------------------------------------] 3.500% | train_error: 11.9 | train_acc: 0.427 | val_error: 11.0 | val_acc: 0.469
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [&gt;---------------------------------------] 3.600% | train_error: 11.8 | train_acc: 0.432 | val_error: 11.0 | val_acc: 0.469
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [&gt;---------------------------------------] 3.700% | train_error: 11.6 | train_acc: 0.439 | val_error: 11.0 | val_acc: 0.469
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [&gt;---------------------------------------] 3.800% | train_error: 11.6 | train_acc: 0.439 | val_error: 11.0 | val_acc: 0.469
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [&gt;---------------------------------------] 3.900% | train_error: 11.7 | train_acc: 0.434 | val_error: 11.3 | val_acc: 0.455
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [&gt;---------------------------------------] 4.000% | train_error: 11.7 | train_acc: 0.434 | val_error: 11.3 | val_acc: 0.455
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [&gt;---------------------------------------] 4.100% | train_error: 11.8 | train_acc: 0.430 | val_error: 11.2 | val_acc: 0.462
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [&gt;---------------------------------------] 4.200% | train_error: 11.9 | train_acc: 0.425 | val_error: 11.2 | val_acc: 0.462
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [&gt;---------------------------------------] 4.300% | train_error: 12.0 | train_acc: 0.423 | val_error: 11.2 | val_acc: 0.462
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [&gt;---------------------------------------] 4.400% | train_error: 11.9 | train_acc: 0.427 | val_error: 11.2 | val_acc: 0.462
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [&gt;---------------------------------------] 4.500% | train_error: 11.8 | train_acc: 0.430 | val_error: 11.2 | val_acc: 0.462
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [&gt;---------------------------------------] 4.600% | train_error: 11.8 | train_acc: 0.432 | val_error: 11.2 | val_acc: 0.462
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [&gt;---------------------------------------] 4.700% | train_error: 11.8 | train_acc: 0.430 | val_error: 11.3 | val_acc: 0.455
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [&gt;---------------------------------------] 4.800% | train_error: 11.7 | train_acc: 0.434 | val_error: 11.3 | val_acc: 0.455
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [&gt;---------------------------------------] 4.900% | train_error: 11.5 | train_acc: 0.446 | val_error: 11.3 | val_acc: 0.455
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=&gt;--------------------------------------] 5.000% | train_error: 11.5 | train_acc: 0.446 | val_error: 11.4 | val_acc: 0.448
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=&gt;--------------------------------------] 5.100% | train_error: 11.5 | train_acc: 0.446 | val_error: 11.3 | val_acc: 0.455
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=&gt;--------------------------------------] 5.200% | train_error: 11.5 | train_acc: 0.446 | val_error: 11.2 | val_acc: 0.462
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=&gt;--------------------------------------] 5.300% | train_error: 11.5 | train_acc: 0.444 | val_error: 11.0 | val_acc: 0.469
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=&gt;--------------------------------------] 5.400% | train_error: 11.5 | train_acc: 0.444 | val_error: 11.0 | val_acc: 0.469
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=&gt;--------------------------------------] 5.500% | train_error: 11.5 | train_acc: 0.444 | val_error: 11.0 | val_acc: 0.469
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=&gt;--------------------------------------] 5.600% | train_error: 11.5 | train_acc: 0.444 | val_error: 11.3 | val_acc: 0.455
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=&gt;--------------------------------------] 5.700% | train_error: 11.5 | train_acc: 0.444 | val_error: 11.3 | val_acc: 0.455
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=&gt;--------------------------------------] 5.800% | train_error: 11.9 | train_acc: 0.425 | val_error: 11.4 | val_acc: 0.448
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=&gt;--------------------------------------] 5.900% | train_error: 11.9 | train_acc: 0.425 | val_error: 11.4 | val_acc: 0.448
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=&gt;--------------------------------------] 6.000% | train_error: 12.0 | train_acc: 0.420 | val_error: 11.4 | val_acc: 0.448
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=&gt;--------------------------------------] 6.100% | train_error: 12.0 | train_acc: 0.420 | val_error: 11.4 | val_acc: 0.448
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=&gt;--------------------------------------] 6.200% | train_error: 12.1 | train_acc: 0.418 | val_error: 11.4 | val_acc: 0.448
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=&gt;--------------------------------------] 6.300% | train_error: 12.1 | train_acc: 0.418 | val_error: 11.4 | val_acc: 0.448
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=&gt;--------------------------------------] 6.400% | train_error: 11.9 | train_acc: 0.425 | val_error: 11.7 | val_acc: 0.434
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=&gt;--------------------------------------] 6.500% | train_error: 11.9 | train_acc: 0.427 | val_error: 11.9 | val_acc: 0.427
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=&gt;--------------------------------------] 6.600% | train_error: 11.8 | train_acc: 0.432 | val_error: 12.0 | val_acc: 0.420
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=&gt;--------------------------------------] 6.700% | train_error: 11.5 | train_acc: 0.444 | val_error: 12.2 | val_acc: 0.413
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=&gt;--------------------------------------] 6.800% | train_error: 11.4 | train_acc: 0.448 | val_error: 12.3 | val_acc: 0.406
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=&gt;--------------------------------------] 6.900% | train_error: 11.2 | train_acc: 0.458 | val_error: 12.5 | val_acc: 0.399
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=&gt;--------------------------------------] 7.000% | train_error: 11.2 | train_acc: 0.458 | val_error: 12.3 | val_acc: 0.406
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=&gt;--------------------------------------] 7.100% | train_error: 11.3 | train_acc: 0.455 | val_error: 12.5 | val_acc: 0.399
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=&gt;--------------------------------------] 7.200% | train_error: 11.3 | train_acc: 0.455 | val_error: 12.6 | val_acc: 0.392
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=&gt;--------------------------------------] 7.300% | train_error: 11.3 | train_acc: 0.455 | val_error: 12.5 | val_acc: 0.399
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=&gt;--------------------------------------] 7.400% | train_error: 11.3 | train_acc: 0.453 | val_error: 12.3 | val_acc: 0.406
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==&gt;-------------------------------------] 7.500% | train_error: 11.3 | train_acc: 0.455 | val_error: 12.5 | val_acc: 0.399
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==&gt;-------------------------------------] 7.600% | train_error: 11.5 | train_acc: 0.446 | val_error: 12.5 | val_acc: 0.399
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==&gt;-------------------------------------] 7.700% | train_error: 11.3 | train_acc: 0.453 | val_error: 12.5 | val_acc: 0.399
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==&gt;-------------------------------------] 7.800% | train_error: 11.4 | train_acc: 0.448 | val_error: 12.5 | val_acc: 0.399
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==&gt;-------------------------------------] 7.900% | train_error: 11.4 | train_acc: 0.448 | val_error: 12.3 | val_acc: 0.406
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==&gt;-------------------------------------] 8.000% | train_error: 11.4 | train_acc: 0.451 | val_error: 12.5 | val_acc: 0.399
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==&gt;-------------------------------------] 8.100% | train_error: 11.4 | train_acc: 0.448 | val_error: 12.5 | val_acc: 0.399
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==&gt;-------------------------------------] 8.200% | train_error: 11.3 | train_acc: 0.453 | val_error: 12.5 | val_acc: 0.399
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==&gt;-------------------------------------] 8.300% | train_error: 11.3 | train_acc: 0.453 | val_error: 12.5 | val_acc: 0.399
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==&gt;-------------------------------------] 8.400% | train_error: 11.4 | train_acc: 0.451 | val_error: 12.5 | val_acc: 0.399
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==&gt;-------------------------------------] 8.500% | train_error: 11.4 | train_acc: 0.451 | val_error: 12.5 | val_acc: 0.399
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==&gt;-------------------------------------] 8.600% | train_error: 11.4 | train_acc: 0.451 | val_error: 12.3 | val_acc: 0.406
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==&gt;-------------------------------------] 8.700% | train_error: 11.2 | train_acc: 0.458 | val_error: 12.3 | val_acc: 0.406
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==&gt;-------------------------------------] 8.800% | train_error: 11.2 | train_acc: 0.458 | val_error: 12.2 | val_acc: 0.413
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==&gt;-------------------------------------] 8.900% | train_error: 11.2 | train_acc: 0.458 | val_error: 12.0 | val_acc: 0.420
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==&gt;-------------------------------------] 9.000% | train_error: 11.2 | train_acc: 0.458 | val_error: 12.0 | val_acc: 0.420
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==&gt;-------------------------------------] 9.100% | train_error: 11.2 | train_acc: 0.458 | val_error: 11.9 | val_acc: 0.427
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==&gt;-------------------------------------] 9.200% | train_error: 11.2 | train_acc: 0.458 | val_error: 11.9 | val_acc: 0.427
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==&gt;-------------------------------------] 9.300% | train_error: 11.2 | train_acc: 0.460 | val_error: 11.9 | val_acc: 0.427
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==&gt;-------------------------------------] 9.400% | train_error: 11.0 | train_acc: 0.469 | val_error: 11.9 | val_acc: 0.427
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==&gt;-------------------------------------] 9.500% | train_error: 11.0 | train_acc: 0.469 | val_error: 11.9 | val_acc: 0.427
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==&gt;-------------------------------------] 9.600% | train_error: 10.8 | train_acc: 0.479 | val_error: 11.9 | val_acc: 0.427
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==&gt;-------------------------------------] 9.700% | train_error: 10.7 | train_acc: 0.484 | val_error: 11.7 | val_acc: 0.434
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==&gt;-------------------------------------] 9.800% | train_error: 10.7 | train_acc: 0.484 | val_error: 11.7 | val_acc: 0.434
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==&gt;-------------------------------------] 9.900% | train_error: 10.7 | train_acc: 0.484 | val_error: 11.7 | val_acc: 0.434
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===&gt;------------------------------------] 10.00% | train_error: 10.7 | train_acc: 0.484 | val_error: 11.7 | val_acc: 0.434
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===&gt;------------------------------------] 10.10% | train_error: 10.7 | train_acc: 0.486 | val_error: 11.7 | val_acc: 0.434
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===&gt;------------------------------------] 10.20% | train_error: 10.7 | train_acc: 0.486 | val_error: 11.6 | val_acc: 0.441
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===&gt;------------------------------------] 10.30% | train_error: 10.7 | train_acc: 0.486 | val_error: 11.6 | val_acc: 0.441
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===&gt;------------------------------------] 10.40% | train_error: 10.7 | train_acc: 0.486 | val_error: 11.4 | val_acc: 0.448
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===&gt;------------------------------------] 10.50% | train_error: 10.4 | train_acc: 0.500 | val_error: 11.4 | val_acc: 0.448
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===&gt;------------------------------------] 10.60% | train_error: 10.4 | train_acc: 0.500 | val_error: 11.4 | val_acc: 0.448
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===&gt;------------------------------------] 10.70% | train_error: 10.4 | train_acc: 0.500 | val_error: 11.4 | val_acc: 0.448
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===&gt;------------------------------------] 10.80% | train_error: 10.4 | train_acc: 0.500 | val_error: 11.4 | val_acc: 0.448
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===&gt;------------------------------------] 10.90% | train_error: 10.2 | train_acc: 0.507 | val_error: 11.3 | val_acc: 0.455
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===&gt;------------------------------------] 11.00% | train_error: 9.83 | train_acc: 0.526 | val_error: 11.3 | val_acc: 0.455
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===&gt;------------------------------------] 11.10% | train_error: 9.83 | train_acc: 0.526 | val_error: 11.3 | val_acc: 0.455
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===&gt;------------------------------------] 11.20% | train_error: 9.83 | train_acc: 0.526 | val_error: 11.3 | val_acc: 0.455
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===&gt;------------------------------------] 11.30% | train_error: 9.83 | train_acc: 0.526 | val_error: 11.2 | val_acc: 0.462
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===&gt;------------------------------------] 11.40% | train_error: 9.83 | train_acc: 0.526 | val_error: 11.2 | val_acc: 0.462
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===&gt;------------------------------------] 11.50% | train_error: 9.83 | train_acc: 0.526 | val_error: 11.2 | val_acc: 0.462
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===&gt;------------------------------------] 11.60% | train_error: 9.83 | train_acc: 0.526 | val_error: 11.2 | val_acc: 0.462
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===&gt;------------------------------------] 11.70% | train_error: 9.73 | train_acc: 0.531 | val_error: 11.2 | val_acc: 0.462
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===&gt;------------------------------------] 11.80% | train_error: 9.63 | train_acc: 0.535 | val_error: 11.2 | val_acc: 0.462
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===&gt;------------------------------------] 11.90% | train_error: 9.63 | train_acc: 0.535 | val_error: 11.2 | val_acc: 0.462
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===&gt;------------------------------------] 12.00% | train_error: 9.53 | train_acc: 0.540 | val_error: 10.9 | val_acc: 0.476
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===&gt;------------------------------------] 12.10% | train_error: 9.53 | train_acc: 0.540 | val_error: 10.9 | val_acc: 0.476
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===&gt;------------------------------------] 12.20% | train_error: 9.49 | train_acc: 0.542 | val_error: 10.9 | val_acc: 0.476
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===&gt;------------------------------------] 12.30% | train_error: 9.49 | train_acc: 0.542 | val_error: 10.9 | val_acc: 0.476
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===&gt;------------------------------------] 12.40% | train_error: 9.44 | train_acc: 0.545 | val_error: 10.9 | val_acc: 0.476
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [====&gt;-----------------------------------] 12.50% | train_error: 9.44 | train_acc: 0.545 | val_error: 10.9 | val_acc: 0.476
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [====&gt;-----------------------------------] 12.60% | train_error: 9.39 | train_acc: 0.547 | val_error: 10.9 | val_acc: 0.476
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [====&gt;-----------------------------------] 12.70% | train_error: 9.39 | train_acc: 0.547 | val_error: 10.7 | val_acc: 0.483
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [====&gt;-----------------------------------] 12.80% | train_error: 9.39 | train_acc: 0.547 | val_error: 10.7 | val_acc: 0.483
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [====&gt;-----------------------------------] 12.90% | train_error: 9.34 | train_acc: 0.549 | val_error: 10.7 | val_acc: 0.483
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [====&gt;-----------------------------------] 13.00% | train_error: 9.10 | train_acc: 0.561 | val_error: 10.6 | val_acc: 0.490
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [====&gt;-----------------------------------] 13.10% | train_error: 9.05 | train_acc: 0.563 | val_error: 10.6 | val_acc: 0.490
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [====&gt;-----------------------------------] 13.20% | train_error: 9.05 | train_acc: 0.563 | val_error: 10.6 | val_acc: 0.490
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [====&gt;-----------------------------------] 13.30% | train_error: 8.80 | train_acc: 0.575 | val_error: 10.6 | val_acc: 0.490
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [====&gt;-----------------------------------] 13.40% | train_error: 8.76 | train_acc: 0.577 | val_error: 10.4 | val_acc: 0.497
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [====&gt;-----------------------------------] 13.50% | train_error: 8.56 | train_acc: 0.587 | val_error: 10.4 | val_acc: 0.497
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [====&gt;-----------------------------------] 13.60% | train_error: 8.56 | train_acc: 0.587 | val_error: 10.4 | val_acc: 0.497
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [====&gt;-----------------------------------] 13.70% | train_error: 8.42 | train_acc: 0.594 | val_error: 10.4 | val_acc: 0.497
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [====&gt;-----------------------------------] 13.80% | train_error: 8.42 | train_acc: 0.594 | val_error: 10.3 | val_acc: 0.503
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [====&gt;-----------------------------------] 13.90% | train_error: 8.42 | train_acc: 0.594 | val_error: 10.1 | val_acc: 0.510
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [====&gt;-----------------------------------] 14.00% | train_error: 8.42 | train_acc: 0.594 | val_error: 10.1 | val_acc: 0.510
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [====&gt;-----------------------------------] 14.10% | train_error: 8.32 | train_acc: 0.599 | val_error: 10.1 | val_acc: 0.510
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [====&gt;-----------------------------------] 14.20% | train_error: 8.32 | train_acc: 0.599 | val_error: 10.1 | val_acc: 0.510
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [====&gt;-----------------------------------] 14.30% | train_error: 8.22 | train_acc: 0.603 | val_error: 10.1 | val_acc: 0.510
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [====&gt;-----------------------------------] 14.40% | train_error: 8.22 | train_acc: 0.603 | val_error: 10.1 | val_acc: 0.510
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [====&gt;-----------------------------------] 14.50% | train_error: 8.22 | train_acc: 0.603 | val_error: 10.1 | val_acc: 0.510
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [====&gt;-----------------------------------] 14.60% | train_error: 8.22 | train_acc: 0.603 | val_error: 10.1 | val_acc: 0.510
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [====&gt;-----------------------------------] 14.70% | train_error: 8.17 | train_acc: 0.606 | val_error: 10.1 | val_acc: 0.510
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [====&gt;-----------------------------------] 14.80% | train_error: 8.17 | train_acc: 0.606 | val_error: 10.1 | val_acc: 0.510
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [====&gt;-----------------------------------] 14.90% | train_error: 8.17 | train_acc: 0.606 | val_error: 10.1 | val_acc: 0.510
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=====&gt;----------------------------------] 15.00% | train_error: 8.08 | train_acc: 0.610 | val_error: 9.85 | val_acc: 0.524
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=====&gt;----------------------------------] 15.10% | train_error: 7.98 | train_acc: 0.615 | val_error: 9.71 | val_acc: 0.531
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=====&gt;----------------------------------] 15.20% | train_error: 7.93 | train_acc: 0.617 | val_error: 9.71 | val_acc: 0.531
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=====&gt;----------------------------------] 15.30% | train_error: 7.93 | train_acc: 0.617 | val_error: 9.71 | val_acc: 0.531
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=====&gt;----------------------------------] 15.40% | train_error: 7.93 | train_acc: 0.617 | val_error: 9.71 | val_acc: 0.531
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=====&gt;----------------------------------] 15.50% | train_error: 7.93 | train_acc: 0.617 | val_error: 9.71 | val_acc: 0.531
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=====&gt;----------------------------------] 15.60% | train_error: 7.93 | train_acc: 0.617 | val_error: 9.71 | val_acc: 0.531
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=====&gt;----------------------------------] 15.70% | train_error: 7.93 | train_acc: 0.617 | val_error: 9.71 | val_acc: 0.531
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=====&gt;----------------------------------] 15.80% | train_error: 7.93 | train_acc: 0.617 | val_error: 9.56 | val_acc: 0.538
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=====&gt;----------------------------------] 15.90% | train_error: 7.93 | train_acc: 0.617 | val_error: 9.27 | val_acc: 0.552
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=====&gt;----------------------------------] 16.00% | train_error: 7.93 | train_acc: 0.617 | val_error: 9.27 | val_acc: 0.552
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=====&gt;----------------------------------] 16.10% | train_error: 7.93 | train_acc: 0.617 | val_error: 9.27 | val_acc: 0.552
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=====&gt;----------------------------------] 16.20% | train_error: 7.88 | train_acc: 0.620 | val_error: 9.27 | val_acc: 0.552
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=====&gt;----------------------------------] 16.30% | train_error: 7.69 | train_acc: 0.629 | val_error: 9.27 | val_acc: 0.552
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=====&gt;----------------------------------] 16.40% | train_error: 7.69 | train_acc: 0.629 | val_error: 9.13 | val_acc: 0.559
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=====&gt;----------------------------------] 16.50% | train_error: 7.69 | train_acc: 0.629 | val_error: 9.13 | val_acc: 0.559
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=====&gt;----------------------------------] 16.60% | train_error: 7.69 | train_acc: 0.629 | val_error: 9.13 | val_acc: 0.559
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=====&gt;----------------------------------] 16.70% | train_error: 7.69 | train_acc: 0.629 | val_error: 8.98 | val_acc: 0.566
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=====&gt;----------------------------------] 16.80% | train_error: 7.59 | train_acc: 0.634 | val_error: 8.84 | val_acc: 0.573
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=====&gt;----------------------------------] 16.90% | train_error: 7.49 | train_acc: 0.638 | val_error: 8.84 | val_acc: 0.573
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=====&gt;----------------------------------] 17.00% | train_error: 7.39 | train_acc: 0.643 | val_error: 8.70 | val_acc: 0.580
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=====&gt;----------------------------------] 17.10% | train_error: 7.39 | train_acc: 0.643 | val_error: 8.55 | val_acc: 0.587
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=====&gt;----------------------------------] 17.20% | train_error: 7.30 | train_acc: 0.648 | val_error: 8.41 | val_acc: 0.594
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=====&gt;----------------------------------] 17.30% | train_error: 7.30 | train_acc: 0.648 | val_error: 8.26 | val_acc: 0.601
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=====&gt;----------------------------------] 17.40% | train_error: 7.15 | train_acc: 0.655 | val_error: 8.26 | val_acc: 0.601
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [======&gt;---------------------------------] 17.50% | train_error: 7.05 | train_acc: 0.660 | val_error: 8.12 | val_acc: 0.608
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [======&gt;---------------------------------] 17.60% | train_error: 7.05 | train_acc: 0.660 | val_error: 7.97 | val_acc: 0.615
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [======&gt;---------------------------------] 17.70% | train_error: 6.91 | train_acc: 0.667 | val_error: 7.97 | val_acc: 0.615
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [======&gt;---------------------------------] 17.80% | train_error: 6.86 | train_acc: 0.669 | val_error: 7.83 | val_acc: 0.622
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [======&gt;---------------------------------] 17.90% | train_error: 6.86 | train_acc: 0.669 | val_error: 7.83 | val_acc: 0.622
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [======&gt;---------------------------------] 18.00% | train_error: 6.86 | train_acc: 0.669 | val_error: 7.54 | val_acc: 0.636
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [======&gt;---------------------------------] 18.10% | train_error: 6.76 | train_acc: 0.674 | val_error: 7.54 | val_acc: 0.636
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [======&gt;---------------------------------] 18.20% | train_error: 6.76 | train_acc: 0.674 | val_error: 7.54 | val_acc: 0.636
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [======&gt;---------------------------------] 18.30% | train_error: 6.62 | train_acc: 0.681 | val_error: 7.54 | val_acc: 0.636
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [======&gt;---------------------------------] 18.40% | train_error: 6.57 | train_acc: 0.683 | val_error: 7.54 | val_acc: 0.636
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [======&gt;---------------------------------] 18.50% | train_error: 6.47 | train_acc: 0.688 | val_error: 7.54 | val_acc: 0.636
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [======&gt;---------------------------------] 18.60% | train_error: 6.47 | train_acc: 0.688 | val_error: 7.54 | val_acc: 0.636
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [======&gt;---------------------------------] 18.70% | train_error: 6.37 | train_acc: 0.692 | val_error: 7.54 | val_acc: 0.636
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [======&gt;---------------------------------] 18.80% | train_error: 6.32 | train_acc: 0.695 | val_error: 7.54 | val_acc: 0.636
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [======&gt;---------------------------------] 18.90% | train_error: 6.28 | train_acc: 0.697 | val_error: 7.54 | val_acc: 0.636
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [======&gt;---------------------------------] 19.00% | train_error: 6.28 | train_acc: 0.697 | val_error: 7.54 | val_acc: 0.636
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [======&gt;---------------------------------] 19.10% | train_error: 6.28 | train_acc: 0.697 | val_error: 7.39 | val_acc: 0.643
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [======&gt;---------------------------------] 19.20% | train_error: 6.28 | train_acc: 0.697 | val_error: 7.39 | val_acc: 0.643
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [======&gt;---------------------------------] 19.30% | train_error: 6.28 | train_acc: 0.697 | val_error: 7.10 | val_acc: 0.657
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [======&gt;---------------------------------] 19.40% | train_error: 6.18 | train_acc: 0.702 | val_error: 7.10 | val_acc: 0.657
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [======&gt;---------------------------------] 19.50% | train_error: 6.18 | train_acc: 0.702 | val_error: 6.81 | val_acc: 0.671
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [======&gt;---------------------------------] 19.60% | train_error: 6.13 | train_acc: 0.704 | val_error: 6.81 | val_acc: 0.671
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [======&gt;---------------------------------] 19.70% | train_error: 6.13 | train_acc: 0.704 | val_error: 6.67 | val_acc: 0.678
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [======&gt;---------------------------------] 19.80% | train_error: 6.13 | train_acc: 0.704 | val_error: 6.52 | val_acc: 0.685
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [======&gt;---------------------------------] 19.90% | train_error: 6.13 | train_acc: 0.704 | val_error: 6.52 | val_acc: 0.685
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=======&gt;--------------------------------] 20.00% | train_error: 6.13 | train_acc: 0.704 | val_error: 6.52 | val_acc: 0.685
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=======&gt;--------------------------------] 20.10% | train_error: 6.08 | train_acc: 0.707 | val_error: 6.38 | val_acc: 0.692
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=======&gt;--------------------------------] 20.20% | train_error: 6.03 | train_acc: 0.709 | val_error: 6.52 | val_acc: 0.685
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=======&gt;--------------------------------] 20.30% | train_error: 5.64 | train_acc: 0.728 | val_error: 6.52 | val_acc: 0.685
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=======&gt;--------------------------------] 20.40% | train_error: 5.69 | train_acc: 0.725 | val_error: 6.52 | val_acc: 0.685
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=======&gt;--------------------------------] 20.50% | train_error: 5.69 | train_acc: 0.725 | val_error: 6.52 | val_acc: 0.685
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=======&gt;--------------------------------] 20.60% | train_error: 5.59 | train_acc: 0.730 | val_error: 6.52 | val_acc: 0.685
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=======&gt;--------------------------------] 20.70% | train_error: 5.59 | train_acc: 0.730 | val_error: 6.67 | val_acc: 0.678
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=======&gt;--------------------------------] 20.80% | train_error: 5.55 | train_acc: 0.732 | val_error: 6.67 | val_acc: 0.678
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=======&gt;--------------------------------] 20.90% | train_error: 5.50 | train_acc: 0.735 | val_error: 6.52 | val_acc: 0.685
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=======&gt;--------------------------------] 21.00% | train_error: 5.35 | train_acc: 0.742 | val_error: 6.38 | val_acc: 0.692
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=======&gt;--------------------------------] 21.10% | train_error: 5.35 | train_acc: 0.742 | val_error: 6.23 | val_acc: 0.699
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=======&gt;--------------------------------] 21.20% | train_error: 5.30 | train_acc: 0.744 | val_error: 6.23 | val_acc: 0.699
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=======&gt;--------------------------------] 21.30% | train_error: 5.21 | train_acc: 0.749 | val_error: 6.23 | val_acc: 0.699
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=======&gt;--------------------------------] 21.40% | train_error: 5.06 | train_acc: 0.756 | val_error: 6.23 | val_acc: 0.699
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=======&gt;--------------------------------] 21.50% | train_error: 5.06 | train_acc: 0.756 | val_error: 6.09 | val_acc: 0.706
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=======&gt;--------------------------------] 21.60% | train_error: 5.06 | train_acc: 0.756 | val_error: 6.09 | val_acc: 0.706
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=======&gt;--------------------------------] 21.70% | train_error: 5.01 | train_acc: 0.758 | val_error: 6.09 | val_acc: 0.706
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=======&gt;--------------------------------] 21.80% | train_error: 5.01 | train_acc: 0.758 | val_error: 6.09 | val_acc: 0.706
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=======&gt;--------------------------------] 21.90% | train_error: 4.96 | train_acc: 0.761 | val_error: 6.09 | val_acc: 0.706
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=======&gt;--------------------------------] 22.00% | train_error: 4.96 | train_acc: 0.761 | val_error: 5.65 | val_acc: 0.727
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=======&gt;--------------------------------] 22.10% | train_error: 4.72 | train_acc: 0.772 | val_error: 5.65 | val_acc: 0.727
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=======&gt;--------------------------------] 22.20% | train_error: 4.62 | train_acc: 0.777 | val_error: 5.65 | val_acc: 0.727
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=======&gt;--------------------------------] 22.30% | train_error: 4.52 | train_acc: 0.782 | val_error: 5.65 | val_acc: 0.727
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=======&gt;--------------------------------] 22.40% | train_error: 4.62 | train_acc: 0.777 | val_error: 5.65 | val_acc: 0.727
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [========&gt;-------------------------------] 22.50% | train_error: 4.52 | train_acc: 0.782 | val_error: 5.51 | val_acc: 0.734
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [========&gt;-------------------------------] 22.60% | train_error: 4.57 | train_acc: 0.779 | val_error: 5.65 | val_acc: 0.727
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [========&gt;-------------------------------] 22.70% | train_error: 4.52 | train_acc: 0.782 | val_error: 5.65 | val_acc: 0.727
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [========&gt;-------------------------------] 22.80% | train_error: 4.33 | train_acc: 0.791 | val_error: 5.51 | val_acc: 0.734
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [========&gt;-------------------------------] 22.90% | train_error: 4.33 | train_acc: 0.791 | val_error: 5.36 | val_acc: 0.741
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [========&gt;-------------------------------] 23.00% | train_error: 4.33 | train_acc: 0.791 | val_error: 5.36 | val_acc: 0.741
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [========&gt;-------------------------------] 23.10% | train_error: 4.33 | train_acc: 0.791 | val_error: 5.36 | val_acc: 0.741
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [========&gt;-------------------------------] 23.20% | train_error: 4.33 | train_acc: 0.791 | val_error: 5.07 | val_acc: 0.755
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [========&gt;-------------------------------] 23.30% | train_error: 4.33 | train_acc: 0.791 | val_error: 5.07 | val_acc: 0.755
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [========&gt;-------------------------------] 23.40% | train_error: 4.28 | train_acc: 0.793 | val_error: 5.07 | val_acc: 0.755
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [========&gt;-------------------------------] 23.50% | train_error: 4.28 | train_acc: 0.793 | val_error: 5.07 | val_acc: 0.755
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [========&gt;-------------------------------] 23.60% | train_error: 4.13 | train_acc: 0.800 | val_error: 5.07 | val_acc: 0.755
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [========&gt;-------------------------------] 23.70% | train_error: 4.04 | train_acc: 0.805 | val_error: 4.93 | val_acc: 0.762
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [========&gt;-------------------------------] 23.80% | train_error: 4.04 | train_acc: 0.805 | val_error: 4.78 | val_acc: 0.769
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [========&gt;-------------------------------] 23.90% | train_error: 4.04 | train_acc: 0.805 | val_error: 4.78 | val_acc: 0.769
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [========&gt;-------------------------------] 24.00% | train_error: 3.89 | train_acc: 0.812 | val_error: 4.64 | val_acc: 0.776
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [========&gt;-------------------------------] 24.10% | train_error: 3.84 | train_acc: 0.815 | val_error: 4.64 | val_acc: 0.776
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [========&gt;-------------------------------] 24.20% | train_error: 3.79 | train_acc: 0.817 | val_error: 4.64 | val_acc: 0.776
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [========&gt;-------------------------------] 24.30% | train_error: 3.70 | train_acc: 0.822 | val_error: 4.64 | val_acc: 0.776
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [========&gt;-------------------------------] 24.40% | train_error: 3.70 | train_acc: 0.822 | val_error: 4.49 | val_acc: 0.783
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [========&gt;-------------------------------] 24.50% | train_error: 3.65 | train_acc: 0.824 | val_error: 4.35 | val_acc: 0.790
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [========&gt;-------------------------------] 24.60% | train_error: 3.55 | train_acc: 0.829 | val_error: 4.35 | val_acc: 0.790
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [========&gt;-------------------------------] 24.70% | train_error: 3.55 | train_acc: 0.829 | val_error: 4.35 | val_acc: 0.790
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [========&gt;-------------------------------] 24.80% | train_error: 3.50 | train_acc: 0.831 | val_error: 4.64 | val_acc: 0.776
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [========&gt;-------------------------------] 24.90% | train_error: 3.45 | train_acc: 0.833 | val_error: 4.64 | val_acc: 0.776
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=========&gt;------------------------------] 25.00% | train_error: 3.21 | train_acc: 0.845 | val_error: 4.35 | val_acc: 0.790
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=========&gt;------------------------------] 25.10% | train_error: 3.21 | train_acc: 0.845 | val_error: 4.20 | val_acc: 0.797
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=========&gt;------------------------------] 25.20% | train_error: 3.11 | train_acc: 0.850 | val_error: 4.20 | val_acc: 0.797
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=========&gt;------------------------------] 25.30% | train_error: 3.11 | train_acc: 0.850 | val_error: 4.35 | val_acc: 0.790
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=========&gt;------------------------------] 25.40% | train_error: 3.11 | train_acc: 0.850 | val_error: 4.06 | val_acc: 0.804
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=========&gt;------------------------------] 25.50% | train_error: 3.11 | train_acc: 0.850 | val_error: 3.91 | val_acc: 0.811
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=========&gt;------------------------------] 25.60% | train_error: 3.11 | train_acc: 0.850 | val_error: 3.77 | val_acc: 0.818
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=========&gt;------------------------------] 25.70% | train_error: 3.11 | train_acc: 0.850 | val_error: 3.77 | val_acc: 0.818
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=========&gt;------------------------------] 25.80% | train_error: 3.11 | train_acc: 0.850 | val_error: 3.77 | val_acc: 0.818
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=========&gt;------------------------------] 25.90% | train_error: 3.11 | train_acc: 0.850 | val_error: 3.77 | val_acc: 0.818
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=========&gt;------------------------------] 26.00% | train_error: 3.21 | train_acc: 0.845 | val_error: 3.91 | val_acc: 0.811
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=========&gt;------------------------------] 26.10% | train_error: 3.21 | train_acc: 0.845 | val_error: 3.91 | val_acc: 0.811
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=========&gt;------------------------------] 26.20% | train_error: 3.21 | train_acc: 0.845 | val_error: 3.91 | val_acc: 0.811
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=========&gt;------------------------------] 26.30% | train_error: 3.21 | train_acc: 0.845 | val_error: 3.91 | val_acc: 0.811
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=========&gt;------------------------------] 26.40% | train_error: 3.21 | train_acc: 0.845 | val_error: 3.91 | val_acc: 0.811
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=========&gt;------------------------------] 26.50% | train_error: 3.02 | train_acc: 0.854 | val_error: 3.91 | val_acc: 0.811
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=========&gt;------------------------------] 26.60% | train_error: 2.77 | train_acc: 0.866 | val_error: 3.91 | val_acc: 0.811
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=========&gt;------------------------------] 26.70% | train_error: 2.77 | train_acc: 0.866 | val_error: 3.91 | val_acc: 0.811
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=========&gt;------------------------------] 26.80% | train_error: 2.72 | train_acc: 0.869 | val_error: 3.91 | val_acc: 0.811
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=========&gt;------------------------------] 26.90% | train_error: 2.68 | train_acc: 0.871 | val_error: 3.77 | val_acc: 0.818
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=========&gt;------------------------------] 27.00% | train_error: 2.53 | train_acc: 0.878 | val_error: 3.77 | val_acc: 0.818
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=========&gt;------------------------------] 27.10% | train_error: 2.48 | train_acc: 0.880 | val_error: 3.77 | val_acc: 0.818
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=========&gt;------------------------------] 27.20% | train_error: 2.48 | train_acc: 0.880 | val_error: 3.77 | val_acc: 0.818
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=========&gt;------------------------------] 27.30% | train_error: 2.38 | train_acc: 0.885 | val_error: 3.77 | val_acc: 0.818
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=========&gt;------------------------------] 27.40% | train_error: 2.38 | train_acc: 0.885 | val_error: 3.77 | val_acc: 0.818
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==========&gt;-----------------------------] 27.50% | train_error: 2.38 | train_acc: 0.885 | val_error: 3.62 | val_acc: 0.825
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==========&gt;-----------------------------] 27.60% | train_error: 2.29 | train_acc: 0.890 | val_error: 3.62 | val_acc: 0.825
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==========&gt;-----------------------------] 27.70% | train_error: 2.24 | train_acc: 0.892 | val_error: 3.62 | val_acc: 0.825
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==========&gt;-----------------------------] 27.80% | train_error: 2.24 | train_acc: 0.892 | val_error: 3.62 | val_acc: 0.825
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==========&gt;-----------------------------] 27.90% | train_error: 2.19 | train_acc: 0.894 | val_error: 3.48 | val_acc: 0.832
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==========&gt;-----------------------------] 28.00% | train_error: 2.19 | train_acc: 0.894 | val_error: 3.33 | val_acc: 0.839
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==========&gt;-----------------------------] 28.10% | train_error: 2.19 | train_acc: 0.894 | val_error: 3.19 | val_acc: 0.846
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==========&gt;-----------------------------] 28.20% | train_error: 2.19 | train_acc: 0.894 | val_error: 3.04 | val_acc: 0.853
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==========&gt;-----------------------------] 28.30% | train_error: 2.19 | train_acc: 0.894 | val_error: 2.90 | val_acc: 0.860
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==========&gt;-----------------------------] 28.40% | train_error: 2.14 | train_acc: 0.897 | val_error: 2.90 | val_acc: 0.860
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==========&gt;-----------------------------] 28.50% | train_error: 2.09 | train_acc: 0.899 | val_error: 2.90 | val_acc: 0.860
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==========&gt;-----------------------------] 28.60% | train_error: 2.04 | train_acc: 0.901 | val_error: 2.90 | val_acc: 0.860
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==========&gt;-----------------------------] 28.70% | train_error: 2.09 | train_acc: 0.899 | val_error: 2.90 | val_acc: 0.860
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==========&gt;-----------------------------] 28.80% | train_error: 2.09 | train_acc: 0.899 | val_error: 2.90 | val_acc: 0.860
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==========&gt;-----------------------------] 28.90% | train_error: 2.09 | train_acc: 0.899 | val_error: 2.90 | val_acc: 0.860
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==========&gt;-----------------------------] 29.00% | train_error: 2.09 | train_acc: 0.899 | val_error: 3.04 | val_acc: 0.853
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==========&gt;-----------------------------] 29.10% | train_error: 2.09 | train_acc: 0.899 | val_error: 3.04 | val_acc: 0.853
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==========&gt;-----------------------------] 29.20% | train_error: 2.09 | train_acc: 0.899 | val_error: 3.04 | val_acc: 0.853
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==========&gt;-----------------------------] 29.30% | train_error: 2.04 | train_acc: 0.901 | val_error: 3.04 | val_acc: 0.853
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==========&gt;-----------------------------] 29.40% | train_error: 2.04 | train_acc: 0.901 | val_error: 3.04 | val_acc: 0.853
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==========&gt;-----------------------------] 29.50% | train_error: 2.04 | train_acc: 0.901 | val_error: 3.04 | val_acc: 0.853
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==========&gt;-----------------------------] 29.60% | train_error: 2.04 | train_acc: 0.901 | val_error: 3.04 | val_acc: 0.853
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==========&gt;-----------------------------] 29.70% | train_error: 2.04 | train_acc: 0.901 | val_error: 3.04 | val_acc: 0.853
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==========&gt;-----------------------------] 29.80% | train_error: 1.99 | train_acc: 0.904 | val_error: 3.04 | val_acc: 0.853
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==========&gt;-----------------------------] 29.90% | train_error: 1.95 | train_acc: 0.906 | val_error: 3.04 | val_acc: 0.853
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===========&gt;----------------------------] 30.00% | train_error: 1.90 | train_acc: 0.908 | val_error: 3.04 | val_acc: 0.853
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===========&gt;----------------------------] 30.10% | train_error: 1.90 | train_acc: 0.908 | val_error: 3.04 | val_acc: 0.853
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===========&gt;----------------------------] 30.20% | train_error: 1.90 | train_acc: 0.908 | val_error: 3.19 | val_acc: 0.846
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===========&gt;----------------------------] 30.30% | train_error: 1.90 | train_acc: 0.908 | val_error: 3.19 | val_acc: 0.846
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===========&gt;----------------------------] 30.40% | train_error: 1.90 | train_acc: 0.908 | val_error: 3.19 | val_acc: 0.846
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===========&gt;----------------------------] 30.50% | train_error: 1.90 | train_acc: 0.908 | val_error: 3.19 | val_acc: 0.846
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===========&gt;----------------------------] 30.60% | train_error: 1.90 | train_acc: 0.908 | val_error: 3.19 | val_acc: 0.846
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===========&gt;----------------------------] 30.70% | train_error: 1.90 | train_acc: 0.908 | val_error: 3.04 | val_acc: 0.853
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===========&gt;----------------------------] 30.80% | train_error: 1.90 | train_acc: 0.908 | val_error: 3.04 | val_acc: 0.853
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===========&gt;----------------------------] 30.90% | train_error: 1.90 | train_acc: 0.908 | val_error: 3.04 | val_acc: 0.853
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===========&gt;----------------------------] 31.00% | train_error: 1.75 | train_acc: 0.915 | val_error: 3.04 | val_acc: 0.853
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===========&gt;----------------------------] 31.10% | train_error: 1.75 | train_acc: 0.915 | val_error: 3.04 | val_acc: 0.853
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===========&gt;----------------------------] 31.20% | train_error: 1.61 | train_acc: 0.923 | val_error: 3.04 | val_acc: 0.853
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===========&gt;----------------------------] 31.30% | train_error: 1.61 | train_acc: 0.923 | val_error: 3.04 | val_acc: 0.853
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===========&gt;----------------------------] 31.40% | train_error: 1.56 | train_acc: 0.925 | val_error: 3.04 | val_acc: 0.853
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===========&gt;----------------------------] 31.50% | train_error: 1.56 | train_acc: 0.925 | val_error: 3.04 | val_acc: 0.853
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===========&gt;----------------------------] 31.60% | train_error: 1.56 | train_acc: 0.925 | val_error: 3.04 | val_acc: 0.853
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===========&gt;----------------------------] 31.70% | train_error: 1.51 | train_acc: 0.927 | val_error: 3.04 | val_acc: 0.853
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===========&gt;----------------------------] 31.80% | train_error: 1.51 | train_acc: 0.927 | val_error: 3.04 | val_acc: 0.853
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===========&gt;----------------------------] 31.90% | train_error: 1.51 | train_acc: 0.927 | val_error: 3.04 | val_acc: 0.853
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===========&gt;----------------------------] 32.00% | train_error: 1.51 | train_acc: 0.927 | val_error: 3.04 | val_acc: 0.853
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===========&gt;----------------------------] 32.10% | train_error: 1.41 | train_acc: 0.932 | val_error: 2.90 | val_acc: 0.860
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===========&gt;----------------------------] 32.20% | train_error: 1.41 | train_acc: 0.932 | val_error: 2.90 | val_acc: 0.860
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===========&gt;----------------------------] 32.30% | train_error: 1.41 | train_acc: 0.932 | val_error: 2.90 | val_acc: 0.860
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===========&gt;----------------------------] 32.40% | train_error: 1.41 | train_acc: 0.932 | val_error: 2.90 | val_acc: 0.860
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [============&gt;---------------------------] 32.50% | train_error: 1.41 | train_acc: 0.932 | val_error: 2.90 | val_acc: 0.860
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [============&gt;---------------------------] 32.60% | train_error: 1.41 | train_acc: 0.932 | val_error: 2.90 | val_acc: 0.860
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [============&gt;---------------------------] 32.70% | train_error: 1.41 | train_acc: 0.932 | val_error: 2.90 | val_acc: 0.860
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [============&gt;---------------------------] 32.80% | train_error: 1.31 | train_acc: 0.937 | val_error: 3.04 | val_acc: 0.853
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [============&gt;---------------------------] 32.90% | train_error: 1.26 | train_acc: 0.939 | val_error: 3.04 | val_acc: 0.853
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [============&gt;---------------------------] 33.00% | train_error: 1.22 | train_acc: 0.941 | val_error: 2.90 | val_acc: 0.860
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [============&gt;---------------------------] 33.10% | train_error: 1.22 | train_acc: 0.941 | val_error: 2.90 | val_acc: 0.860
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [============&gt;---------------------------] 33.20% | train_error: 1.22 | train_acc: 0.941 | val_error: 2.90 | val_acc: 0.860
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [============&gt;---------------------------] 33.30% | train_error: 1.22 | train_acc: 0.941 | val_error: 2.90 | val_acc: 0.860
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [============&gt;---------------------------] 33.40% | train_error: 1.22 | train_acc: 0.941 | val_error: 2.75 | val_acc: 0.867
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [============&gt;---------------------------] 33.50% | train_error: 1.22 | train_acc: 0.941 | val_error: 2.75 | val_acc: 0.867
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [============&gt;---------------------------] 33.60% | train_error: 1.22 | train_acc: 0.941 | val_error: 2.75 | val_acc: 0.867
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [============&gt;---------------------------] 33.70% | train_error: 1.22 | train_acc: 0.941 | val_error: 2.75 | val_acc: 0.867
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [============&gt;---------------------------] 33.80% | train_error: 1.22 | train_acc: 0.941 | val_error: 2.75 | val_acc: 0.867
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [============&gt;---------------------------] 33.90% | train_error: 1.22 | train_acc: 0.941 | val_error: 2.75 | val_acc: 0.867
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [============&gt;---------------------------] 34.00% | train_error: 1.22 | train_acc: 0.941 | val_error: 2.61 | val_acc: 0.874
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [============&gt;---------------------------] 34.10% | train_error: 1.22 | train_acc: 0.941 | val_error: 2.61 | val_acc: 0.874
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [============&gt;---------------------------] 34.20% | train_error: 1.22 | train_acc: 0.941 | val_error: 2.61 | val_acc: 0.874
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [============&gt;---------------------------] 34.30% | train_error: 1.22 | train_acc: 0.941 | val_error: 2.61 | val_acc: 0.874
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [============&gt;---------------------------] 34.40% | train_error: 1.22 | train_acc: 0.941 | val_error: 2.61 | val_acc: 0.874
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [============&gt;---------------------------] 34.50% | train_error: 1.22 | train_acc: 0.941 | val_error: 2.75 | val_acc: 0.867
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [============&gt;---------------------------] 34.60% | train_error: 1.22 | train_acc: 0.941 | val_error: 2.75 | val_acc: 0.867
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [============&gt;---------------------------] 34.70% | train_error: 1.22 | train_acc: 0.941 | val_error: 2.75 | val_acc: 0.867
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [============&gt;---------------------------] 34.80% | train_error: 1.22 | train_acc: 0.941 | val_error: 2.75 | val_acc: 0.867
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [============&gt;---------------------------] 34.90% | train_error: 1.22 | train_acc: 0.941 | val_error: 2.75 | val_acc: 0.867
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=============&gt;--------------------------] 35.00% | train_error: 1.02 | train_acc: 0.951 | val_error: 2.75 | val_acc: 0.867
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=============&gt;--------------------------] 35.10% | train_error: 1.02 | train_acc: 0.951 | val_error: 2.75 | val_acc: 0.867
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=============&gt;--------------------------] 35.20% | train_error: 1.02 | train_acc: 0.951 | val_error: 2.75 | val_acc: 0.867
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=============&gt;--------------------------] 35.30% | train_error: 1.02 | train_acc: 0.951 | val_error: 2.61 | val_acc: 0.874
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=============&gt;--------------------------] 35.40% | train_error: 1.02 | train_acc: 0.951 | val_error: 2.61 | val_acc: 0.874
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=============&gt;--------------------------] 35.50% | train_error: 1.02 | train_acc: 0.951 | val_error: 2.61 | val_acc: 0.874
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=============&gt;--------------------------] 35.60% | train_error: 1.02 | train_acc: 0.951 | val_error: 2.61 | val_acc: 0.874
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=============&gt;--------------------------] 35.70% | train_error: 1.02 | train_acc: 0.951 | val_error: 2.61 | val_acc: 0.874
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=============&gt;--------------------------] 35.80% | train_error: 1.02 | train_acc: 0.951 | val_error: 2.61 | val_acc: 0.874
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=============&gt;--------------------------] 35.90% | train_error: 0.876 | train_acc: 0.958 | val_error: 2.46 | val_acc: 0.881
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=============&gt;--------------------------] 36.00% | train_error: 0.876 | train_acc: 0.958 | val_error: 2.46 | val_acc: 0.881
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=============&gt;--------------------------] 36.10% | train_error: 0.778 | train_acc: 0.962 | val_error: 2.46 | val_acc: 0.881
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=============&gt;--------------------------] 36.20% | train_error: 0.778 | train_acc: 0.962 | val_error: 2.46 | val_acc: 0.881
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=============&gt;--------------------------] 36.30% | train_error: 0.778 | train_acc: 0.962 | val_error: 2.46 | val_acc: 0.881
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=============&gt;--------------------------] 36.40% | train_error: 0.730 | train_acc: 0.965 | val_error: 2.46 | val_acc: 0.881
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=============&gt;--------------------------] 36.50% | train_error: 0.730 | train_acc: 0.965 | val_error: 2.46 | val_acc: 0.881
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=============&gt;--------------------------] 36.60% | train_error: 0.730 | train_acc: 0.965 | val_error: 2.46 | val_acc: 0.881
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=============&gt;--------------------------] 36.70% | train_error: 0.730 | train_acc: 0.965 | val_error: 2.46 | val_acc: 0.881
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=============&gt;--------------------------] 36.80% | train_error: 0.730 | train_acc: 0.965 | val_error: 2.46 | val_acc: 0.881
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=============&gt;--------------------------] 36.90% | train_error: 0.730 | train_acc: 0.965 | val_error: 2.46 | val_acc: 0.881
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=============&gt;--------------------------] 37.00% | train_error: 0.681 | train_acc: 0.967 | val_error: 2.46 | val_acc: 0.881
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=============&gt;--------------------------] 37.10% | train_error: 0.681 | train_acc: 0.967 | val_error: 2.46 | val_acc: 0.881
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=============&gt;--------------------------] 37.20% | train_error: 0.681 | train_acc: 0.967 | val_error: 2.46 | val_acc: 0.881
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=============&gt;--------------------------] 37.30% | train_error: 0.632 | train_acc: 0.969 | val_error: 2.46 | val_acc: 0.881
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=============&gt;--------------------------] 37.40% | train_error: 0.632 | train_acc: 0.969 | val_error: 2.46 | val_acc: 0.881
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==============&gt;-------------------------] 37.50% | train_error: 0.632 | train_acc: 0.969 | val_error: 2.46 | val_acc: 0.881
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==============&gt;-------------------------] 37.60% | train_error: 0.632 | train_acc: 0.969 | val_error: 2.46 | val_acc: 0.881
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==============&gt;-------------------------] 37.70% | train_error: 0.632 | train_acc: 0.969 | val_error: 2.46 | val_acc: 0.881
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==============&gt;-------------------------] 37.80% | train_error: 0.632 | train_acc: 0.969 | val_error: 2.46 | val_acc: 0.881
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==============&gt;-------------------------] 37.90% | train_error: 0.632 | train_acc: 0.969 | val_error: 2.46 | val_acc: 0.881
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==============&gt;-------------------------] 38.00% | train_error: 0.535 | train_acc: 0.974 | val_error: 2.46 | val_acc: 0.881
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==============&gt;-------------------------] 38.10% | train_error: 0.535 | train_acc: 0.974 | val_error: 2.46 | val_acc: 0.881
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==============&gt;-------------------------] 38.20% | train_error: 0.486 | train_acc: 0.977 | val_error: 2.46 | val_acc: 0.881
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==============&gt;-------------------------] 38.30% | train_error: 0.341 | train_acc: 0.984 | val_error: 2.46 | val_acc: 0.881
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==============&gt;-------------------------] 38.40% | train_error: 0.486 | train_acc: 0.977 | val_error: 2.46 | val_acc: 0.881
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==============&gt;-------------------------] 38.50% | train_error: 0.341 | train_acc: 0.984 | val_error: 2.46 | val_acc: 0.881
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==============&gt;-------------------------] 38.60% | train_error: 0.486 | train_acc: 0.977 | val_error: 2.46 | val_acc: 0.881
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==============&gt;-------------------------] 38.70% | train_error: 0.341 | train_acc: 0.984 | val_error: 2.46 | val_acc: 0.881
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==============&gt;-------------------------] 38.80% | train_error: 0.486 | train_acc: 0.977 | val_error: 2.46 | val_acc: 0.881
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==============&gt;-------------------------] 38.90% | train_error: 0.341 | train_acc: 0.984 | val_error: 2.46 | val_acc: 0.881
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==============&gt;-------------------------] 39.00% | train_error: 0.486 | train_acc: 0.977 | val_error: 2.46 | val_acc: 0.881
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==============&gt;-------------------------] 39.10% | train_error: 0.292 | train_acc: 0.986 | val_error: 2.46 | val_acc: 0.881
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==============&gt;-------------------------] 39.20% | train_error: 0.292 | train_acc: 0.986 | val_error: 2.46 | val_acc: 0.881
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==============&gt;-------------------------] 39.30% | train_error: 0.292 | train_acc: 0.986 | val_error: 2.46 | val_acc: 0.881
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==============&gt;-------------------------] 39.40% | train_error: 0.292 | train_acc: 0.986 | val_error: 2.46 | val_acc: 0.881
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==============&gt;-------------------------] 39.50% | train_error: 0.292 | train_acc: 0.986 | val_error: 2.46 | val_acc: 0.881
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==============&gt;-------------------------] 39.60% | train_error: 0.292 | train_acc: 0.986 | val_error: 2.46 | val_acc: 0.881
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==============&gt;-------------------------] 39.70% | train_error: 0.292 | train_acc: 0.986 | val_error: 2.46 | val_acc: 0.881
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==============&gt;-------------------------] 39.80% | train_error: 0.292 | train_acc: 0.986 | val_error: 2.46 | val_acc: 0.881
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==============&gt;-------------------------] 39.90% | train_error: 0.292 | train_acc: 0.986 | val_error: 2.46 | val_acc: 0.881
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===============&gt;------------------------] 40.00% | train_error: 0.292 | train_acc: 0.986 | val_error: 2.46 | val_acc: 0.881
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===============&gt;------------------------] 40.10% | train_error: 0.292 | train_acc: 0.986 | val_error: 2.46 | val_acc: 0.881
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===============&gt;------------------------] 40.20% | train_error: 0.292 | train_acc: 0.986 | val_error: 2.46 | val_acc: 0.881
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===============&gt;------------------------] 40.30% | train_error: 0.292 | train_acc: 0.986 | val_error: 2.32 | val_acc: 0.888
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===============&gt;------------------------] 40.40% | train_error: 0.292 | train_acc: 0.986 | val_error: 2.46 | val_acc: 0.881
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===============&gt;------------------------] 40.50% | train_error: 0.292 | train_acc: 0.986 | val_error: 2.32 | val_acc: 0.888
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===============&gt;------------------------] 40.60% | train_error: 0.292 | train_acc: 0.986 | val_error: 2.46 | val_acc: 0.881
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===============&gt;------------------------] 40.70% | train_error: 0.292 | train_acc: 0.986 | val_error: 2.32 | val_acc: 0.888
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===============&gt;------------------------] 40.80% | train_error: 0.292 | train_acc: 0.986 | val_error: 2.32 | val_acc: 0.888
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===============&gt;------------------------] 40.90% | train_error: 0.292 | train_acc: 0.986 | val_error: 2.32 | val_acc: 0.888
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===============&gt;------------------------] 41.00% | train_error: 0.292 | train_acc: 0.986 | val_error: 2.32 | val_acc: 0.888
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===============&gt;------------------------] 41.10% | train_error: 0.292 | train_acc: 0.986 | val_error: 2.32 | val_acc: 0.888
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===============&gt;------------------------] 41.20% | train_error: 0.292 | train_acc: 0.986 | val_error: 2.46 | val_acc: 0.881
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===============&gt;------------------------] 41.30% | train_error: 0.292 | train_acc: 0.986 | val_error: 2.32 | val_acc: 0.888
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===============&gt;------------------------] 41.40% | train_error: 0.292 | train_acc: 0.986 | val_error: 2.46 | val_acc: 0.881
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===============&gt;------------------------] 41.50% | train_error: 0.292 | train_acc: 0.986 | val_error: 2.32 | val_acc: 0.888
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===============&gt;------------------------] 41.60% | train_error: 0.292 | train_acc: 0.986 | val_error: 2.46 | val_acc: 0.881
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===============&gt;------------------------] 41.70% | train_error: 0.292 | train_acc: 0.986 | val_error: 2.32 | val_acc: 0.888
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===============&gt;------------------------] 41.80% | train_error: 0.292 | train_acc: 0.986 | val_error: 2.46 | val_acc: 0.881
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===============&gt;------------------------] 41.90% | train_error: 0.292 | train_acc: 0.986 | val_error: 2.17 | val_acc: 0.895
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===============&gt;------------------------] 42.00% | train_error: 0.292 | train_acc: 0.986 | val_error: 2.46 | val_acc: 0.881
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===============&gt;------------------------] 42.10% | train_error: 0.292 | train_acc: 0.986 | val_error: 2.17 | val_acc: 0.895
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===============&gt;------------------------] 42.20% | train_error: 0.292 | train_acc: 0.986 | val_error: 2.32 | val_acc: 0.888
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===============&gt;------------------------] 42.30% | train_error: 0.292 | train_acc: 0.986 | val_error: 2.32 | val_acc: 0.888
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===============&gt;------------------------] 42.40% | train_error: 0.292 | train_acc: 0.986 | val_error: 2.32 | val_acc: 0.888
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [================&gt;-----------------------] 42.50% | train_error: 0.292 | train_acc: 0.986 | val_error: 2.32 | val_acc: 0.888
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [================&gt;-----------------------] 42.60% | train_error: 0.292 | train_acc: 0.986 | val_error: 2.32 | val_acc: 0.888
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [================&gt;-----------------------] 42.70% | train_error: 0.292 | train_acc: 0.986 | val_error: 2.32 | val_acc: 0.888
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [================&gt;-----------------------] 42.80% | train_error: 0.292 | train_acc: 0.986 | val_error: 2.32 | val_acc: 0.888
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [================&gt;-----------------------] 42.90% | train_error: 0.292 | train_acc: 0.986 | val_error: 2.32 | val_acc: 0.888
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [================&gt;-----------------------] 43.00% | train_error: 0.292 | train_acc: 0.986 | val_error: 2.46 | val_acc: 0.881
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [================&gt;-----------------------] 43.10% | train_error: 0.292 | train_acc: 0.986 | val_error: 2.32 | val_acc: 0.888
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [================&gt;-----------------------] 43.20% | train_error: 0.292 | train_acc: 0.986 | val_error: 2.46 | val_acc: 0.881
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [================&gt;-----------------------] 43.30% | train_error: 0.292 | train_acc: 0.986 | val_error: 2.32 | val_acc: 0.888
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [================&gt;-----------------------] 43.40% | train_error: 0.292 | train_acc: 0.986 | val_error: 2.46 | val_acc: 0.881
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [================&gt;-----------------------] 43.50% | train_error: 0.292 | train_acc: 0.986 | val_error: 2.32 | val_acc: 0.888
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [================&gt;-----------------------] 43.60% | train_error: 0.292 | train_acc: 0.986 | val_error: 2.46 | val_acc: 0.881
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [================&gt;-----------------------] 43.70% | train_error: 0.292 | train_acc: 0.986 | val_error: 2.17 | val_acc: 0.895
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [================&gt;-----------------------] 43.80% | train_error: 0.292 | train_acc: 0.986 | val_error: 2.46 | val_acc: 0.881
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [================&gt;-----------------------] 43.90% | train_error: 0.243 | train_acc: 0.988 | val_error: 2.17 | val_acc: 0.895
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [================&gt;-----------------------] 44.00% | train_error: 0.292 | train_acc: 0.986 | val_error: 2.46 | val_acc: 0.881
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [================&gt;-----------------------] 44.10% | train_error: 0.243 | train_acc: 0.988 | val_error: 2.17 | val_acc: 0.895
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [================&gt;-----------------------] 44.20% | train_error: 0.292 | train_acc: 0.986 | val_error: 2.46 | val_acc: 0.881
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [================&gt;-----------------------] 44.30% | train_error: 0.243 | train_acc: 0.988 | val_error: 2.17 | val_acc: 0.895
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [================&gt;-----------------------] 44.40% | train_error: 0.292 | train_acc: 0.986 | val_error: 2.32 | val_acc: 0.888
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [================&gt;-----------------------] 44.50% | train_error: 0.243 | train_acc: 0.988 | val_error: 2.17 | val_acc: 0.895
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [================&gt;-----------------------] 44.60% | train_error: 0.243 | train_acc: 0.988 | val_error: 2.32 | val_acc: 0.888
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [================&gt;-----------------------] 44.70% | train_error: 0.243 | train_acc: 0.988 | val_error: 2.17 | val_acc: 0.895
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [================&gt;-----------------------] 44.80% | train_error: 0.243 | train_acc: 0.988 | val_error: 2.32 | val_acc: 0.888
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [================&gt;-----------------------] 44.90% | train_error: 0.243 | train_acc: 0.988 | val_error: 2.17 | val_acc: 0.895
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=================&gt;----------------------] 45.00% | train_error: 0.243 | train_acc: 0.988 | val_error: 2.32 | val_acc: 0.888
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=================&gt;----------------------] 45.10% | train_error: 0.243 | train_acc: 0.988 | val_error: 2.17 | val_acc: 0.895
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=================&gt;----------------------] 45.20% | train_error: 0.195 | train_acc: 0.991 | val_error: 2.17 | val_acc: 0.895
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=================&gt;----------------------] 45.30% | train_error: 0.243 | train_acc: 0.988 | val_error: 2.17 | val_acc: 0.895
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=================&gt;----------------------] 45.40% | train_error: 0.195 | train_acc: 0.991 | val_error: 2.17 | val_acc: 0.895
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=================&gt;----------------------] 45.50% | train_error: 0.243 | train_acc: 0.988 | val_error: 2.17 | val_acc: 0.895
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=================&gt;----------------------] 45.60% | train_error: 0.195 | train_acc: 0.991 | val_error: 2.17 | val_acc: 0.895
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=================&gt;----------------------] 45.70% | train_error: 0.243 | train_acc: 0.988 | val_error: 2.17 | val_acc: 0.895
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=================&gt;----------------------] 45.80% | train_error: 0.195 | train_acc: 0.991 | val_error: 2.17 | val_acc: 0.895
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=================&gt;----------------------] 45.90% | train_error: 0.195 | train_acc: 0.991 | val_error: 2.17 | val_acc: 0.895
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=================&gt;----------------------] 46.00% | train_error: 0.195 | train_acc: 0.991 | val_error: 2.17 | val_acc: 0.895
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=================&gt;----------------------] 46.10% | train_error: 0.195 | train_acc: 0.991 | val_error: 2.03 | val_acc: 0.902
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=================&gt;----------------------] 46.20% | train_error: 0.195 | train_acc: 0.991 | val_error: 2.17 | val_acc: 0.895
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=================&gt;----------------------] 46.30% | train_error: 0.195 | train_acc: 0.991 | val_error: 2.03 | val_acc: 0.902
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=================&gt;----------------------] 46.40% | train_error: 0.195 | train_acc: 0.991 | val_error: 2.17 | val_acc: 0.895
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=================&gt;----------------------] 46.50% | train_error: 0.195 | train_acc: 0.991 | val_error: 2.03 | val_acc: 0.902
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=================&gt;----------------------] 46.60% | train_error: 0.195 | train_acc: 0.991 | val_error: 2.17 | val_acc: 0.895
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=================&gt;----------------------] 46.70% | train_error: 0.195 | train_acc: 0.991 | val_error: 2.03 | val_acc: 0.902
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=================&gt;----------------------] 46.80% | train_error: 0.195 | train_acc: 0.991 | val_error: 2.17 | val_acc: 0.895
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=================&gt;----------------------] 46.90% | train_error: 0.195 | train_acc: 0.991 | val_error: 2.03 | val_acc: 0.902
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=================&gt;----------------------] 47.00% | train_error: 0.146 | train_acc: 0.993 | val_error: 2.17 | val_acc: 0.895
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=================&gt;----------------------] 47.10% | train_error: 0.195 | train_acc: 0.991 | val_error: 2.03 | val_acc: 0.902
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=================&gt;----------------------] 47.20% | train_error: 0.146 | train_acc: 0.993 | val_error: 2.17 | val_acc: 0.895
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=================&gt;----------------------] 47.30% | train_error: 0.195 | train_acc: 0.991 | val_error: 2.03 | val_acc: 0.902
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=================&gt;----------------------] 47.40% | train_error: 0.146 | train_acc: 0.993 | val_error: 2.17 | val_acc: 0.895
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==================&gt;---------------------] 47.50% | train_error: 0.195 | train_acc: 0.991 | val_error: 2.03 | val_acc: 0.902
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==================&gt;---------------------] 47.60% | train_error: 0.146 | train_acc: 0.993 | val_error: 2.17 | val_acc: 0.895
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==================&gt;---------------------] 47.70% | train_error: 0.195 | train_acc: 0.991 | val_error: 2.03 | val_acc: 0.902
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==================&gt;---------------------] 47.80% | train_error: 0.146 | train_acc: 0.993 | val_error: 2.17 | val_acc: 0.895
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==================&gt;---------------------] 47.90% | train_error: 0.146 | train_acc: 0.993 | val_error: 2.03 | val_acc: 0.902
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==================&gt;---------------------] 48.00% | train_error: 0.146 | train_acc: 0.993 | val_error: 2.17 | val_acc: 0.895
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==================&gt;---------------------] 48.10% | train_error: 0.146 | train_acc: 0.993 | val_error: 2.03 | val_acc: 0.902
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==================&gt;---------------------] 48.20% | train_error: 0.146 | train_acc: 0.993 | val_error: 2.17 | val_acc: 0.895
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==================&gt;---------------------] 48.30% | train_error: 0.146 | train_acc: 0.993 | val_error: 2.03 | val_acc: 0.902
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==================&gt;---------------------] 48.40% | train_error: 0.146 | train_acc: 0.993 | val_error: 2.17 | val_acc: 0.895
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==================&gt;---------------------] 48.50% | train_error: 0.146 | train_acc: 0.993 | val_error: 2.03 | val_acc: 0.902
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==================&gt;---------------------] 48.60% | train_error: 0.146 | train_acc: 0.993 | val_error: 2.17 | val_acc: 0.895
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==================&gt;---------------------] 48.70% | train_error: 0.146 | train_acc: 0.993 | val_error: 2.03 | val_acc: 0.902
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==================&gt;---------------------] 48.80% | train_error: 0.146 | train_acc: 0.993 | val_error: 2.17 | val_acc: 0.895
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==================&gt;---------------------] 48.90% | train_error: 0.0973 | train_acc: 0.995 | val_error: 2.03 | val_acc: 0.902
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==================&gt;---------------------] 49.00% | train_error: 0.146 | train_acc: 0.993 | val_error: 2.17 | val_acc: 0.895
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==================&gt;---------------------] 49.10% | train_error: 0.0973 | train_acc: 0.995 | val_error: 2.03 | val_acc: 0.902
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==================&gt;---------------------] 49.20% | train_error: 0.146 | train_acc: 0.993 | val_error: 2.17 | val_acc: 0.895
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==================&gt;---------------------] 49.30% | train_error: 0.0973 | train_acc: 0.995 | val_error: 2.03 | val_acc: 0.902
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==================&gt;---------------------] 49.40% | train_error: 0.146 | train_acc: 0.993 | val_error: 2.17 | val_acc: 0.895
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==================&gt;---------------------] 49.50% | train_error: 0.0973 | train_acc: 0.995 | val_error: 2.03 | val_acc: 0.902
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==================&gt;---------------------] 49.60% | train_error: 0.0973 | train_acc: 0.995 | val_error: 2.17 | val_acc: 0.895
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==================&gt;---------------------] 49.70% | train_error: 0.0973 | train_acc: 0.995 | val_error: 2.03 | val_acc: 0.902
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==================&gt;---------------------] 49.80% | train_error: 0.0973 | train_acc: 0.995 | val_error: 2.17 | val_acc: 0.895
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==================&gt;---------------------] 49.90% | train_error: 0.0973 | train_acc: 0.995 | val_error: 2.03 | val_acc: 0.902
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===================&gt;--------------------] 50.00% | train_error: 0.0973 | train_acc: 0.995 | val_error: 2.17 | val_acc: 0.895
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===================&gt;--------------------] 50.10% | train_error: 0.0973 | train_acc: 0.995 | val_error: 2.03 | val_acc: 0.902
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===================&gt;--------------------] 50.20% | train_error: 0.0973 | train_acc: 0.995 | val_error: 2.17 | val_acc: 0.895
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===================&gt;--------------------] 50.30% | train_error: 0.0973 | train_acc: 0.995 | val_error: 2.03 | val_acc: 0.902
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===================&gt;--------------------] 50.40% | train_error: 0.0973 | train_acc: 0.995 | val_error: 2.17 | val_acc: 0.895
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===================&gt;--------------------] 50.50% | train_error: 0.0973 | train_acc: 0.995 | val_error: 2.03 | val_acc: 0.902
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===================&gt;--------------------] 50.60% | train_error: 0.0973 | train_acc: 0.995 | val_error: 2.17 | val_acc: 0.895
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===================&gt;--------------------] 50.70% | train_error: 0.0973 | train_acc: 0.995 | val_error: 2.03 | val_acc: 0.902
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===================&gt;--------------------] 50.80% | train_error: 0.0973 | train_acc: 0.995 | val_error: 2.17 | val_acc: 0.895
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===================&gt;--------------------] 50.90% | train_error: 0.0973 | train_acc: 0.995 | val_error: 2.03 | val_acc: 0.902
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===================&gt;--------------------] 51.00% | train_error: 0.0973 | train_acc: 0.995 | val_error: 2.17 | val_acc: 0.895
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===================&gt;--------------------] 51.10% | train_error: 0.0973 | train_acc: 0.995 | val_error: 2.03 | val_acc: 0.902
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===================&gt;--------------------] 51.20% | train_error: 0.0973 | train_acc: 0.995 | val_error: 2.17 | val_acc: 0.895
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===================&gt;--------------------] 51.30% | train_error: 0.0973 | train_acc: 0.995 | val_error: 2.03 | val_acc: 0.902
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===================&gt;--------------------] 51.40% | train_error: 0.0973 | train_acc: 0.995 | val_error: 2.17 | val_acc: 0.895
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===================&gt;--------------------] 51.50% | train_error: 0.0973 | train_acc: 0.995 | val_error: 2.03 | val_acc: 0.902
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===================&gt;--------------------] 51.60% | train_error: 0.0973 | train_acc: 0.995 | val_error: 2.17 | val_acc: 0.895
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===================&gt;--------------------] 51.70% | train_error: 0.0973 | train_acc: 0.995 | val_error: 2.03 | val_acc: 0.902
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===================&gt;--------------------] 51.80% | train_error: 0.0973 | train_acc: 0.995 | val_error: 2.17 | val_acc: 0.895
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===================&gt;--------------------] 51.90% | train_error: 0.0973 | train_acc: 0.995 | val_error: 2.03 | val_acc: 0.902
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===================&gt;--------------------] 52.00% | train_error: 0.0973 | train_acc: 0.995 | val_error: 2.17 | val_acc: 0.895
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===================&gt;--------------------] 52.10% | train_error: 0.0973 | train_acc: 0.995 | val_error: 2.03 | val_acc: 0.902
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===================&gt;--------------------] 52.20% | train_error: 0.0973 | train_acc: 0.995 | val_error: 2.03 | val_acc: 0.902
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===================&gt;--------------------] 52.30% | train_error: 0.0973 | train_acc: 0.995 | val_error: 2.03 | val_acc: 0.902
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===================&gt;--------------------] 52.40% | train_error: 0.0973 | train_acc: 0.995 | val_error: 2.03 | val_acc: 0.902
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [====================&gt;-------------------] 52.50% | train_error: 0.0973 | train_acc: 0.995 | val_error: 2.03 | val_acc: 0.902
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [====================&gt;-------------------] 52.60% | train_error: 0.0973 | train_acc: 0.995 | val_error: 2.03 | val_acc: 0.902
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [====================&gt;-------------------] 52.70% | train_error: 0.0973 | train_acc: 0.995 | val_error: 2.03 | val_acc: 0.902
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [====================&gt;-------------------] 52.80% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.88 | val_acc: 0.909
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [====================&gt;-------------------] 52.90% | train_error: 0.0973 | train_acc: 0.995 | val_error: 2.03 | val_acc: 0.902
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [====================&gt;-------------------] 53.00% | train_error: 0.0973 | train_acc: 0.995 | val_error: 2.03 | val_acc: 0.902
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [====================&gt;-------------------] 53.10% | train_error: 0.0973 | train_acc: 0.995 | val_error: 2.03 | val_acc: 0.902
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [====================&gt;-------------------] 53.20% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.88 | val_acc: 0.909
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [====================&gt;-------------------] 53.30% | train_error: 0.0973 | train_acc: 0.995 | val_error: 2.03 | val_acc: 0.902
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [====================&gt;-------------------] 53.40% | train_error: 0.0973 | train_acc: 0.995 | val_error: 2.03 | val_acc: 0.902
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [====================&gt;-------------------] 53.50% | train_error: 0.0973 | train_acc: 0.995 | val_error: 2.03 | val_acc: 0.902
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [====================&gt;-------------------] 53.60% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.88 | val_acc: 0.909
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [====================&gt;-------------------] 53.70% | train_error: 0.0973 | train_acc: 0.995 | val_error: 2.03 | val_acc: 0.902
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [====================&gt;-------------------] 53.80% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.88 | val_acc: 0.909
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [====================&gt;-------------------] 53.90% | train_error: 0.0973 | train_acc: 0.995 | val_error: 2.03 | val_acc: 0.902
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [====================&gt;-------------------] 54.00% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.88 | val_acc: 0.909
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [====================&gt;-------------------] 54.10% | train_error: 0.0973 | train_acc: 0.995 | val_error: 2.03 | val_acc: 0.902
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [====================&gt;-------------------] 54.20% | train_error: 0.0973 | train_acc: 0.995 | val_error: 2.03 | val_acc: 0.902
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [====================&gt;-------------------] 54.30% | train_error: 0.0973 | train_acc: 0.995 | val_error: 2.03 | val_acc: 0.902
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [====================&gt;-------------------] 54.40% | train_error: 0.0973 | train_acc: 0.995 | val_error: 2.03 | val_acc: 0.902
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [====================&gt;-------------------] 54.50% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.88 | val_acc: 0.909
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [====================&gt;-------------------] 54.60% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.88 | val_acc: 0.909
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [====================&gt;-------------------] 54.70% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.88 | val_acc: 0.909
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [====================&gt;-------------------] 54.80% | train_error: 0.0973 | train_acc: 0.995 | val_error: 2.03 | val_acc: 0.902
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [====================&gt;-------------------] 54.90% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.88 | val_acc: 0.909
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=====================&gt;------------------] 55.00% | train_error: 0.0973 | train_acc: 0.995 | val_error: 2.03 | val_acc: 0.902
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=====================&gt;------------------] 55.10% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.88 | val_acc: 0.909
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=====================&gt;------------------] 55.20% | train_error: 0.0973 | train_acc: 0.995 | val_error: 2.03 | val_acc: 0.902
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=====================&gt;------------------] 55.30% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.88 | val_acc: 0.909
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=====================&gt;------------------] 55.40% | train_error: 0.0973 | train_acc: 0.995 | val_error: 2.03 | val_acc: 0.902
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=====================&gt;------------------] 55.50% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.88 | val_acc: 0.909
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=====================&gt;------------------] 55.60% | train_error: 0.0973 | train_acc: 0.995 | val_error: 2.03 | val_acc: 0.902
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=====================&gt;------------------] 55.70% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.88 | val_acc: 0.909
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=====================&gt;------------------] 55.80% | train_error: 0.0973 | train_acc: 0.995 | val_error: 2.03 | val_acc: 0.902
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=====================&gt;------------------] 55.90% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.88 | val_acc: 0.909
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=====================&gt;------------------] 56.00% | train_error: 0.0973 | train_acc: 0.995 | val_error: 2.03 | val_acc: 0.902
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=====================&gt;------------------] 56.10% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.88 | val_acc: 0.909
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=====================&gt;------------------] 56.20% | train_error: 0.0973 | train_acc: 0.995 | val_error: 2.03 | val_acc: 0.902
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=====================&gt;------------------] 56.30% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.88 | val_acc: 0.909
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=====================&gt;------------------] 56.40% | train_error: 0.0973 | train_acc: 0.995 | val_error: 2.03 | val_acc: 0.902
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=====================&gt;------------------] 56.50% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.88 | val_acc: 0.909
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=====================&gt;------------------] 56.60% | train_error: 0.0973 | train_acc: 0.995 | val_error: 2.03 | val_acc: 0.902
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=====================&gt;------------------] 56.70% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.88 | val_acc: 0.909
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=====================&gt;------------------] 56.80% | train_error: 0.0973 | train_acc: 0.995 | val_error: 2.03 | val_acc: 0.902
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=====================&gt;------------------] 56.90% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.88 | val_acc: 0.909
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=====================&gt;------------------] 57.00% | train_error: 0.0973 | train_acc: 0.995 | val_error: 2.03 | val_acc: 0.902
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=====================&gt;------------------] 57.10% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.88 | val_acc: 0.909
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=====================&gt;------------------] 57.20% | train_error: 0.0973 | train_acc: 0.995 | val_error: 2.03 | val_acc: 0.902
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=====================&gt;------------------] 57.30% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.88 | val_acc: 0.909
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=====================&gt;------------------] 57.40% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.88 | val_acc: 0.909
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [======================&gt;-----------------] 57.50% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.88 | val_acc: 0.909
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [======================&gt;-----------------] 57.60% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.88 | val_acc: 0.909
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [======================&gt;-----------------] 57.70% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.88 | val_acc: 0.909
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [======================&gt;-----------------] 57.80% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.88 | val_acc: 0.909
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [======================&gt;-----------------] 57.90% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.88 | val_acc: 0.909
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [======================&gt;-----------------] 58.00% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.88 | val_acc: 0.909
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [======================&gt;-----------------] 58.10% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.88 | val_acc: 0.909
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [======================&gt;-----------------] 58.20% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.88 | val_acc: 0.909
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [======================&gt;-----------------] 58.30% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.88 | val_acc: 0.909
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [======================&gt;-----------------] 58.40% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.88 | val_acc: 0.909
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [======================&gt;-----------------] 58.50% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.88 | val_acc: 0.909
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [======================&gt;-----------------] 58.60% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.88 | val_acc: 0.909
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [======================&gt;-----------------] 58.70% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.88 | val_acc: 0.909
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [======================&gt;-----------------] 58.80% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.88 | val_acc: 0.909
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [======================&gt;-----------------] 58.90% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.88 | val_acc: 0.909
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [======================&gt;-----------------] 59.00% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.88 | val_acc: 0.909
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [======================&gt;-----------------] 59.10% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.88 | val_acc: 0.909
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [======================&gt;-----------------] 59.20% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.88 | val_acc: 0.909
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [======================&gt;-----------------] 59.30% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.88 | val_acc: 0.909
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [======================&gt;-----------------] 59.40% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.88 | val_acc: 0.909
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [======================&gt;-----------------] 59.50% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.88 | val_acc: 0.909
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [======================&gt;-----------------] 59.60% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.88 | val_acc: 0.909
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [======================&gt;-----------------] 59.70% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.88 | val_acc: 0.909
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [======================&gt;-----------------] 59.80% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.88 | val_acc: 0.909
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [======================&gt;-----------------] 59.90% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.88 | val_acc: 0.909
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=======================&gt;----------------] 60.00% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.88 | val_acc: 0.909
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=======================&gt;----------------] 60.10% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.88 | val_acc: 0.909
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=======================&gt;----------------] 60.20% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.88 | val_acc: 0.909
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=======================&gt;----------------] 60.30% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.88 | val_acc: 0.909
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=======================&gt;----------------] 60.40% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.88 | val_acc: 0.909
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=======================&gt;----------------] 60.50% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.88 | val_acc: 0.909
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=======================&gt;----------------] 60.60% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.88 | val_acc: 0.909
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=======================&gt;----------------] 60.70% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.88 | val_acc: 0.909
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=======================&gt;----------------] 60.80% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.88 | val_acc: 0.909
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=======================&gt;----------------] 60.90% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.88 | val_acc: 0.909
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=======================&gt;----------------] 61.00% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.88 | val_acc: 0.909
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=======================&gt;----------------] 61.10% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.88 | val_acc: 0.909
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=======================&gt;----------------] 61.20% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.88 | val_acc: 0.909
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=======================&gt;----------------] 61.30% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.88 | val_acc: 0.909
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=======================&gt;----------------] 61.40% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.88 | val_acc: 0.909
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=======================&gt;----------------] 61.50% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.88 | val_acc: 0.909
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=======================&gt;----------------] 61.60% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.88 | val_acc: 0.909
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=======================&gt;----------------] 61.70% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.88 | val_acc: 0.909
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=======================&gt;----------------] 61.80% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.88 | val_acc: 0.909
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=======================&gt;----------------] 61.90% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.88 | val_acc: 0.909
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=======================&gt;----------------] 62.00% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.88 | val_acc: 0.909
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=======================&gt;----------------] 62.10% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.88 | val_acc: 0.909
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=======================&gt;----------------] 62.20% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.88 | val_acc: 0.909
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=======================&gt;----------------] 62.30% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.88 | val_acc: 0.909
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=======================&gt;----------------] 62.40% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.88 | val_acc: 0.909
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [========================&gt;---------------] 62.50% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.88 | val_acc: 0.909
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [========================&gt;---------------] 62.60% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.88 | val_acc: 0.909
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [========================&gt;---------------] 62.70% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.88 | val_acc: 0.909
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [========================&gt;---------------] 62.80% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.88 | val_acc: 0.909
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [========================&gt;---------------] 62.90% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.88 | val_acc: 0.909
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [========================&gt;---------------] 63.00% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.88 | val_acc: 0.909
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [========================&gt;---------------] 63.10% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.88 | val_acc: 0.909
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [========================&gt;---------------] 63.20% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.88 | val_acc: 0.909
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [========================&gt;---------------] 63.30% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.88 | val_acc: 0.909
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [========================&gt;---------------] 63.40% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.88 | val_acc: 0.909
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [========================&gt;---------------] 63.50% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.88 | val_acc: 0.909
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [========================&gt;---------------] 63.60% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.88 | val_acc: 0.909
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [========================&gt;---------------] 63.70% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.88 | val_acc: 0.909
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [========================&gt;---------------] 63.80% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.88 | val_acc: 0.909
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [========================&gt;---------------] 63.90% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.74 | val_acc: 0.916
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [========================&gt;---------------] 64.00% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.88 | val_acc: 0.909
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [========================&gt;---------------] 64.10% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.74 | val_acc: 0.916
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [========================&gt;---------------] 64.20% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.88 | val_acc: 0.909
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [========================&gt;---------------] 64.30% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.74 | val_acc: 0.916
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [========================&gt;---------------] 64.40% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.88 | val_acc: 0.909
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [========================&gt;---------------] 64.50% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.74 | val_acc: 0.916
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [========================&gt;---------------] 64.60% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.88 | val_acc: 0.909
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [========================&gt;---------------] 64.70% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.74 | val_acc: 0.916
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [========================&gt;---------------] 64.80% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.88 | val_acc: 0.909
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [========================&gt;---------------] 64.90% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.74 | val_acc: 0.916
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=========================&gt;--------------] 65.00% | train_error: 0.146 | train_acc: 0.993 | val_error: 1.88 | val_acc: 0.909
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=========================&gt;--------------] 65.10% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.74 | val_acc: 0.916
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=========================&gt;--------------] 65.20% | train_error: 0.146 | train_acc: 0.993 | val_error: 1.88 | val_acc: 0.909
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=========================&gt;--------------] 65.30% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.74 | val_acc: 0.916
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=========================&gt;--------------] 65.40% | train_error: 0.146 | train_acc: 0.993 | val_error: 1.88 | val_acc: 0.909
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=========================&gt;--------------] 65.50% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.74 | val_acc: 0.916
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=========================&gt;--------------] 65.60% | train_error: 0.146 | train_acc: 0.993 | val_error: 1.88 | val_acc: 0.909
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=========================&gt;--------------] 65.70% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.74 | val_acc: 0.916
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=========================&gt;--------------] 65.80% | train_error: 0.146 | train_acc: 0.993 | val_error: 1.88 | val_acc: 0.909
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=========================&gt;--------------] 65.90% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.74 | val_acc: 0.916
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=========================&gt;--------------] 66.00% | train_error: 0.146 | train_acc: 0.993 | val_error: 1.88 | val_acc: 0.909
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=========================&gt;--------------] 66.10% | train_error: 0.0486 | train_acc: 0.998 | val_error: 1.74 | val_acc: 0.916
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=========================&gt;--------------] 66.20% | train_error: 0.146 | train_acc: 0.993 | val_error: 1.88 | val_acc: 0.909
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=========================&gt;--------------] 66.30% | train_error: 0.0486 | train_acc: 0.998 | val_error: 1.74 | val_acc: 0.916
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=========================&gt;--------------] 66.40% | train_error: 0.146 | train_acc: 0.993 | val_error: 1.88 | val_acc: 0.909
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=========================&gt;--------------] 66.50% | train_error: 0.0486 | train_acc: 0.998 | val_error: 1.74 | val_acc: 0.916
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=========================&gt;--------------] 66.60% | train_error: 0.146 | train_acc: 0.993 | val_error: 1.88 | val_acc: 0.909
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=========================&gt;--------------] 66.70% | train_error: 0.0486 | train_acc: 0.998 | val_error: 1.74 | val_acc: 0.916
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=========================&gt;--------------] 66.80% | train_error: 0.146 | train_acc: 0.993 | val_error: 1.88 | val_acc: 0.909
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=========================&gt;--------------] 66.90% | train_error: 0.0486 | train_acc: 0.998 | val_error: 1.74 | val_acc: 0.916
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=========================&gt;--------------] 67.00% | train_error: 0.146 | train_acc: 0.993 | val_error: 1.88 | val_acc: 0.909
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=========================&gt;--------------] 67.10% | train_error: 0.0486 | train_acc: 0.998 | val_error: 1.74 | val_acc: 0.916
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=========================&gt;--------------] 67.20% | train_error: 0.146 | train_acc: 0.993 | val_error: 1.88 | val_acc: 0.909
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=========================&gt;--------------] 67.30% | train_error: 0.0486 | train_acc: 0.998 | val_error: 1.74 | val_acc: 0.916
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=========================&gt;--------------] 67.40% | train_error: 0.146 | train_acc: 0.993 | val_error: 1.88 | val_acc: 0.909
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==========================&gt;-------------] 67.50% | train_error: 0.0486 | train_acc: 0.998 | val_error: 1.74 | val_acc: 0.916
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==========================&gt;-------------] 67.60% | train_error: 0.146 | train_acc: 0.993 | val_error: 1.88 | val_acc: 0.909
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==========================&gt;-------------] 67.70% | train_error: 0.0486 | train_acc: 0.998 | val_error: 1.74 | val_acc: 0.916
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==========================&gt;-------------] 67.80% | train_error: 0.146 | train_acc: 0.993 | val_error: 1.88 | val_acc: 0.909
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==========================&gt;-------------] 67.90% | train_error: 0.0486 | train_acc: 0.998 | val_error: 1.74 | val_acc: 0.916
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==========================&gt;-------------] 68.00% | train_error: 0.146 | train_acc: 0.993 | val_error: 1.88 | val_acc: 0.909
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==========================&gt;-------------] 68.10% | train_error: 0.0486 | train_acc: 0.998 | val_error: 1.74 | val_acc: 0.916
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==========================&gt;-------------] 68.20% | train_error: 0.146 | train_acc: 0.993 | val_error: 1.88 | val_acc: 0.909
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==========================&gt;-------------] 68.30% | train_error: 0.0486 | train_acc: 0.998 | val_error: 1.74 | val_acc: 0.916
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==========================&gt;-------------] 68.40% | train_error: 0.146 | train_acc: 0.993 | val_error: 1.88 | val_acc: 0.909
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==========================&gt;-------------] 68.50% | train_error: 0.0486 | train_acc: 0.998 | val_error: 1.74 | val_acc: 0.916
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==========================&gt;-------------] 68.60% | train_error: 0.146 | train_acc: 0.993 | val_error: 1.88 | val_acc: 0.909
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==========================&gt;-------------] 68.70% | train_error: 0.0486 | train_acc: 0.998 | val_error: 1.74 | val_acc: 0.916
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==========================&gt;-------------] 68.80% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.88 | val_acc: 0.909
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==========================&gt;-------------] 68.90% | train_error: 0.0486 | train_acc: 0.998 | val_error: 1.74 | val_acc: 0.916
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==========================&gt;-------------] 69.00% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.88 | val_acc: 0.909
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==========================&gt;-------------] 69.10% | train_error: 0.0486 | train_acc: 0.998 | val_error: 1.74 | val_acc: 0.916
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==========================&gt;-------------] 69.20% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.88 | val_acc: 0.909
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==========================&gt;-------------] 69.30% | train_error: 0.0486 | train_acc: 0.998 | val_error: 1.74 | val_acc: 0.916
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==========================&gt;-------------] 69.40% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.88 | val_acc: 0.909
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==========================&gt;-------------] 69.50% | train_error: 0.0486 | train_acc: 0.998 | val_error: 1.74 | val_acc: 0.916
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==========================&gt;-------------] 69.60% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.88 | val_acc: 0.909
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==========================&gt;-------------] 69.70% | train_error: 0.0486 | train_acc: 0.998 | val_error: 1.74 | val_acc: 0.916
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==========================&gt;-------------] 69.80% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.88 | val_acc: 0.909
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==========================&gt;-------------] 69.90% | train_error: 0.0486 | train_acc: 0.998 | val_error: 1.74 | val_acc: 0.916
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===========================&gt;------------] 70.00% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.74 | val_acc: 0.916
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===========================&gt;------------] 70.10% | train_error: 0.0486 | train_acc: 0.998 | val_error: 1.74 | val_acc: 0.916
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===========================&gt;------------] 70.20% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.74 | val_acc: 0.916
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===========================&gt;------------] 70.30% | train_error: 0.0486 | train_acc: 0.998 | val_error: 1.74 | val_acc: 0.916
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===========================&gt;------------] 70.40% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.74 | val_acc: 0.916
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===========================&gt;------------] 70.50% | train_error: 0.0486 | train_acc: 0.998 | val_error: 1.74 | val_acc: 0.916
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===========================&gt;------------] 70.60% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.74 | val_acc: 0.916
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===========================&gt;------------] 70.70% | train_error: 0.0486 | train_acc: 0.998 | val_error: 1.74 | val_acc: 0.916
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===========================&gt;------------] 70.80% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.74 | val_acc: 0.916
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===========================&gt;------------] 70.90% | train_error: 0.0486 | train_acc: 0.998 | val_error: 1.74 | val_acc: 0.916
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===========================&gt;------------] 71.00% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.74 | val_acc: 0.916
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===========================&gt;------------] 71.10% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.74 | val_acc: 0.916
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===========================&gt;------------] 71.20% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.74 | val_acc: 0.916
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===========================&gt;------------] 71.30% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.74 | val_acc: 0.916
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===========================&gt;------------] 71.40% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.74 | val_acc: 0.916
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===========================&gt;------------] 71.50% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.74 | val_acc: 0.916
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===========================&gt;------------] 71.60% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.74 | val_acc: 0.916
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===========================&gt;------------] 71.70% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.74 | val_acc: 0.916
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===========================&gt;------------] 71.80% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.74 | val_acc: 0.916
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===========================&gt;------------] 71.90% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.74 | val_acc: 0.916
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===========================&gt;------------] 72.00% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.74 | val_acc: 0.916
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===========================&gt;------------] 72.10% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.74 | val_acc: 0.916
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===========================&gt;------------] 72.20% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.74 | val_acc: 0.916
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===========================&gt;------------] 72.30% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.74 | val_acc: 0.916
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===========================&gt;------------] 72.40% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.74 | val_acc: 0.916
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [============================&gt;-----------] 72.50% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.74 | val_acc: 0.916
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [============================&gt;-----------] 72.60% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.74 | val_acc: 0.916
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [============================&gt;-----------] 72.70% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.74 | val_acc: 0.916
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [============================&gt;-----------] 72.80% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.74 | val_acc: 0.916
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [============================&gt;-----------] 72.90% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.74 | val_acc: 0.916
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [============================&gt;-----------] 73.00% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.74 | val_acc: 0.916
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [============================&gt;-----------] 73.10% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.74 | val_acc: 0.916
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [============================&gt;-----------] 73.20% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.74 | val_acc: 0.916
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [============================&gt;-----------] 73.30% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.74 | val_acc: 0.916
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [============================&gt;-----------] 73.40% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.74 | val_acc: 0.916
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [============================&gt;-----------] 73.50% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.74 | val_acc: 0.916
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [============================&gt;-----------] 73.60% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.74 | val_acc: 0.916
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [============================&gt;-----------] 73.70% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.74 | val_acc: 0.916
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [============================&gt;-----------] 73.80% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.74 | val_acc: 0.916
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [============================&gt;-----------] 73.90% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.74 | val_acc: 0.916
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [============================&gt;-----------] 74.00% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.74 | val_acc: 0.916
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [============================&gt;-----------] 74.10% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.74 | val_acc: 0.916
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [============================&gt;-----------] 74.20% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.74 | val_acc: 0.916
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [============================&gt;-----------] 74.30% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.74 | val_acc: 0.916
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [============================&gt;-----------] 74.40% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.74 | val_acc: 0.916
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [============================&gt;-----------] 74.50% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.74 | val_acc: 0.916
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [============================&gt;-----------] 74.60% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.74 | val_acc: 0.916
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [============================&gt;-----------] 74.70% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.74 | val_acc: 0.916
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [============================&gt;-----------] 74.80% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.74 | val_acc: 0.916
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [============================&gt;-----------] 74.90% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.74 | val_acc: 0.916
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=============================&gt;----------] 75.00% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.74 | val_acc: 0.916
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=============================&gt;----------] 75.10% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.74 | val_acc: 0.916
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=============================&gt;----------] 75.20% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.74 | val_acc: 0.916
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=============================&gt;----------] 75.30% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.74 | val_acc: 0.916
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=============================&gt;----------] 75.40% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.74 | val_acc: 0.916
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=============================&gt;----------] 75.50% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.74 | val_acc: 0.916
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=============================&gt;----------] 75.60% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.74 | val_acc: 0.916
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=============================&gt;----------] 75.70% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.74 | val_acc: 0.916
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=============================&gt;----------] 75.80% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.74 | val_acc: 0.916
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=============================&gt;----------] 75.90% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.74 | val_acc: 0.916
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=============================&gt;----------] 76.00% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.74 | val_acc: 0.916
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=============================&gt;----------] 76.10% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.74 | val_acc: 0.916
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=============================&gt;----------] 76.20% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.74 | val_acc: 0.916
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=============================&gt;----------] 76.30% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.74 | val_acc: 0.916
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=============================&gt;----------] 76.40% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.74 | val_acc: 0.916
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=============================&gt;----------] 76.50% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.74 | val_acc: 0.916
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=============================&gt;----------] 76.60% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.74 | val_acc: 0.916
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=============================&gt;----------] 76.70% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.74 | val_acc: 0.916
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=============================&gt;----------] 76.80% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.74 | val_acc: 0.916
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=============================&gt;----------] 76.90% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.74 | val_acc: 0.916
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=============================&gt;----------] 77.00% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.74 | val_acc: 0.916
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=============================&gt;----------] 77.10% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.59 | val_acc: 0.923
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=============================&gt;----------] 77.20% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.74 | val_acc: 0.916
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=============================&gt;----------] 77.30% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.59 | val_acc: 0.923
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=============================&gt;----------] 77.40% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.74 | val_acc: 0.916
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==============================&gt;---------] 77.50% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.59 | val_acc: 0.923
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==============================&gt;---------] 77.60% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.74 | val_acc: 0.916
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==============================&gt;---------] 77.70% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.59 | val_acc: 0.923
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==============================&gt;---------] 77.80% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.74 | val_acc: 0.916
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==============================&gt;---------] 77.90% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.59 | val_acc: 0.923
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==============================&gt;---------] 78.00% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.74 | val_acc: 0.916
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==============================&gt;---------] 78.10% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.59 | val_acc: 0.923
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==============================&gt;---------] 78.20% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.74 | val_acc: 0.916
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==============================&gt;---------] 78.30% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.59 | val_acc: 0.923
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==============================&gt;---------] 78.40% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.74 | val_acc: 0.916
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==============================&gt;---------] 78.50% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.59 | val_acc: 0.923
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==============================&gt;---------] 78.60% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.74 | val_acc: 0.916
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==============================&gt;---------] 78.70% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.59 | val_acc: 0.923
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==============================&gt;---------] 78.80% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.74 | val_acc: 0.916
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==============================&gt;---------] 78.90% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.59 | val_acc: 0.923
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==============================&gt;---------] 79.00% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.74 | val_acc: 0.916
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==============================&gt;---------] 79.10% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.59 | val_acc: 0.923
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==============================&gt;---------] 79.20% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.74 | val_acc: 0.916
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==============================&gt;---------] 79.30% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.59 | val_acc: 0.923
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==============================&gt;---------] 79.40% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.74 | val_acc: 0.916
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==============================&gt;---------] 79.50% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.59 | val_acc: 0.923
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==============================&gt;---------] 79.60% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.74 | val_acc: 0.916
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==============================&gt;---------] 79.70% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.59 | val_acc: 0.923
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==============================&gt;---------] 79.80% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.74 | val_acc: 0.916
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==============================&gt;---------] 79.90% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.59 | val_acc: 0.923
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===============================&gt;--------] 80.00% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.74 | val_acc: 0.916
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===============================&gt;--------] 80.10% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.59 | val_acc: 0.923
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===============================&gt;--------] 80.20% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.74 | val_acc: 0.916
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===============================&gt;--------] 80.30% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.59 | val_acc: 0.923
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===============================&gt;--------] 80.40% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.74 | val_acc: 0.916
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===============================&gt;--------] 80.50% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.59 | val_acc: 0.923
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===============================&gt;--------] 80.60% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.74 | val_acc: 0.916
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===============================&gt;--------] 80.70% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.59 | val_acc: 0.923
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===============================&gt;--------] 80.80% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.74 | val_acc: 0.916
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===============================&gt;--------] 80.90% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.59 | val_acc: 0.923
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===============================&gt;--------] 81.00% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.74 | val_acc: 0.916
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===============================&gt;--------] 81.10% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.59 | val_acc: 0.923
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===============================&gt;--------] 81.20% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.74 | val_acc: 0.916
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===============================&gt;--------] 81.30% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.59 | val_acc: 0.923
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===============================&gt;--------] 81.40% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.74 | val_acc: 0.916
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===============================&gt;--------] 81.50% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.59 | val_acc: 0.923
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===============================&gt;--------] 81.60% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.74 | val_acc: 0.916
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===============================&gt;--------] 81.70% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.59 | val_acc: 0.923
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===============================&gt;--------] 81.80% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.74 | val_acc: 0.916
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===============================&gt;--------] 81.90% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.59 | val_acc: 0.923
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===============================&gt;--------] 82.00% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.74 | val_acc: 0.916
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===============================&gt;--------] 82.10% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.59 | val_acc: 0.923
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===============================&gt;--------] 82.20% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.74 | val_acc: 0.916
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===============================&gt;--------] 82.30% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.59 | val_acc: 0.923
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===============================&gt;--------] 82.40% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.74 | val_acc: 0.916
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [================================&gt;-------] 82.50% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.59 | val_acc: 0.923
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [================================&gt;-------] 82.60% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.74 | val_acc: 0.916
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [================================&gt;-------] 82.70% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.59 | val_acc: 0.923
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [================================&gt;-------] 82.80% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.74 | val_acc: 0.916
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [================================&gt;-------] 82.90% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.59 | val_acc: 0.923
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [================================&gt;-------] 83.00% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.74 | val_acc: 0.916
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [================================&gt;-------] 83.10% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.59 | val_acc: 0.923
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [================================&gt;-------] 83.20% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.74 | val_acc: 0.916
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [================================&gt;-------] 83.30% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.59 | val_acc: 0.923
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [================================&gt;-------] 83.40% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.74 | val_acc: 0.916
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [================================&gt;-------] 83.50% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.59 | val_acc: 0.923
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [================================&gt;-------] 83.60% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.74 | val_acc: 0.916
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [================================&gt;-------] 83.70% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.59 | val_acc: 0.923
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [================================&gt;-------] 83.80% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.74 | val_acc: 0.916
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [================================&gt;-------] 83.90% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.59 | val_acc: 0.923
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [================================&gt;-------] 84.00% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.74 | val_acc: 0.916
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [================================&gt;-------] 84.10% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.59 | val_acc: 0.923
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [================================&gt;-------] 84.20% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.74 | val_acc: 0.916
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [================================&gt;-------] 84.30% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.59 | val_acc: 0.923
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [================================&gt;-------] 84.40% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.74 | val_acc: 0.916
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [================================&gt;-------] 84.50% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.59 | val_acc: 0.923
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [================================&gt;-------] 84.60% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.74 | val_acc: 0.916
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [================================&gt;-------] 84.70% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.59 | val_acc: 0.923
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [================================&gt;-------] 84.80% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.74 | val_acc: 0.916
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [================================&gt;-------] 84.90% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.59 | val_acc: 0.923
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=================================&gt;------] 85.00% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.74 | val_acc: 0.916
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=================================&gt;------] 85.10% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.59 | val_acc: 0.923
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=================================&gt;------] 85.20% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.74 | val_acc: 0.916
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=================================&gt;------] 85.30% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.59 | val_acc: 0.923
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=================================&gt;------] 85.40% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.74 | val_acc: 0.916
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=================================&gt;------] 85.50% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.59 | val_acc: 0.923
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=================================&gt;------] 85.60% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.74 | val_acc: 0.916
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=================================&gt;------] 85.70% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.59 | val_acc: 0.923
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=================================&gt;------] 85.80% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.74 | val_acc: 0.916
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=================================&gt;------] 85.90% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.59 | val_acc: 0.923
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=================================&gt;------] 86.00% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.74 | val_acc: 0.916
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=================================&gt;------] 86.10% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.59 | val_acc: 0.923
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=================================&gt;------] 86.20% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.74 | val_acc: 0.916
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=================================&gt;------] 86.30% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.59 | val_acc: 0.923
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=================================&gt;------] 86.40% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.74 | val_acc: 0.916
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=================================&gt;------] 86.50% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.59 | val_acc: 0.923
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=================================&gt;------] 86.60% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.74 | val_acc: 0.916
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=================================&gt;------] 86.70% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.59 | val_acc: 0.923
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=================================&gt;------] 86.80% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.74 | val_acc: 0.916
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=================================&gt;------] 86.90% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.59 | val_acc: 0.923
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=================================&gt;------] 87.00% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.74 | val_acc: 0.916
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=================================&gt;------] 87.10% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.59 | val_acc: 0.923
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=================================&gt;------] 87.20% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.74 | val_acc: 0.916
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=================================&gt;------] 87.30% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.59 | val_acc: 0.923
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=================================&gt;------] 87.40% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.74 | val_acc: 0.916
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==================================&gt;-----] 87.50% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.59 | val_acc: 0.923
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==================================&gt;-----] 87.60% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.74 | val_acc: 0.916
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==================================&gt;-----] 87.70% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.59 | val_acc: 0.923
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==================================&gt;-----] 87.80% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.74 | val_acc: 0.916
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==================================&gt;-----] 87.90% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.59 | val_acc: 0.923
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==================================&gt;-----] 88.00% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.74 | val_acc: 0.916
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==================================&gt;-----] 88.10% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.59 | val_acc: 0.923
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==================================&gt;-----] 88.20% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.74 | val_acc: 0.916
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==================================&gt;-----] 88.30% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.59 | val_acc: 0.923
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==================================&gt;-----] 88.40% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.74 | val_acc: 0.916
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==================================&gt;-----] 88.50% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.59 | val_acc: 0.923
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==================================&gt;-----] 88.60% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.74 | val_acc: 0.916
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==================================&gt;-----] 88.70% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.59 | val_acc: 0.923
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==================================&gt;-----] 88.80% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.74 | val_acc: 0.916
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==================================&gt;-----] 88.90% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.59 | val_acc: 0.923
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==================================&gt;-----] 89.00% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.74 | val_acc: 0.916
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==================================&gt;-----] 89.10% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.59 | val_acc: 0.923
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==================================&gt;-----] 89.20% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.74 | val_acc: 0.916
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==================================&gt;-----] 89.30% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.59 | val_acc: 0.923
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==================================&gt;-----] 89.40% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.74 | val_acc: 0.916
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==================================&gt;-----] 89.50% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.59 | val_acc: 0.923
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==================================&gt;-----] 89.60% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.74 | val_acc: 0.916
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==================================&gt;-----] 89.70% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.59 | val_acc: 0.923
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==================================&gt;-----] 89.80% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.74 | val_acc: 0.916
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==================================&gt;-----] 89.90% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.59 | val_acc: 0.923
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===================================&gt;----] 90.00% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.74 | val_acc: 0.916
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===================================&gt;----] 90.10% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.74 | val_acc: 0.916
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===================================&gt;----] 90.20% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.74 | val_acc: 0.916
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===================================&gt;----] 90.30% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.74 | val_acc: 0.916
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===================================&gt;----] 90.40% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.74 | val_acc: 0.916
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===================================&gt;----] 90.50% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.74 | val_acc: 0.916
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===================================&gt;----] 90.60% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.74 | val_acc: 0.916
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===================================&gt;----] 90.70% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.74 | val_acc: 0.916
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===================================&gt;----] 90.80% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.74 | val_acc: 0.916
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===================================&gt;----] 90.90% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.74 | val_acc: 0.916
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===================================&gt;----] 91.00% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.74 | val_acc: 0.916
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===================================&gt;----] 91.10% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.74 | val_acc: 0.916
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===================================&gt;----] 91.20% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.74 | val_acc: 0.916
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===================================&gt;----] 91.30% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.74 | val_acc: 0.916
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===================================&gt;----] 91.40% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.74 | val_acc: 0.916
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===================================&gt;----] 91.50% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.74 | val_acc: 0.916
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===================================&gt;----] 91.60% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.74 | val_acc: 0.916
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===================================&gt;----] 91.70% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.74 | val_acc: 0.916
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===================================&gt;----] 91.80% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.74 | val_acc: 0.916
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===================================&gt;----] 91.90% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.74 | val_acc: 0.916
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===================================&gt;----] 92.00% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.74 | val_acc: 0.916
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===================================&gt;----] 92.10% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.74 | val_acc: 0.916
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===================================&gt;----] 92.20% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.74 | val_acc: 0.916
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===================================&gt;----] 92.30% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.74 | val_acc: 0.916
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===================================&gt;----] 92.40% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.74 | val_acc: 0.916
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [====================================&gt;---] 92.50% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.74 | val_acc: 0.916
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [====================================&gt;---] 92.60% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.74 | val_acc: 0.916
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [====================================&gt;---] 92.70% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.74 | val_acc: 0.916
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [====================================&gt;---] 92.80% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.74 | val_acc: 0.916
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [====================================&gt;---] 92.90% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.74 | val_acc: 0.916
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [====================================&gt;---] 93.00% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.74 | val_acc: 0.916
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [====================================&gt;---] 93.10% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.74 | val_acc: 0.916
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [====================================&gt;---] 93.20% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.74 | val_acc: 0.916
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [====================================&gt;---] 93.30% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.74 | val_acc: 0.916
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [====================================&gt;---] 93.40% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.74 | val_acc: 0.916
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [====================================&gt;---] 93.50% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.74 | val_acc: 0.916
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [====================================&gt;---] 93.60% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.74 | val_acc: 0.916
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [====================================&gt;---] 93.70% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.74 | val_acc: 0.916
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [====================================&gt;---] 93.80% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.74 | val_acc: 0.916
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [====================================&gt;---] 93.90% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.74 | val_acc: 0.916
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [====================================&gt;---] 94.00% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.74 | val_acc: 0.916
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [====================================&gt;---] 94.10% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.74 | val_acc: 0.916
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [====================================&gt;---] 94.20% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.74 | val_acc: 0.916
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [====================================&gt;---] 94.30% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.74 | val_acc: 0.916
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [====================================&gt;---] 94.40% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.74 | val_acc: 0.916
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [====================================&gt;---] 94.50% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.74 | val_acc: 0.916
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [====================================&gt;---] 94.60% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.74 | val_acc: 0.916
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [====================================&gt;---] 94.70% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.74 | val_acc: 0.916
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [====================================&gt;---] 94.80% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.74 | val_acc: 0.916
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [====================================&gt;---] 94.90% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.74 | val_acc: 0.916
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=====================================&gt;--] 95.00% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.74 | val_acc: 0.916
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=====================================&gt;--] 95.10% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.74 | val_acc: 0.916
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=====================================&gt;--] 95.20% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.74 | val_acc: 0.916
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=====================================&gt;--] 95.30% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.74 | val_acc: 0.916
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=====================================&gt;--] 95.40% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.74 | val_acc: 0.916
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=====================================&gt;--] 95.50% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.74 | val_acc: 0.916
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=====================================&gt;--] 95.60% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.74 | val_acc: 0.916
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=====================================&gt;--] 95.70% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.74 | val_acc: 0.916
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=====================================&gt;--] 95.80% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.74 | val_acc: 0.916
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=====================================&gt;--] 95.90% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.74 | val_acc: 0.916
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=====================================&gt;--] 96.00% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.74 | val_acc: 0.916
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=====================================&gt;--] 96.10% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.74 | val_acc: 0.916
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=====================================&gt;--] 96.20% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.74 | val_acc: 0.916
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=====================================&gt;--] 96.30% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.74 | val_acc: 0.916
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=====================================&gt;--] 96.40% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.74 | val_acc: 0.916
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=====================================&gt;--] 96.50% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.74 | val_acc: 0.916
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=====================================&gt;--] 96.60% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.74 | val_acc: 0.916
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=====================================&gt;--] 96.70% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.74 | val_acc: 0.916
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=====================================&gt;--] 96.80% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.74 | val_acc: 0.916
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=====================================&gt;--] 96.90% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.74 | val_acc: 0.916
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=====================================&gt;--] 97.00% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.74 | val_acc: 0.916
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=====================================&gt;--] 97.10% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.74 | val_acc: 0.916
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=====================================&gt;--] 97.20% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.74 | val_acc: 0.916
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=====================================&gt;--] 97.30% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.74 | val_acc: 0.916
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=====================================&gt;--] 97.40% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.74 | val_acc: 0.916
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [======================================&gt;-] 97.50% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.74 | val_acc: 0.916
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [======================================&gt;-] 97.60% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.74 | val_acc: 0.916
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [======================================&gt;-] 97.70% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.74 | val_acc: 0.916
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [======================================&gt;-] 97.80% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.74 | val_acc: 0.916
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [======================================&gt;-] 97.90% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.74 | val_acc: 0.916
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [======================================&gt;-] 98.00% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.74 | val_acc: 0.916
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [======================================&gt;-] 98.10% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.74 | val_acc: 0.916
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [======================================&gt;-] 98.20% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.74 | val_acc: 0.916
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [======================================&gt;-] 98.30% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.74 | val_acc: 0.916
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [======================================&gt;-] 98.40% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.74 | val_acc: 0.916
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [======================================&gt;-] 98.50% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.74 | val_acc: 0.916
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [======================================&gt;-] 98.60% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.74 | val_acc: 0.916
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [======================================&gt;-] 98.70% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.74 | val_acc: 0.916
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [======================================&gt;-] 98.80% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.74 | val_acc: 0.916
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [======================================&gt;-] 98.90% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.74 | val_acc: 0.916
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [======================================&gt;-] 99.00% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.74 | val_acc: 0.916
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [======================================&gt;-] 99.10% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.74 | val_acc: 0.916
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [======================================&gt;-] 99.20% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.74 | val_acc: 0.916
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [======================================&gt;-] 99.30% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.74 | val_acc: 0.916
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [======================================&gt;-] 99.40% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.74 | val_acc: 0.916
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [======================================&gt;-] 99.50% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.74 | val_acc: 0.916
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [======================================&gt;-] 99.60% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.74 | val_acc: 0.916
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [======================================&gt;-] 99.70% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.74 | val_acc: 0.916
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [======================================&gt;-] 99.80% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.74 | val_acc: 0.916
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [======================================&gt;-] 99.90% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.74 | val_acc: 0.916
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=======================================&gt;] 100.0% | train_error: 0.0973 | train_acc: 0.995 | val_error: 1.74 | val_acc: 0.916
</pre></div>
</div>
</div>
</div>
</div>
<div class="section" id="multiclass-classification">
<h3>Multiclass classification<a class="headerlink" href="#multiclass-classification" title="Permalink to this headline">¶</a></h3>
<p>Finally, we will demonstrate the use case of multiclass classification
using our FFNN with the famous MNIST dataset, which contain images of
digits between the range of 0 to 9.</p>
<div class="cell docutils container">
<div class="cell_input docutils container">
<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">sklearn.datasets</span> <span class="kn">import</span> <span class="n">load_digits</span>
<span class="k">def</span> <span class="nf">onehot</span><span class="p">(</span><span class="n">target</span><span class="p">:</span> <span class="n">np</span><span class="o">.</span><span class="n">ndarray</span><span class="p">):</span>
<span class="n">onehot</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">((</span><span class="n">target</span><span class="o">.</span><span class="n">size</span><span class="p">,</span> <span class="n">target</span><span class="o">.</span><span class="n">max</span><span class="p">()</span> <span class="o">+</span> <span class="mi">1</span><span class="p">))</span>
<span class="n">onehot</span><span class="p">[</span><span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="n">target</span><span class="o">.</span><span class="n">size</span><span class="p">),</span> <span class="n">target</span><span class="p">]</span> <span class="o">=</span> <span class="mi">1</span>
<span class="k">return</span> <span class="n">onehot</span>
<span class="n">digits</span> <span class="o">=</span> <span class="n">load_digits</span><span class="p">()</span>
<span class="n">X</span> <span class="o">=</span> <span class="n">digits</span><span class="o">.</span><span class="n">data</span>
<span class="n">target</span> <span class="o">=</span> <span class="n">digits</span><span class="o">.</span><span class="n">target</span>
<span class="n">target</span> <span class="o">=</span> <span class="n">onehot</span><span class="p">(</span><span class="n">target</span><span class="p">)</span>
<span class="n">input_nodes</span> <span class="o">=</span> <span class="mi">64</span>
<span class="n">hidden_nodes1</span> <span class="o">=</span> <span class="mi">100</span>
<span class="n">hidden_nodes2</span> <span class="o">=</span> <span class="mi">30</span>
<span class="n">output_nodes</span> <span class="o">=</span> <span class="mi">10</span>
<span class="n">dims</span> <span class="o">=</span> <span class="p">(</span><span class="n">input_nodes</span><span class="p">,</span> <span class="n">hidden_nodes1</span><span class="p">,</span> <span class="n">hidden_nodes2</span><span class="p">,</span> <span class="n">output_nodes</span><span class="p">)</span>
<span class="n">multiclass</span> <span class="o">=</span> <span class="n">FFNN</span><span class="p">(</span><span class="n">dims</span><span class="p">,</span> <span class="n">hidden_func</span><span class="o">=</span><span class="n">LRELU</span><span class="p">,</span> <span class="n">output_func</span><span class="o">=</span><span class="n">softmax</span><span class="p">,</span> <span class="n">cost_func</span><span class="o">=</span><span class="n">CostCrossEntropy</span><span class="p">)</span>
<span class="n">multiclass</span><span class="o">.</span><span class="n">reset_weights</span><span class="p">()</span> <span class="c1"># reset weights such that previous runs or reruns don&#39;t affect the weights</span>
<span class="n">scheduler</span> <span class="o">=</span> <span class="n">Adam</span><span class="p">(</span><span class="n">eta</span><span class="o">=</span><span class="mf">1e-4</span><span class="p">,</span> <span class="n">rho</span><span class="o">=</span><span class="mf">0.9</span><span class="p">,</span> <span class="n">rho2</span><span class="o">=</span><span class="mf">0.999</span><span class="p">)</span>
<span class="n">scores</span> <span class="o">=</span> <span class="n">multiclass</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">target</span><span class="p">,</span> <span class="n">scheduler</span><span class="p">,</span> <span class="n">epochs</span><span class="o">=</span><span class="mi">1000</span><span class="p">)</span>
</pre></div>
</div>
</div>
<div class="cell_output docutils container">
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Adam: Eta=0.0001, Lambda=0
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [----------------------------------------] 0.000% | train_error: 1.83 | train_acc: 0.823
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [----------------------------------------] 0.1000% | train_error: 1.83 | train_acc: 0.823
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [----------------------------------------] 0.2000% | train_error: 1.83 | train_acc: 0.823
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [----------------------------------------] 0.3000% | train_error: 1.83 | train_acc: 0.823
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [----------------------------------------] 0.4000% | train_error: 1.83 | train_acc: 0.823
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [----------------------------------------] 0.5000% | train_error: 1.83 | train_acc: 0.823
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [----------------------------------------] 0.6000% | train_error: 1.83 | train_acc: 0.824
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [----------------------------------------] 0.7000% | train_error: 1.83 | train_acc: 0.824
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [----------------------------------------] 0.8000% | train_error: 1.83 | train_acc: 0.824
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [----------------------------------------] 0.9000% | train_error: 1.83 | train_acc: 0.824
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [----------------------------------------] 1.000% | train_error: 1.83 | train_acc: 0.824
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [----------------------------------------] 1.100% | train_error: 1.83 | train_acc: 0.824
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [----------------------------------------] 1.200% | train_error: 1.83 | train_acc: 0.824
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [----------------------------------------] 1.300% | train_error: 1.83 | train_acc: 0.824
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [----------------------------------------] 1.400% | train_error: 1.83 | train_acc: 0.824
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [----------------------------------------] 1.500% | train_error: 1.82 | train_acc: 0.824
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [----------------------------------------] 1.600% | train_error: 1.83 | train_acc: 0.824
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [----------------------------------------] 1.700% | train_error: 1.83 | train_acc: 0.824
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [----------------------------------------] 1.800% | train_error: 1.83 | train_acc: 0.824
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [----------------------------------------] 1.900% | train_error: 1.82 | train_acc: 0.824
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [----------------------------------------] 2.000% | train_error: 1.82 | train_acc: 0.824
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [----------------------------------------] 2.100% | train_error: 1.82 | train_acc: 0.824
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [----------------------------------------] 2.200% | train_error: 1.82 | train_acc: 0.824
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [----------------------------------------] 2.300% | train_error: 1.82 | train_acc: 0.824
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [----------------------------------------] 2.400% | train_error: 1.82 | train_acc: 0.824
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [&gt;---------------------------------------] 2.500% | train_error: 1.82 | train_acc: 0.824
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [&gt;---------------------------------------] 2.600% | train_error: 1.83 | train_acc: 0.824
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [&gt;---------------------------------------] 2.700% | train_error: 1.83 | train_acc: 0.824
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [&gt;---------------------------------------] 2.800% | train_error: 1.83 | train_acc: 0.824
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [&gt;---------------------------------------] 2.900% | train_error: 1.82 | train_acc: 0.824
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [&gt;---------------------------------------] 3.000% | train_error: 1.82 | train_acc: 0.824
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [&gt;---------------------------------------] 3.100% | train_error: 1.82 | train_acc: 0.824
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [&gt;---------------------------------------] 3.200% | train_error: 1.82 | train_acc: 0.824
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [&gt;---------------------------------------] 3.300% | train_error: 1.82 | train_acc: 0.824
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [&gt;---------------------------------------] 3.400% | train_error: 1.82 | train_acc: 0.824
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [&gt;---------------------------------------] 3.500% | train_error: 1.82 | train_acc: 0.824
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [&gt;---------------------------------------] 3.600% | train_error: 1.82 | train_acc: 0.824
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [&gt;---------------------------------------] 3.700% | train_error: 1.82 | train_acc: 0.824
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [&gt;---------------------------------------] 3.800% | train_error: 1.82 | train_acc: 0.824
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [&gt;---------------------------------------] 3.900% | train_error: 1.82 | train_acc: 0.824
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [&gt;---------------------------------------] 4.000% | train_error: 1.82 | train_acc: 0.824
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [&gt;---------------------------------------] 4.100% | train_error: 1.83 | train_acc: 0.824
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [&gt;---------------------------------------] 4.200% | train_error: 1.83 | train_acc: 0.824
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [&gt;---------------------------------------] 4.300% | train_error: 1.83 | train_acc: 0.824
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [&gt;---------------------------------------] 4.400% | train_error: 1.83 | train_acc: 0.823
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [&gt;---------------------------------------] 4.500% | train_error: 1.83 | train_acc: 0.823
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [&gt;---------------------------------------] 4.600% | train_error: 1.83 | train_acc: 0.823
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [&gt;---------------------------------------] 4.700% | train_error: 1.83 | train_acc: 0.823
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [&gt;---------------------------------------] 4.800% | train_error: 1.83 | train_acc: 0.823
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [&gt;---------------------------------------] 4.900% | train_error: 1.83 | train_acc: 0.823
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=&gt;--------------------------------------] 5.000% | train_error: 1.83 | train_acc: 0.823
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=&gt;--------------------------------------] 5.100% | train_error: 1.83 | train_acc: 0.823
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=&gt;--------------------------------------] 5.200% | train_error: 1.83 | train_acc: 0.823
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=&gt;--------------------------------------] 5.300% | train_error: 1.83 | train_acc: 0.823
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=&gt;--------------------------------------] 5.400% | train_error: 1.83 | train_acc: 0.823
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=&gt;--------------------------------------] 5.500% | train_error: 1.84 | train_acc: 0.823
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=&gt;--------------------------------------] 5.600% | train_error: 1.84 | train_acc: 0.823
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=&gt;--------------------------------------] 5.700% | train_error: 1.84 | train_acc: 0.822
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=&gt;--------------------------------------] 5.800% | train_error: 1.84 | train_acc: 0.822
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=&gt;--------------------------------------] 5.900% | train_error: 1.84 | train_acc: 0.822
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=&gt;--------------------------------------] 6.000% | train_error: 1.84 | train_acc: 0.823
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=&gt;--------------------------------------] 6.100% | train_error: 1.84 | train_acc: 0.823
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=&gt;--------------------------------------] 6.200% | train_error: 1.84 | train_acc: 0.823
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=&gt;--------------------------------------] 6.300% | train_error: 1.84 | train_acc: 0.823
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=&gt;--------------------------------------] 6.400% | train_error: 1.84 | train_acc: 0.823
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=&gt;--------------------------------------] 6.500% | train_error: 1.84 | train_acc: 0.823
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=&gt;--------------------------------------] 6.600% | train_error: 1.84 | train_acc: 0.823
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=&gt;--------------------------------------] 6.700% | train_error: 1.84 | train_acc: 0.823
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=&gt;--------------------------------------] 6.800% | train_error: 1.84 | train_acc: 0.822
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=&gt;--------------------------------------] 6.900% | train_error: 1.85 | train_acc: 0.822
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=&gt;--------------------------------------] 7.000% | train_error: 1.85 | train_acc: 0.822
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=&gt;--------------------------------------] 7.100% | train_error: 1.85 | train_acc: 0.821
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=&gt;--------------------------------------] 7.200% | train_error: 1.85 | train_acc: 0.821
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=&gt;--------------------------------------] 7.300% | train_error: 1.85 | train_acc: 0.821
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=&gt;--------------------------------------] 7.400% | train_error: 1.85 | train_acc: 0.821
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==&gt;-------------------------------------] 7.500% | train_error: 1.86 | train_acc: 0.821
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==&gt;-------------------------------------] 7.600% | train_error: 1.86 | train_acc: 0.820
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==&gt;-------------------------------------] 7.700% | train_error: 1.86 | train_acc: 0.820
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==&gt;-------------------------------------] 7.800% | train_error: 1.86 | train_acc: 0.820
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==&gt;-------------------------------------] 7.900% | train_error: 1.86 | train_acc: 0.820
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==&gt;-------------------------------------] 8.000% | train_error: 1.86 | train_acc: 0.820
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==&gt;-------------------------------------] 8.100% | train_error: 1.86 | train_acc: 0.820
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==&gt;-------------------------------------] 8.200% | train_error: 1.86 | train_acc: 0.820
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==&gt;-------------------------------------] 8.300% | train_error: 1.86 | train_acc: 0.820
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==&gt;-------------------------------------] 8.400% | train_error: 1.87 | train_acc: 0.820
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==&gt;-------------------------------------] 8.500% | train_error: 1.86 | train_acc: 0.820
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==&gt;-------------------------------------] 8.600% | train_error: 1.86 | train_acc: 0.820
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==&gt;-------------------------------------] 8.700% | train_error: 1.86 | train_acc: 0.820
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==&gt;-------------------------------------] 8.800% | train_error: 1.86 | train_acc: 0.820
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==&gt;-------------------------------------] 8.900% | train_error: 1.87 | train_acc: 0.820
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==&gt;-------------------------------------] 9.000% | train_error: 1.87 | train_acc: 0.820
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==&gt;-------------------------------------] 9.100% | train_error: 1.87 | train_acc: 0.819
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==&gt;-------------------------------------] 9.200% | train_error: 1.87 | train_acc: 0.819
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==&gt;-------------------------------------] 9.300% | train_error: 1.87 | train_acc: 0.819
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==&gt;-------------------------------------] 9.400% | train_error: 1.87 | train_acc: 0.819
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==&gt;-------------------------------------] 9.500% | train_error: 1.87 | train_acc: 0.819
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==&gt;-------------------------------------] 9.600% | train_error: 1.87 | train_acc: 0.819
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==&gt;-------------------------------------] 9.700% | train_error: 1.87 | train_acc: 0.819
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==&gt;-------------------------------------] 9.800% | train_error: 1.88 | train_acc: 0.819
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==&gt;-------------------------------------] 9.900% | train_error: 1.88 | train_acc: 0.819
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===&gt;------------------------------------] 10.00% | train_error: 1.88 | train_acc: 0.819
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===&gt;------------------------------------] 10.10% | train_error: 1.88 | train_acc: 0.819
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===&gt;------------------------------------] 10.20% | train_error: 1.88 | train_acc: 0.819
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===&gt;------------------------------------] 10.30% | train_error: 1.88 | train_acc: 0.819
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===&gt;------------------------------------] 10.40% | train_error: 1.88 | train_acc: 0.819
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===&gt;------------------------------------] 10.50% | train_error: 1.88 | train_acc: 0.819
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===&gt;------------------------------------] 10.60% | train_error: 1.88 | train_acc: 0.819
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===&gt;------------------------------------] 10.70% | train_error: 1.88 | train_acc: 0.819
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===&gt;------------------------------------] 10.80% | train_error: 1.88 | train_acc: 0.819
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===&gt;------------------------------------] 10.90% | train_error: 1.88 | train_acc: 0.819
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===&gt;------------------------------------] 11.00% | train_error: 1.88 | train_acc: 0.819
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===&gt;------------------------------------] 11.10% | train_error: 1.88 | train_acc: 0.819
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===&gt;------------------------------------] 11.20% | train_error: 1.88 | train_acc: 0.819
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===&gt;------------------------------------] 11.30% | train_error: 1.88 | train_acc: 0.818
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===&gt;------------------------------------] 11.40% | train_error: 1.88 | train_acc: 0.818
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===&gt;------------------------------------] 11.50% | train_error: 1.88 | train_acc: 0.818
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===&gt;------------------------------------] 11.60% | train_error: 1.88 | train_acc: 0.819
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===&gt;------------------------------------] 11.70% | train_error: 1.88 | train_acc: 0.818
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===&gt;------------------------------------] 11.80% | train_error: 1.88 | train_acc: 0.818
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===&gt;------------------------------------] 11.90% | train_error: 1.88 | train_acc: 0.818
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===&gt;------------------------------------] 12.00% | train_error: 1.88 | train_acc: 0.818
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===&gt;------------------------------------] 12.10% | train_error: 1.88 | train_acc: 0.818
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===&gt;------------------------------------] 12.20% | train_error: 1.88 | train_acc: 0.818
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===&gt;------------------------------------] 12.30% | train_error: 1.88 | train_acc: 0.818
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===&gt;------------------------------------] 12.40% | train_error: 1.88 | train_acc: 0.818
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [====&gt;-----------------------------------] 12.50% | train_error: 1.88 | train_acc: 0.818
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [====&gt;-----------------------------------] 12.60% | train_error: 1.88 | train_acc: 0.818
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [====&gt;-----------------------------------] 12.70% | train_error: 1.88 | train_acc: 0.819
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [====&gt;-----------------------------------] 12.80% | train_error: 1.88 | train_acc: 0.819
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [====&gt;-----------------------------------] 12.90% | train_error: 1.88 | train_acc: 0.819
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [====&gt;-----------------------------------] 13.00% | train_error: 1.88 | train_acc: 0.819
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [====&gt;-----------------------------------] 13.10% | train_error: 1.87 | train_acc: 0.819
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [====&gt;-----------------------------------] 13.20% | train_error: 1.87 | train_acc: 0.819
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [====&gt;-----------------------------------] 13.30% | train_error: 1.87 | train_acc: 0.819
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [====&gt;-----------------------------------] 13.40% | train_error: 1.87 | train_acc: 0.819
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [====&gt;-----------------------------------] 13.50% | train_error: 1.87 | train_acc: 0.820
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [====&gt;-----------------------------------] 13.60% | train_error: 1.87 | train_acc: 0.820
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [====&gt;-----------------------------------] 13.70% | train_error: 1.87 | train_acc: 0.820
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [====&gt;-----------------------------------] 13.80% | train_error: 1.86 | train_acc: 0.820
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [====&gt;-----------------------------------] 13.90% | train_error: 1.86 | train_acc: 0.820
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [====&gt;-----------------------------------] 14.00% | train_error: 1.87 | train_acc: 0.820
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [====&gt;-----------------------------------] 14.10% | train_error: 1.87 | train_acc: 0.820
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [====&gt;-----------------------------------] 14.20% | train_error: 1.86 | train_acc: 0.820
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [====&gt;-----------------------------------] 14.30% | train_error: 1.86 | train_acc: 0.820
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [====&gt;-----------------------------------] 14.40% | train_error: 1.86 | train_acc: 0.820
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [====&gt;-----------------------------------] 14.50% | train_error: 1.86 | train_acc: 0.820
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [====&gt;-----------------------------------] 14.60% | train_error: 1.87 | train_acc: 0.820
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [====&gt;-----------------------------------] 14.70% | train_error: 1.87 | train_acc: 0.820
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [====&gt;-----------------------------------] 14.80% | train_error: 1.87 | train_acc: 0.820
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [====&gt;-----------------------------------] 14.90% | train_error: 1.87 | train_acc: 0.820
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=====&gt;----------------------------------] 15.00% | train_error: 1.86 | train_acc: 0.820
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=====&gt;----------------------------------] 15.10% | train_error: 1.86 | train_acc: 0.820
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=====&gt;----------------------------------] 15.20% | train_error: 1.86 | train_acc: 0.820
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=====&gt;----------------------------------] 15.30% | train_error: 1.86 | train_acc: 0.820
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=====&gt;----------------------------------] 15.40% | train_error: 1.87 | train_acc: 0.820
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=====&gt;----------------------------------] 15.50% | train_error: 1.87 | train_acc: 0.820
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=====&gt;----------------------------------] 15.60% | train_error: 1.87 | train_acc: 0.820
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=====&gt;----------------------------------] 15.70% | train_error: 1.87 | train_acc: 0.820
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=====&gt;----------------------------------] 15.80% | train_error: 1.87 | train_acc: 0.820
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=====&gt;----------------------------------] 15.90% | train_error: 1.87 | train_acc: 0.820
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=====&gt;----------------------------------] 16.00% | train_error: 1.87 | train_acc: 0.819
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=====&gt;----------------------------------] 16.10% | train_error: 1.87 | train_acc: 0.819
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=====&gt;----------------------------------] 16.20% | train_error: 1.87 | train_acc: 0.819
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=====&gt;----------------------------------] 16.30% | train_error: 1.87 | train_acc: 0.819
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=====&gt;----------------------------------] 16.40% | train_error: 1.87 | train_acc: 0.819
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=====&gt;----------------------------------] 16.50% | train_error: 1.88 | train_acc: 0.819
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=====&gt;----------------------------------] 16.60% | train_error: 1.88 | train_acc: 0.819
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=====&gt;----------------------------------] 16.70% | train_error: 1.88 | train_acc: 0.819
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=====&gt;----------------------------------] 16.80% | train_error: 1.88 | train_acc: 0.819
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=====&gt;----------------------------------] 16.90% | train_error: 1.88 | train_acc: 0.819
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=====&gt;----------------------------------] 17.00% | train_error: 1.88 | train_acc: 0.819
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=====&gt;----------------------------------] 17.10% | train_error: 1.88 | train_acc: 0.819
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=====&gt;----------------------------------] 17.20% | train_error: 1.88 | train_acc: 0.819
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=====&gt;----------------------------------] 17.30% | train_error: 1.88 | train_acc: 0.819
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=====&gt;----------------------------------] 17.40% | train_error: 1.88 | train_acc: 0.819
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [======&gt;---------------------------------] 17.50% | train_error: 1.88 | train_acc: 0.819
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [======&gt;---------------------------------] 17.60% | train_error: 1.88 | train_acc: 0.819
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [======&gt;---------------------------------] 17.70% | train_error: 1.88 | train_acc: 0.819
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [======&gt;---------------------------------] 17.80% | train_error: 1.88 | train_acc: 0.819
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [======&gt;---------------------------------] 17.90% | train_error: 1.88 | train_acc: 0.819
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [======&gt;---------------------------------] 18.00% | train_error: 1.88 | train_acc: 0.819
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [======&gt;---------------------------------] 18.10% | train_error: 1.88 | train_acc: 0.819
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [======&gt;---------------------------------] 18.20% | train_error: 1.87 | train_acc: 0.820
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [======&gt;---------------------------------] 18.30% | train_error: 1.87 | train_acc: 0.820
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [======&gt;---------------------------------] 18.40% | train_error: 1.87 | train_acc: 0.819
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [======&gt;---------------------------------] 18.50% | train_error: 1.87 | train_acc: 0.820
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [======&gt;---------------------------------] 18.60% | train_error: 1.87 | train_acc: 0.819
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [======&gt;---------------------------------] 18.70% | train_error: 1.88 | train_acc: 0.819
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [======&gt;---------------------------------] 18.80% | train_error: 1.88 | train_acc: 0.819
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [======&gt;---------------------------------] 18.90% | train_error: 1.87 | train_acc: 0.819
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [======&gt;---------------------------------] 19.00% | train_error: 1.88 | train_acc: 0.819
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [======&gt;---------------------------------] 19.10% | train_error: 1.87 | train_acc: 0.819
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [======&gt;---------------------------------] 19.20% | train_error: 1.87 | train_acc: 0.819
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [======&gt;---------------------------------] 19.30% | train_error: 1.87 | train_acc: 0.819
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [======&gt;---------------------------------] 19.40% | train_error: 1.88 | train_acc: 0.819
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [======&gt;---------------------------------] 19.50% | train_error: 1.88 | train_acc: 0.819
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [======&gt;---------------------------------] 19.60% | train_error: 1.88 | train_acc: 0.819
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [======&gt;---------------------------------] 19.70% | train_error: 1.88 | train_acc: 0.819
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [======&gt;---------------------------------] 19.80% | train_error: 1.88 | train_acc: 0.818
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [======&gt;---------------------------------] 19.90% | train_error: 1.88 | train_acc: 0.819
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=======&gt;--------------------------------] 20.00% | train_error: 1.88 | train_acc: 0.819
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=======&gt;--------------------------------] 20.10% | train_error: 1.88 | train_acc: 0.819
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=======&gt;--------------------------------] 20.20% | train_error: 1.88 | train_acc: 0.819
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=======&gt;--------------------------------] 20.30% | train_error: 1.88 | train_acc: 0.819
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=======&gt;--------------------------------] 20.40% | train_error: 1.88 | train_acc: 0.818
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=======&gt;--------------------------------] 20.50% | train_error: 1.88 | train_acc: 0.819
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=======&gt;--------------------------------] 20.60% | train_error: 1.88 | train_acc: 0.819
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=======&gt;--------------------------------] 20.70% | train_error: 1.88 | train_acc: 0.819
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=======&gt;--------------------------------] 20.80% | train_error: 1.88 | train_acc: 0.819
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=======&gt;--------------------------------] 20.90% | train_error: 1.88 | train_acc: 0.819
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=======&gt;--------------------------------] 21.00% | train_error: 1.88 | train_acc: 0.819
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=======&gt;--------------------------------] 21.10% | train_error: 1.88 | train_acc: 0.819
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=======&gt;--------------------------------] 21.20% | train_error: 1.88 | train_acc: 0.819
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=======&gt;--------------------------------] 21.30% | train_error: 1.88 | train_acc: 0.819
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=======&gt;--------------------------------] 21.40% | train_error: 1.88 | train_acc: 0.819
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=======&gt;--------------------------------] 21.50% | train_error: 1.88 | train_acc: 0.818
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=======&gt;--------------------------------] 21.60% | train_error: 1.88 | train_acc: 0.818
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=======&gt;--------------------------------] 21.70% | train_error: 1.88 | train_acc: 0.818
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=======&gt;--------------------------------] 21.80% | train_error: 1.88 | train_acc: 0.818
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=======&gt;--------------------------------] 21.90% | train_error: 1.88 | train_acc: 0.818
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=======&gt;--------------------------------] 22.00% | train_error: 1.88 | train_acc: 0.819
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=======&gt;--------------------------------] 22.10% | train_error: 1.88 | train_acc: 0.819
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=======&gt;--------------------------------] 22.20% | train_error: 1.88 | train_acc: 0.819
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=======&gt;--------------------------------] 22.30% | train_error: 1.88 | train_acc: 0.819
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=======&gt;--------------------------------] 22.40% | train_error: 1.88 | train_acc: 0.819
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [========&gt;-------------------------------] 22.50% | train_error: 1.88 | train_acc: 0.819
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [========&gt;-------------------------------] 22.60% | train_error: 1.88 | train_acc: 0.819
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [========&gt;-------------------------------] 22.70% | train_error: 1.88 | train_acc: 0.819
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [========&gt;-------------------------------] 22.80% | train_error: 1.88 | train_acc: 0.819
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [========&gt;-------------------------------] 22.90% | train_error: 1.88 | train_acc: 0.819
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [========&gt;-------------------------------] 23.00% | train_error: 1.87 | train_acc: 0.819
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [========&gt;-------------------------------] 23.10% | train_error: 1.87 | train_acc: 0.819
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [========&gt;-------------------------------] 23.20% | train_error: 1.87 | train_acc: 0.820
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [========&gt;-------------------------------] 23.30% | train_error: 1.87 | train_acc: 0.820
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [========&gt;-------------------------------] 23.40% | train_error: 1.86 | train_acc: 0.820
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [========&gt;-------------------------------] 23.50% | train_error: 1.86 | train_acc: 0.820
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [========&gt;-------------------------------] 23.60% | train_error: 1.86 | train_acc: 0.820
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [========&gt;-------------------------------] 23.70% | train_error: 1.86 | train_acc: 0.821
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [========&gt;-------------------------------] 23.80% | train_error: 1.86 | train_acc: 0.821
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [========&gt;-------------------------------] 23.90% | train_error: 1.86 | train_acc: 0.821
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [========&gt;-------------------------------] 24.00% | train_error: 1.86 | train_acc: 0.821
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [========&gt;-------------------------------] 24.10% | train_error: 1.86 | train_acc: 0.821
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [========&gt;-------------------------------] 24.20% | train_error: 1.86 | train_acc: 0.820
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [========&gt;-------------------------------] 24.30% | train_error: 1.86 | train_acc: 0.821
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [========&gt;-------------------------------] 24.40% | train_error: 1.86 | train_acc: 0.821
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [========&gt;-------------------------------] 24.50% | train_error: 1.85 | train_acc: 0.821
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [========&gt;-------------------------------] 24.60% | train_error: 1.85 | train_acc: 0.821
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [========&gt;-------------------------------] 24.70% | train_error: 1.85 | train_acc: 0.822
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [========&gt;-------------------------------] 24.80% | train_error: 1.85 | train_acc: 0.821
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [========&gt;-------------------------------] 24.90% | train_error: 1.85 | train_acc: 0.822
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=========&gt;------------------------------] 25.00% | train_error: 1.85 | train_acc: 0.822
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=========&gt;------------------------------] 25.10% | train_error: 1.84 | train_acc: 0.822
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=========&gt;------------------------------] 25.20% | train_error: 1.84 | train_acc: 0.822
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=========&gt;------------------------------] 25.30% | train_error: 1.84 | train_acc: 0.822
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=========&gt;------------------------------] 25.40% | train_error: 1.84 | train_acc: 0.822
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=========&gt;------------------------------] 25.50% | train_error: 1.84 | train_acc: 0.822
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=========&gt;------------------------------] 25.60% | train_error: 1.84 | train_acc: 0.823
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=========&gt;------------------------------] 25.70% | train_error: 1.84 | train_acc: 0.823
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=========&gt;------------------------------] 25.80% | train_error: 1.83 | train_acc: 0.823
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=========&gt;------------------------------] 25.90% | train_error: 1.83 | train_acc: 0.823
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=========&gt;------------------------------] 26.00% | train_error: 1.83 | train_acc: 0.823
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=========&gt;------------------------------] 26.10% | train_error: 1.83 | train_acc: 0.823
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=========&gt;------------------------------] 26.20% | train_error: 1.83 | train_acc: 0.823
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=========&gt;------------------------------] 26.30% | train_error: 1.83 | train_acc: 0.824
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=========&gt;------------------------------] 26.40% | train_error: 1.83 | train_acc: 0.824
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=========&gt;------------------------------] 26.50% | train_error: 1.83 | train_acc: 0.824
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=========&gt;------------------------------] 26.60% | train_error: 1.83 | train_acc: 0.823
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=========&gt;------------------------------] 26.70% | train_error: 1.83 | train_acc: 0.823
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=========&gt;------------------------------] 26.80% | train_error: 1.83 | train_acc: 0.824
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=========&gt;------------------------------] 26.90% | train_error: 1.82 | train_acc: 0.824
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=========&gt;------------------------------] 27.00% | train_error: 1.82 | train_acc: 0.824
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=========&gt;------------------------------] 27.10% | train_error: 1.82 | train_acc: 0.824
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=========&gt;------------------------------] 27.20% | train_error: 1.82 | train_acc: 0.824
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=========&gt;------------------------------] 27.30% | train_error: 1.82 | train_acc: 0.825
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=========&gt;------------------------------] 27.40% | train_error: 1.82 | train_acc: 0.825
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==========&gt;-----------------------------] 27.50% | train_error: 1.82 | train_acc: 0.825
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==========&gt;-----------------------------] 27.60% | train_error: 1.82 | train_acc: 0.825
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==========&gt;-----------------------------] 27.70% | train_error: 1.81 | train_acc: 0.825
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==========&gt;-----------------------------] 27.80% | train_error: 1.81 | train_acc: 0.825
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==========&gt;-----------------------------] 27.90% | train_error: 1.81 | train_acc: 0.825
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==========&gt;-----------------------------] 28.00% | train_error: 1.81 | train_acc: 0.825
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==========&gt;-----------------------------] 28.10% | train_error: 1.81 | train_acc: 0.825
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==========&gt;-----------------------------] 28.20% | train_error: 1.81 | train_acc: 0.825
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==========&gt;-----------------------------] 28.30% | train_error: 1.81 | train_acc: 0.825
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==========&gt;-----------------------------] 28.40% | train_error: 1.80 | train_acc: 0.826
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==========&gt;-----------------------------] 28.50% | train_error: 1.80 | train_acc: 0.826
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==========&gt;-----------------------------] 28.60% | train_error: 1.80 | train_acc: 0.826
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==========&gt;-----------------------------] 28.70% | train_error: 1.80 | train_acc: 0.826
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==========&gt;-----------------------------] 28.80% | train_error: 1.80 | train_acc: 0.826
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==========&gt;-----------------------------] 28.90% | train_error: 1.80 | train_acc: 0.826
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==========&gt;-----------------------------] 29.00% | train_error: 1.80 | train_acc: 0.826
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==========&gt;-----------------------------] 29.10% | train_error: 1.80 | train_acc: 0.826
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==========&gt;-----------------------------] 29.20% | train_error: 1.80 | train_acc: 0.827
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==========&gt;-----------------------------] 29.30% | train_error: 1.80 | train_acc: 0.827
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==========&gt;-----------------------------] 29.40% | train_error: 1.80 | train_acc: 0.826
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==========&gt;-----------------------------] 29.50% | train_error: 1.80 | train_acc: 0.827
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==========&gt;-----------------------------] 29.60% | train_error: 1.80 | train_acc: 0.827
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==========&gt;-----------------------------] 29.70% | train_error: 1.80 | train_acc: 0.826
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==========&gt;-----------------------------] 29.80% | train_error: 1.79 | train_acc: 0.827
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==========&gt;-----------------------------] 29.90% | train_error: 1.79 | train_acc: 0.827
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===========&gt;----------------------------] 30.00% | train_error: 1.79 | train_acc: 0.827
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===========&gt;----------------------------] 30.10% | train_error: 1.79 | train_acc: 0.828
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===========&gt;----------------------------] 30.20% | train_error: 1.79 | train_acc: 0.827
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===========&gt;----------------------------] 30.30% | train_error: 1.79 | train_acc: 0.827
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===========&gt;----------------------------] 30.40% | train_error: 1.79 | train_acc: 0.827
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===========&gt;----------------------------] 30.50% | train_error: 1.79 | train_acc: 0.828
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===========&gt;----------------------------] 30.60% | train_error: 1.78 | train_acc: 0.828
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===========&gt;----------------------------] 30.70% | train_error: 1.78 | train_acc: 0.828
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===========&gt;----------------------------] 30.80% | train_error: 1.78 | train_acc: 0.828
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===========&gt;----------------------------] 30.90% | train_error: 1.78 | train_acc: 0.828
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===========&gt;----------------------------] 31.00% | train_error: 1.78 | train_acc: 0.828
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===========&gt;----------------------------] 31.10% | train_error: 1.78 | train_acc: 0.828
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===========&gt;----------------------------] 31.20% | train_error: 1.78 | train_acc: 0.828
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===========&gt;----------------------------] 31.30% | train_error: 1.78 | train_acc: 0.828
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===========&gt;----------------------------] 31.40% | train_error: 1.78 | train_acc: 0.829
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===========&gt;----------------------------] 31.50% | train_error: 1.78 | train_acc: 0.829
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===========&gt;----------------------------] 31.60% | train_error: 1.77 | train_acc: 0.829
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===========&gt;----------------------------] 31.70% | train_error: 1.78 | train_acc: 0.829
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===========&gt;----------------------------] 31.80% | train_error: 1.78 | train_acc: 0.829
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===========&gt;----------------------------] 31.90% | train_error: 1.77 | train_acc: 0.829
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===========&gt;----------------------------] 32.00% | train_error: 1.77 | train_acc: 0.829
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===========&gt;----------------------------] 32.10% | train_error: 1.76 | train_acc: 0.830
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===========&gt;----------------------------] 32.20% | train_error: 1.77 | train_acc: 0.829
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===========&gt;----------------------------] 32.30% | train_error: 1.76 | train_acc: 0.830
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===========&gt;----------------------------] 32.40% | train_error: 1.76 | train_acc: 0.830
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [============&gt;---------------------------] 32.50% | train_error: 1.76 | train_acc: 0.830
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [============&gt;---------------------------] 32.60% | train_error: 1.76 | train_acc: 0.830
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [============&gt;---------------------------] 32.70% | train_error: 1.76 | train_acc: 0.830
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [============&gt;---------------------------] 32.80% | train_error: 1.76 | train_acc: 0.830
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [============&gt;---------------------------] 32.90% | train_error: 1.76 | train_acc: 0.831
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [============&gt;---------------------------] 33.00% | train_error: 1.75 | train_acc: 0.831
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [============&gt;---------------------------] 33.10% | train_error: 1.75 | train_acc: 0.831
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [============&gt;---------------------------] 33.20% | train_error: 1.75 | train_acc: 0.831
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [============&gt;---------------------------] 33.30% | train_error: 1.75 | train_acc: 0.831
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [============&gt;---------------------------] 33.40% | train_error: 1.75 | train_acc: 0.831
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [============&gt;---------------------------] 33.50% | train_error: 1.75 | train_acc: 0.831
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [============&gt;---------------------------] 33.60% | train_error: 1.75 | train_acc: 0.831
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [============&gt;---------------------------] 33.70% | train_error: 1.75 | train_acc: 0.831
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [============&gt;---------------------------] 33.80% | train_error: 1.75 | train_acc: 0.831
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [============&gt;---------------------------] 33.90% | train_error: 1.75 | train_acc: 0.831
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [============&gt;---------------------------] 34.00% | train_error: 1.75 | train_acc: 0.831
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [============&gt;---------------------------] 34.10% | train_error: 1.74 | train_acc: 0.832
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [============&gt;---------------------------] 34.20% | train_error: 1.74 | train_acc: 0.832
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [============&gt;---------------------------] 34.30% | train_error: 1.74 | train_acc: 0.832
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [============&gt;---------------------------] 34.40% | train_error: 1.74 | train_acc: 0.832
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [============&gt;---------------------------] 34.50% | train_error: 1.74 | train_acc: 0.832
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [============&gt;---------------------------] 34.60% | train_error: 1.73 | train_acc: 0.833
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [============&gt;---------------------------] 34.70% | train_error: 1.73 | train_acc: 0.833
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [============&gt;---------------------------] 34.80% | train_error: 1.73 | train_acc: 0.833
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [============&gt;---------------------------] 34.90% | train_error: 1.73 | train_acc: 0.833
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=============&gt;--------------------------] 35.00% | train_error: 1.73 | train_acc: 0.833
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=============&gt;--------------------------] 35.10% | train_error: 1.73 | train_acc: 0.834
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=============&gt;--------------------------] 35.20% | train_error: 1.73 | train_acc: 0.834
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=============&gt;--------------------------] 35.30% | train_error: 1.72 | train_acc: 0.834
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=============&gt;--------------------------] 35.40% | train_error: 1.73 | train_acc: 0.833
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=============&gt;--------------------------] 35.50% | train_error: 1.73 | train_acc: 0.833
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=============&gt;--------------------------] 35.60% | train_error: 1.74 | train_acc: 0.832
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=============&gt;--------------------------] 35.70% | train_error: 1.73 | train_acc: 0.833
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=============&gt;--------------------------] 35.80% | train_error: 1.74 | train_acc: 0.832
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=============&gt;--------------------------] 35.90% | train_error: 1.74 | train_acc: 0.832
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=============&gt;--------------------------] 36.00% | train_error: 1.73 | train_acc: 0.833
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=============&gt;--------------------------] 36.10% | train_error: 1.73 | train_acc: 0.833
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=============&gt;--------------------------] 36.20% | train_error: 1.74 | train_acc: 0.832
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=============&gt;--------------------------] 36.30% | train_error: 1.74 | train_acc: 0.832
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=============&gt;--------------------------] 36.40% | train_error: 1.74 | train_acc: 0.832
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=============&gt;--------------------------] 36.50% | train_error: 1.74 | train_acc: 0.832
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=============&gt;--------------------------] 36.60% | train_error: 1.74 | train_acc: 0.832
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=============&gt;--------------------------] 36.70% | train_error: 1.74 | train_acc: 0.832
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=============&gt;--------------------------] 36.80% | train_error: 1.74 | train_acc: 0.832
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=============&gt;--------------------------] 36.90% | train_error: 1.74 | train_acc: 0.832
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=============&gt;--------------------------] 37.00% | train_error: 1.74 | train_acc: 0.832
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=============&gt;--------------------------] 37.10% | train_error: 1.74 | train_acc: 0.832
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=============&gt;--------------------------] 37.20% | train_error: 1.74 | train_acc: 0.832
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=============&gt;--------------------------] 37.30% | train_error: 1.74 | train_acc: 0.832
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=============&gt;--------------------------] 37.40% | train_error: 1.74 | train_acc: 0.832
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==============&gt;-------------------------] 37.50% | train_error: 1.74 | train_acc: 0.832
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==============&gt;-------------------------] 37.60% | train_error: 1.73 | train_acc: 0.833
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==============&gt;-------------------------] 37.70% | train_error: 1.72 | train_acc: 0.834
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==============&gt;-------------------------] 37.80% | train_error: 1.73 | train_acc: 0.834
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==============&gt;-------------------------] 37.90% | train_error: 1.73 | train_acc: 0.833
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==============&gt;-------------------------] 38.00% | train_error: 1.73 | train_acc: 0.833
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==============&gt;-------------------------] 38.10% | train_error: 1.73 | train_acc: 0.833
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==============&gt;-------------------------] 38.20% | train_error: 1.73 | train_acc: 0.833
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==============&gt;-------------------------] 38.30% | train_error: 1.72 | train_acc: 0.834
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==============&gt;-------------------------] 38.40% | train_error: 1.73 | train_acc: 0.834
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==============&gt;-------------------------] 38.50% | train_error: 1.73 | train_acc: 0.834
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==============&gt;-------------------------] 38.60% | train_error: 1.73 | train_acc: 0.833
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==============&gt;-------------------------] 38.70% | train_error: 1.73 | train_acc: 0.834
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==============&gt;-------------------------] 38.80% | train_error: 1.72 | train_acc: 0.834
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==============&gt;-------------------------] 38.90% | train_error: 1.72 | train_acc: 0.834
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==============&gt;-------------------------] 39.00% | train_error: 1.73 | train_acc: 0.833
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==============&gt;-------------------------] 39.10% | train_error: 1.73 | train_acc: 0.833
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==============&gt;-------------------------] 39.20% | train_error: 1.73 | train_acc: 0.833
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==============&gt;-------------------------] 39.30% | train_error: 1.73 | train_acc: 0.833
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==============&gt;-------------------------] 39.40% | train_error: 1.73 | train_acc: 0.833
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==============&gt;-------------------------] 39.50% | train_error: 1.73 | train_acc: 0.833
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==============&gt;-------------------------] 39.60% | train_error: 1.73 | train_acc: 0.833
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==============&gt;-------------------------] 39.70% | train_error: 1.73 | train_acc: 0.833
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==============&gt;-------------------------] 39.80% | train_error: 1.73 | train_acc: 0.833
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==============&gt;-------------------------] 39.90% | train_error: 1.73 | train_acc: 0.833
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===============&gt;------------------------] 40.00% | train_error: 1.73 | train_acc: 0.833
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===============&gt;------------------------] 40.10% | train_error: 1.72 | train_acc: 0.834
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===============&gt;------------------------] 40.20% | train_error: 1.72 | train_acc: 0.834
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===============&gt;------------------------] 40.30% | train_error: 1.72 | train_acc: 0.834
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===============&gt;------------------------] 40.40% | train_error: 1.71 | train_acc: 0.835
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===============&gt;------------------------] 40.50% | train_error: 1.71 | train_acc: 0.835
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===============&gt;------------------------] 40.60% | train_error: 1.71 | train_acc: 0.835
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===============&gt;------------------------] 40.70% | train_error: 1.71 | train_acc: 0.835
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===============&gt;------------------------] 40.80% | train_error: 1.71 | train_acc: 0.835
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===============&gt;------------------------] 40.90% | train_error: 1.71 | train_acc: 0.835
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===============&gt;------------------------] 41.00% | train_error: 1.71 | train_acc: 0.835
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===============&gt;------------------------] 41.10% | train_error: 1.71 | train_acc: 0.835
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===============&gt;------------------------] 41.20% | train_error: 1.71 | train_acc: 0.835
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===============&gt;------------------------] 41.30% | train_error: 1.71 | train_acc: 0.835
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===============&gt;------------------------] 41.40% | train_error: 1.71 | train_acc: 0.835
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===============&gt;------------------------] 41.50% | train_error: 1.70 | train_acc: 0.836
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===============&gt;------------------------] 41.60% | train_error: 1.70 | train_acc: 0.836
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===============&gt;------------------------] 41.70% | train_error: 1.70 | train_acc: 0.836
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===============&gt;------------------------] 41.80% | train_error: 1.70 | train_acc: 0.836
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===============&gt;------------------------] 41.90% | train_error: 1.70 | train_acc: 0.836
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===============&gt;------------------------] 42.00% | train_error: 1.70 | train_acc: 0.836
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===============&gt;------------------------] 42.10% | train_error: 1.69 | train_acc: 0.837
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===============&gt;------------------------] 42.20% | train_error: 1.69 | train_acc: 0.837
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===============&gt;------------------------] 42.30% | train_error: 1.69 | train_acc: 0.837
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===============&gt;------------------------] 42.40% | train_error: 1.69 | train_acc: 0.837
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [================&gt;-----------------------] 42.50% | train_error: 1.69 | train_acc: 0.837
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [================&gt;-----------------------] 42.60% | train_error: 1.69 | train_acc: 0.837
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [================&gt;-----------------------] 42.70% | train_error: 1.68 | train_acc: 0.838
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [================&gt;-----------------------] 42.80% | train_error: 1.68 | train_acc: 0.838
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [================&gt;-----------------------] 42.90% | train_error: 1.68 | train_acc: 0.838
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [================&gt;-----------------------] 43.00% | train_error: 1.68 | train_acc: 0.838
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [================&gt;-----------------------] 43.10% | train_error: 1.68 | train_acc: 0.838
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [================&gt;-----------------------] 43.20% | train_error: 1.67 | train_acc: 0.839
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [================&gt;-----------------------] 43.30% | train_error: 1.67 | train_acc: 0.839
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [================&gt;-----------------------] 43.40% | train_error: 1.67 | train_acc: 0.839
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [================&gt;-----------------------] 43.50% | train_error: 1.67 | train_acc: 0.839
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [================&gt;-----------------------] 43.60% | train_error: 1.66 | train_acc: 0.840
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [================&gt;-----------------------] 43.70% | train_error: 1.66 | train_acc: 0.840
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [================&gt;-----------------------] 43.80% | train_error: 1.65 | train_acc: 0.840
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [================&gt;-----------------------] 43.90% | train_error: 1.65 | train_acc: 0.841
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [================&gt;-----------------------] 44.00% | train_error: 1.65 | train_acc: 0.841
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [================&gt;-----------------------] 44.10% | train_error: 1.64 | train_acc: 0.841
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [================&gt;-----------------------] 44.20% | train_error: 1.64 | train_acc: 0.842
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [================&gt;-----------------------] 44.30% | train_error: 1.62 | train_acc: 0.843
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [================&gt;-----------------------] 44.40% | train_error: 1.62 | train_acc: 0.843
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [================&gt;-----------------------] 44.50% | train_error: 1.62 | train_acc: 0.843
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [================&gt;-----------------------] 44.60% | train_error: 1.62 | train_acc: 0.844
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [================&gt;-----------------------] 44.70% | train_error: 1.61 | train_acc: 0.844
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [================&gt;-----------------------] 44.80% | train_error: 1.61 | train_acc: 0.845
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [================&gt;-----------------------] 44.90% | train_error: 1.60 | train_acc: 0.846
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=================&gt;----------------------] 45.00% | train_error: 1.59 | train_acc: 0.846
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=================&gt;----------------------] 45.10% | train_error: 1.59 | train_acc: 0.846
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=================&gt;----------------------] 45.20% | train_error: 1.59 | train_acc: 0.847
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=================&gt;----------------------] 45.30% | train_error: 1.58 | train_acc: 0.847
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=================&gt;----------------------] 45.40% | train_error: 1.58 | train_acc: 0.847
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=================&gt;----------------------] 45.50% | train_error: 1.58 | train_acc: 0.848
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=================&gt;----------------------] 45.60% | train_error: 1.57 | train_acc: 0.848
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=================&gt;----------------------] 45.70% | train_error: 1.57 | train_acc: 0.849
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=================&gt;----------------------] 45.80% | train_error: 1.56 | train_acc: 0.849
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=================&gt;----------------------] 45.90% | train_error: 1.56 | train_acc: 0.850
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=================&gt;----------------------] 46.00% | train_error: 1.55 | train_acc: 0.850
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=================&gt;----------------------] 46.10% | train_error: 1.55 | train_acc: 0.851
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=================&gt;----------------------] 46.20% | train_error: 1.55 | train_acc: 0.851
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=================&gt;----------------------] 46.30% | train_error: 1.54 | train_acc: 0.852
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=================&gt;----------------------] 46.40% | train_error: 1.53 | train_acc: 0.852
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=================&gt;----------------------] 46.50% | train_error: 1.53 | train_acc: 0.852
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=================&gt;----------------------] 46.60% | train_error: 1.53 | train_acc: 0.853
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=================&gt;----------------------] 46.70% | train_error: 1.52 | train_acc: 0.853
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=================&gt;----------------------] 46.80% | train_error: 1.52 | train_acc: 0.854
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=================&gt;----------------------] 46.90% | train_error: 1.52 | train_acc: 0.854
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=================&gt;----------------------] 47.00% | train_error: 1.51 | train_acc: 0.854
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=================&gt;----------------------] 47.10% | train_error: 1.51 | train_acc: 0.854
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=================&gt;----------------------] 47.20% | train_error: 1.50 | train_acc: 0.855
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=================&gt;----------------------] 47.30% | train_error: 1.50 | train_acc: 0.855
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=================&gt;----------------------] 47.40% | train_error: 1.49 | train_acc: 0.856
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==================&gt;---------------------] 47.50% | train_error: 1.47 | train_acc: 0.858
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==================&gt;---------------------] 47.60% | train_error: 1.47 | train_acc: 0.858
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==================&gt;---------------------] 47.70% | train_error: 1.47 | train_acc: 0.858
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==================&gt;---------------------] 47.80% | train_error: 1.46 | train_acc: 0.859
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==================&gt;---------------------] 47.90% | train_error: 1.46 | train_acc: 0.859
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==================&gt;---------------------] 48.00% | train_error: 1.46 | train_acc: 0.859
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==================&gt;---------------------] 48.10% | train_error: 1.46 | train_acc: 0.859
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==================&gt;---------------------] 48.20% | train_error: 1.46 | train_acc: 0.859
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==================&gt;---------------------] 48.30% | train_error: 1.46 | train_acc: 0.859
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==================&gt;---------------------] 48.40% | train_error: 1.45 | train_acc: 0.860
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==================&gt;---------------------] 48.50% | train_error: 1.44 | train_acc: 0.861
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==================&gt;---------------------] 48.60% | train_error: 1.44 | train_acc: 0.861
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==================&gt;---------------------] 48.70% | train_error: 1.44 | train_acc: 0.861
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==================&gt;---------------------] 48.80% | train_error: 1.44 | train_acc: 0.861
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==================&gt;---------------------] 48.90% | train_error: 1.43 | train_acc: 0.862
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==================&gt;---------------------] 49.00% | train_error: 1.42 | train_acc: 0.863
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==================&gt;---------------------] 49.10% | train_error: 1.41 | train_acc: 0.864
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==================&gt;---------------------] 49.20% | train_error: 1.40 | train_acc: 0.865
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==================&gt;---------------------] 49.30% | train_error: 1.39 | train_acc: 0.866
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==================&gt;---------------------] 49.40% | train_error: 1.39 | train_acc: 0.866
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==================&gt;---------------------] 49.50% | train_error: 1.39 | train_acc: 0.866
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==================&gt;---------------------] 49.60% | train_error: 1.39 | train_acc: 0.866
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==================&gt;---------------------] 49.70% | train_error: 1.38 | train_acc: 0.867
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==================&gt;---------------------] 49.80% | train_error: 1.38 | train_acc: 0.867
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==================&gt;---------------------] 49.90% | train_error: 1.37 | train_acc: 0.868
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===================&gt;--------------------] 50.00% | train_error: 1.36 | train_acc: 0.869
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===================&gt;--------------------] 50.10% | train_error: 1.35 | train_acc: 0.869
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===================&gt;--------------------] 50.20% | train_error: 1.35 | train_acc: 0.870
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===================&gt;--------------------] 50.30% | train_error: 1.34 | train_acc: 0.870
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===================&gt;--------------------] 50.40% | train_error: 1.34 | train_acc: 0.870
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===================&gt;--------------------] 50.50% | train_error: 1.34 | train_acc: 0.870
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===================&gt;--------------------] 50.60% | train_error: 1.34 | train_acc: 0.871
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===================&gt;--------------------] 50.70% | train_error: 1.33 | train_acc: 0.872
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===================&gt;--------------------] 50.80% | train_error: 1.32 | train_acc: 0.873
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===================&gt;--------------------] 50.90% | train_error: 1.31 | train_acc: 0.873
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===================&gt;--------------------] 51.00% | train_error: 1.31 | train_acc: 0.874
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===================&gt;--------------------] 51.10% | train_error: 1.31 | train_acc: 0.874
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===================&gt;--------------------] 51.20% | train_error: 1.31 | train_acc: 0.874
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===================&gt;--------------------] 51.30% | train_error: 1.31 | train_acc: 0.874
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===================&gt;--------------------] 51.40% | train_error: 1.31 | train_acc: 0.874
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===================&gt;--------------------] 51.50% | train_error: 1.31 | train_acc: 0.874
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===================&gt;--------------------] 51.60% | train_error: 1.30 | train_acc: 0.874
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===================&gt;--------------------] 51.70% | train_error: 1.30 | train_acc: 0.875
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===================&gt;--------------------] 51.80% | train_error: 1.30 | train_acc: 0.875
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===================&gt;--------------------] 51.90% | train_error: 1.29 | train_acc: 0.875
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===================&gt;--------------------] 52.00% | train_error: 1.29 | train_acc: 0.875
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===================&gt;--------------------] 52.10% | train_error: 1.29 | train_acc: 0.875
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===================&gt;--------------------] 52.20% | train_error: 1.29 | train_acc: 0.875
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===================&gt;--------------------] 52.30% | train_error: 1.29 | train_acc: 0.876
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===================&gt;--------------------] 52.40% | train_error: 1.28 | train_acc: 0.876
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [====================&gt;-------------------] 52.50% | train_error: 1.28 | train_acc: 0.876
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [====================&gt;-------------------] 52.60% | train_error: 1.28 | train_acc: 0.877
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [====================&gt;-------------------] 52.70% | train_error: 1.28 | train_acc: 0.877
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [====================&gt;-------------------] 52.80% | train_error: 1.27 | train_acc: 0.877
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [====================&gt;-------------------] 52.90% | train_error: 1.27 | train_acc: 0.877
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [====================&gt;-------------------] 53.00% | train_error: 1.27 | train_acc: 0.877
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [====================&gt;-------------------] 53.10% | train_error: 1.27 | train_acc: 0.878
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [====================&gt;-------------------] 53.20% | train_error: 1.26 | train_acc: 0.878
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [====================&gt;-------------------] 53.30% | train_error: 1.26 | train_acc: 0.878
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [====================&gt;-------------------] 53.40% | train_error: 1.26 | train_acc: 0.879
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [====================&gt;-------------------] 53.50% | train_error: 1.25 | train_acc: 0.879
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [====================&gt;-------------------] 53.60% | train_error: 1.25 | train_acc: 0.880
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [====================&gt;-------------------] 53.70% | train_error: 1.24 | train_acc: 0.880
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [====================&gt;-------------------] 53.80% | train_error: 1.24 | train_acc: 0.880
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [====================&gt;-------------------] 53.90% | train_error: 1.24 | train_acc: 0.880
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [====================&gt;-------------------] 54.00% | train_error: 1.23 | train_acc: 0.881
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [====================&gt;-------------------] 54.10% | train_error: 1.22 | train_acc: 0.882
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [====================&gt;-------------------] 54.20% | train_error: 1.22 | train_acc: 0.882
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [====================&gt;-------------------] 54.30% | train_error: 1.21 | train_acc: 0.883
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [====================&gt;-------------------] 54.40% | train_error: 1.21 | train_acc: 0.883
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [====================&gt;-------------------] 54.50% | train_error: 1.21 | train_acc: 0.883
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [====================&gt;-------------------] 54.60% | train_error: 1.21 | train_acc: 0.883
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [====================&gt;-------------------] 54.70% | train_error: 1.21 | train_acc: 0.884
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [====================&gt;-------------------] 54.80% | train_error: 1.20 | train_acc: 0.884
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [====================&gt;-------------------] 54.90% | train_error: 1.20 | train_acc: 0.884
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=====================&gt;------------------] 55.00% | train_error: 1.20 | train_acc: 0.884
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=====================&gt;------------------] 55.10% | train_error: 1.20 | train_acc: 0.884
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=====================&gt;------------------] 55.20% | train_error: 1.19 | train_acc: 0.885
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=====================&gt;------------------] 55.30% | train_error: 1.19 | train_acc: 0.885
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=====================&gt;------------------] 55.40% | train_error: 1.19 | train_acc: 0.885
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=====================&gt;------------------] 55.50% | train_error: 1.19 | train_acc: 0.885
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=====================&gt;------------------] 55.60% | train_error: 1.19 | train_acc: 0.885
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=====================&gt;------------------] 55.70% | train_error: 1.19 | train_acc: 0.885
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=====================&gt;------------------] 55.80% | train_error: 1.18 | train_acc: 0.886
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=====================&gt;------------------] 55.90% | train_error: 1.19 | train_acc: 0.885
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=====================&gt;------------------] 56.00% | train_error: 1.19 | train_acc: 0.885
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=====================&gt;------------------] 56.10% | train_error: 1.18 | train_acc: 0.886
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=====================&gt;------------------] 56.20% | train_error: 1.18 | train_acc: 0.886
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=====================&gt;------------------] 56.30% | train_error: 1.17 | train_acc: 0.887
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=====================&gt;------------------] 56.40% | train_error: 1.17 | train_acc: 0.887
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=====================&gt;------------------] 56.50% | train_error: 1.17 | train_acc: 0.887
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=====================&gt;------------------] 56.60% | train_error: 1.16 | train_acc: 0.888
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=====================&gt;------------------] 56.70% | train_error: 1.16 | train_acc: 0.888
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=====================&gt;------------------] 56.80% | train_error: 1.15 | train_acc: 0.889
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=====================&gt;------------------] 56.90% | train_error: 1.15 | train_acc: 0.889
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=====================&gt;------------------] 57.00% | train_error: 1.14 | train_acc: 0.890
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=====================&gt;------------------] 57.10% | train_error: 1.14 | train_acc: 0.890
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=====================&gt;------------------] 57.20% | train_error: 1.14 | train_acc: 0.890
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=====================&gt;------------------] 57.30% | train_error: 1.14 | train_acc: 0.890
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=====================&gt;------------------] 57.40% | train_error: 1.13 | train_acc: 0.891
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [======================&gt;-----------------] 57.50% | train_error: 1.13 | train_acc: 0.891
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [======================&gt;-----------------] 57.60% | train_error: 1.13 | train_acc: 0.891
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [======================&gt;-----------------] 57.70% | train_error: 1.12 | train_acc: 0.891
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [======================&gt;-----------------] 57.80% | train_error: 1.13 | train_acc: 0.891
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [======================&gt;-----------------] 57.90% | train_error: 1.12 | train_acc: 0.892
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [======================&gt;-----------------] 58.00% | train_error: 1.11 | train_acc: 0.893
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [======================&gt;-----------------] 58.10% | train_error: 1.11 | train_acc: 0.893
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [======================&gt;-----------------] 58.20% | train_error: 1.09 | train_acc: 0.894
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [======================&gt;-----------------] 58.30% | train_error: 1.09 | train_acc: 0.895
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [======================&gt;-----------------] 58.40% | train_error: 1.09 | train_acc: 0.895
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [======================&gt;-----------------] 58.50% | train_error: 1.09 | train_acc: 0.895
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [======================&gt;-----------------] 58.60% | train_error: 1.09 | train_acc: 0.895
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [======================&gt;-----------------] 58.70% | train_error: 1.09 | train_acc: 0.895
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [======================&gt;-----------------] 58.80% | train_error: 1.08 | train_acc: 0.895
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [======================&gt;-----------------] 58.90% | train_error: 1.08 | train_acc: 0.895
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [======================&gt;-----------------] 59.00% | train_error: 1.08 | train_acc: 0.896
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [======================&gt;-----------------] 59.10% | train_error: 1.08 | train_acc: 0.896
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [======================&gt;-----------------] 59.20% | train_error: 1.08 | train_acc: 0.896
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [======================&gt;-----------------] 59.30% | train_error: 1.07 | train_acc: 0.896
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [======================&gt;-----------------] 59.40% | train_error: 1.07 | train_acc: 0.897
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [======================&gt;-----------------] 59.50% | train_error: 1.06 | train_acc: 0.898
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [======================&gt;-----------------] 59.60% | train_error: 1.05 | train_acc: 0.898
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [======================&gt;-----------------] 59.70% | train_error: 1.05 | train_acc: 0.899
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [======================&gt;-----------------] 59.80% | train_error: 1.04 | train_acc: 0.899
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [======================&gt;-----------------] 59.90% | train_error: 1.04 | train_acc: 0.899
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=======================&gt;----------------] 60.00% | train_error: 1.04 | train_acc: 0.899
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=======================&gt;----------------] 60.10% | train_error: 1.04 | train_acc: 0.900
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=======================&gt;----------------] 60.20% | train_error: 1.03 | train_acc: 0.900
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=======================&gt;----------------] 60.30% | train_error: 1.03 | train_acc: 0.901
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=======================&gt;----------------] 60.40% | train_error: 1.03 | train_acc: 0.901
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=======================&gt;----------------] 60.50% | train_error: 1.02 | train_acc: 0.901
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=======================&gt;----------------] 60.60% | train_error: 1.02 | train_acc: 0.902
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=======================&gt;----------------] 60.70% | train_error: 1.01 | train_acc: 0.902
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=======================&gt;----------------] 60.80% | train_error: 1.01 | train_acc: 0.903
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=======================&gt;----------------] 60.90% | train_error: 1.00 | train_acc: 0.903
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=======================&gt;----------------] 61.00% | train_error: 1.00 | train_acc: 0.903
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=======================&gt;----------------] 61.10% | train_error: 1.00 | train_acc: 0.903
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=======================&gt;----------------] 61.20% | train_error: 1.00 | train_acc: 0.903
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=======================&gt;----------------] 61.30% | train_error: 0.999 | train_acc: 0.904
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=======================&gt;----------------] 61.40% | train_error: 0.998 | train_acc: 0.904
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=======================&gt;----------------] 61.50% | train_error: 0.998 | train_acc: 0.904
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=======================&gt;----------------] 61.60% | train_error: 0.994 | train_acc: 0.904
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=======================&gt;----------------] 61.70% | train_error: 0.994 | train_acc: 0.904
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=======================&gt;----------------] 61.80% | train_error: 0.991 | train_acc: 0.904
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=======================&gt;----------------] 61.90% | train_error: 0.987 | train_acc: 0.905
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=======================&gt;----------------] 62.00% | train_error: 0.986 | train_acc: 0.905
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=======================&gt;----------------] 62.10% | train_error: 0.978 | train_acc: 0.906
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=======================&gt;----------------] 62.20% | train_error: 0.976 | train_acc: 0.906
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=======================&gt;----------------] 62.30% | train_error: 0.976 | train_acc: 0.906
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=======================&gt;----------------] 62.40% | train_error: 0.973 | train_acc: 0.906
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [========================&gt;---------------] 62.50% | train_error: 0.972 | train_acc: 0.906
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [========================&gt;---------------] 62.60% | train_error: 0.968 | train_acc: 0.907
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [========================&gt;---------------] 62.70% | train_error: 0.968 | train_acc: 0.907
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [========================&gt;---------------] 62.80% | train_error: 0.965 | train_acc: 0.907
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [========================&gt;---------------] 62.90% | train_error: 0.966 | train_acc: 0.907
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [========================&gt;---------------] 63.00% | train_error: 0.962 | train_acc: 0.907
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [========================&gt;---------------] 63.10% | train_error: 0.954 | train_acc: 0.908
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [========================&gt;---------------] 63.20% | train_error: 0.951 | train_acc: 0.908
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [========================&gt;---------------] 63.30% | train_error: 0.948 | train_acc: 0.909
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [========================&gt;---------------] 63.40% | train_error: 0.946 | train_acc: 0.909
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [========================&gt;---------------] 63.50% | train_error: 0.943 | train_acc: 0.909
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [========================&gt;---------------] 63.60% | train_error: 0.935 | train_acc: 0.910
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [========================&gt;---------------] 63.70% | train_error: 0.926 | train_acc: 0.911
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [========================&gt;---------------] 63.80% | train_error: 0.926 | train_acc: 0.911
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [========================&gt;---------------] 63.90% | train_error: 0.926 | train_acc: 0.911
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [========================&gt;---------------] 64.00% | train_error: 0.914 | train_acc: 0.912
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [========================&gt;---------------] 64.10% | train_error: 0.914 | train_acc: 0.912
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [========================&gt;---------------] 64.20% | train_error: 0.906 | train_acc: 0.913
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [========================&gt;---------------] 64.30% | train_error: 0.903 | train_acc: 0.913
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [========================&gt;---------------] 64.40% | train_error: 0.900 | train_acc: 0.913
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [========================&gt;---------------] 64.50% | train_error: 0.895 | train_acc: 0.914
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [========================&gt;---------------] 64.60% | train_error: 0.894 | train_acc: 0.914
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [========================&gt;---------------] 64.70% | train_error: 0.891 | train_acc: 0.914
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [========================&gt;---------------] 64.80% | train_error: 0.893 | train_acc: 0.914
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [========================&gt;---------------] 64.90% | train_error: 0.893 | train_acc: 0.914
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=========================&gt;--------------] 65.00% | train_error: 0.890 | train_acc: 0.914
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=========================&gt;--------------] 65.10% | train_error: 0.889 | train_acc: 0.914
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=========================&gt;--------------] 65.20% | train_error: 0.883 | train_acc: 0.915
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=========================&gt;--------------] 65.30% | train_error: 0.880 | train_acc: 0.915
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=========================&gt;--------------] 65.40% | train_error: 0.878 | train_acc: 0.915
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=========================&gt;--------------] 65.50% | train_error: 0.876 | train_acc: 0.915
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=========================&gt;--------------] 65.60% | train_error: 0.875 | train_acc: 0.916
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=========================&gt;--------------] 65.70% | train_error: 0.874 | train_acc: 0.916
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=========================&gt;--------------] 65.80% | train_error: 0.875 | train_acc: 0.916
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=========================&gt;--------------] 65.90% | train_error: 0.873 | train_acc: 0.916
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=========================&gt;--------------] 66.00% | train_error: 0.871 | train_acc: 0.916
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=========================&gt;--------------] 66.10% | train_error: 0.868 | train_acc: 0.916
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=========================&gt;--------------] 66.20% | train_error: 0.866 | train_acc: 0.916
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=========================&gt;--------------] 66.30% | train_error: 0.864 | train_acc: 0.917
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=========================&gt;--------------] 66.40% | train_error: 0.855 | train_acc: 0.918
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=========================&gt;--------------] 66.50% | train_error: 0.851 | train_acc: 0.918
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=========================&gt;--------------] 66.60% | train_error: 0.850 | train_acc: 0.918
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=========================&gt;--------------] 66.70% | train_error: 0.850 | train_acc: 0.918
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=========================&gt;--------------] 66.80% | train_error: 0.848 | train_acc: 0.918
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=========================&gt;--------------] 66.90% | train_error: 0.844 | train_acc: 0.919
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=========================&gt;--------------] 67.00% | train_error: 0.845 | train_acc: 0.918
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=========================&gt;--------------] 67.10% | train_error: 0.842 | train_acc: 0.919
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=========================&gt;--------------] 67.20% | train_error: 0.841 | train_acc: 0.919
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=========================&gt;--------------] 67.30% | train_error: 0.841 | train_acc: 0.919
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=========================&gt;--------------] 67.40% | train_error: 0.836 | train_acc: 0.919
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==========================&gt;-------------] 67.50% | train_error: 0.821 | train_acc: 0.921
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==========================&gt;-------------] 67.60% | train_error: 0.808 | train_acc: 0.922
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==========================&gt;-------------] 67.70% | train_error: 0.805 | train_acc: 0.922
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==========================&gt;-------------] 67.80% | train_error: 0.800 | train_acc: 0.923
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==========================&gt;-------------] 67.90% | train_error: 0.799 | train_acc: 0.923
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==========================&gt;-------------] 68.00% | train_error: 0.792 | train_acc: 0.924
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==========================&gt;-------------] 68.10% | train_error: 0.791 | train_acc: 0.924
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==========================&gt;-------------] 68.20% | train_error: 0.782 | train_acc: 0.925
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==========================&gt;-------------] 68.30% | train_error: 0.774 | train_acc: 0.925
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==========================&gt;-------------] 68.40% | train_error: 0.766 | train_acc: 0.926
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==========================&gt;-------------] 68.50% | train_error: 0.763 | train_acc: 0.926
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==========================&gt;-------------] 68.60% | train_error: 0.757 | train_acc: 0.927
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==========================&gt;-------------] 68.70% | train_error: 0.753 | train_acc: 0.927
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==========================&gt;-------------] 68.80% | train_error: 0.754 | train_acc: 0.927
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==========================&gt;-------------] 68.90% | train_error: 0.747 | train_acc: 0.928
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==========================&gt;-------------] 69.00% | train_error: 0.740 | train_acc: 0.929
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==========================&gt;-------------] 69.10% | train_error: 0.746 | train_acc: 0.928
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==========================&gt;-------------] 69.20% | train_error: 0.737 | train_acc: 0.929
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==========================&gt;-------------] 69.30% | train_error: 0.744 | train_acc: 0.928
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==========================&gt;-------------] 69.40% | train_error: 0.736 | train_acc: 0.929
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==========================&gt;-------------] 69.50% | train_error: 0.745 | train_acc: 0.928
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==========================&gt;-------------] 69.60% | train_error: 0.737 | train_acc: 0.929
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==========================&gt;-------------] 69.70% | train_error: 0.736 | train_acc: 0.929
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==========================&gt;-------------] 69.80% | train_error: 0.724 | train_acc: 0.930
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==========================&gt;-------------] 69.90% | train_error: 0.722 | train_acc: 0.930
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===========================&gt;------------] 70.00% | train_error: 0.718 | train_acc: 0.931
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===========================&gt;------------] 70.10% | train_error: 0.718 | train_acc: 0.931
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===========================&gt;------------] 70.20% | train_error: 0.717 | train_acc: 0.931
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===========================&gt;------------] 70.30% | train_error: 0.712 | train_acc: 0.931
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===========================&gt;------------] 70.40% | train_error: 0.713 | train_acc: 0.931
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===========================&gt;------------] 70.50% | train_error: 0.710 | train_acc: 0.931
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===========================&gt;------------] 70.60% | train_error: 0.708 | train_acc: 0.932
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===========================&gt;------------] 70.70% | train_error: 0.705 | train_acc: 0.932
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===========================&gt;------------] 70.80% | train_error: 0.702 | train_acc: 0.932
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===========================&gt;------------] 70.90% | train_error: 0.701 | train_acc: 0.932
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===========================&gt;------------] 71.00% | train_error: 0.695 | train_acc: 0.933
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===========================&gt;------------] 71.10% | train_error: 0.694 | train_acc: 0.933
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===========================&gt;------------] 71.20% | train_error: 0.691 | train_acc: 0.933
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===========================&gt;------------] 71.30% | train_error: 0.687 | train_acc: 0.934
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===========================&gt;------------] 71.40% | train_error: 0.683 | train_acc: 0.934
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===========================&gt;------------] 71.50% | train_error: 0.682 | train_acc: 0.934
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===========================&gt;------------] 71.60% | train_error: 0.677 | train_acc: 0.935
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===========================&gt;------------] 71.70% | train_error: 0.672 | train_acc: 0.935
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===========================&gt;------------] 71.80% | train_error: 0.669 | train_acc: 0.935
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===========================&gt;------------] 71.90% | train_error: 0.669 | train_acc: 0.935
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===========================&gt;------------] 72.00% | train_error: 0.668 | train_acc: 0.936
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===========================&gt;------------] 72.10% | train_error: 0.665 | train_acc: 0.936
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===========================&gt;------------] 72.20% | train_error: 0.658 | train_acc: 0.936
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===========================&gt;------------] 72.30% | train_error: 0.655 | train_acc: 0.937
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===========================&gt;------------] 72.40% | train_error: 0.654 | train_acc: 0.937
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [============================&gt;-----------] 72.50% | train_error: 0.657 | train_acc: 0.937
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [============================&gt;-----------] 72.60% | train_error: 0.652 | train_acc: 0.937
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [============================&gt;-----------] 72.70% | train_error: 0.647 | train_acc: 0.938
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [============================&gt;-----------] 72.80% | train_error: 0.645 | train_acc: 0.938
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [============================&gt;-----------] 72.90% | train_error: 0.645 | train_acc: 0.938
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [============================&gt;-----------] 73.00% | train_error: 0.630 | train_acc: 0.939
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [============================&gt;-----------] 73.10% | train_error: 0.637 | train_acc: 0.939
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [============================&gt;-----------] 73.20% | train_error: 0.624 | train_acc: 0.940
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [============================&gt;-----------] 73.30% | train_error: 0.630 | train_acc: 0.939
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [============================&gt;-----------] 73.40% | train_error: 0.618 | train_acc: 0.940
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [============================&gt;-----------] 73.50% | train_error: 0.623 | train_acc: 0.940
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [============================&gt;-----------] 73.60% | train_error: 0.611 | train_acc: 0.941
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [============================&gt;-----------] 73.70% | train_error: 0.612 | train_acc: 0.941
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [============================&gt;-----------] 73.80% | train_error: 0.614 | train_acc: 0.941
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [============================&gt;-----------] 73.90% | train_error: 0.608 | train_acc: 0.941
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [============================&gt;-----------] 74.00% | train_error: 0.605 | train_acc: 0.942
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [============================&gt;-----------] 74.10% | train_error: 0.609 | train_acc: 0.941
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [============================&gt;-----------] 74.20% | train_error: 0.604 | train_acc: 0.942
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [============================&gt;-----------] 74.30% | train_error: 0.601 | train_acc: 0.942
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [============================&gt;-----------] 74.40% | train_error: 0.600 | train_acc: 0.942
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [============================&gt;-----------] 74.50% | train_error: 0.601 | train_acc: 0.942
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [============================&gt;-----------] 74.60% | train_error: 0.599 | train_acc: 0.942
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [============================&gt;-----------] 74.70% | train_error: 0.593 | train_acc: 0.943
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [============================&gt;-----------] 74.80% | train_error: 0.592 | train_acc: 0.943
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [============================&gt;-----------] 74.90% | train_error: 0.593 | train_acc: 0.943
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=============================&gt;----------] 75.00% | train_error: 0.589 | train_acc: 0.943
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=============================&gt;----------] 75.10% | train_error: 0.590 | train_acc: 0.943
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=============================&gt;----------] 75.20% | train_error: 0.585 | train_acc: 0.944
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=============================&gt;----------] 75.30% | train_error: 0.590 | train_acc: 0.943
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=============================&gt;----------] 75.40% | train_error: 0.580 | train_acc: 0.944
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=============================&gt;----------] 75.50% | train_error: 0.579 | train_acc: 0.944
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=============================&gt;----------] 75.60% | train_error: 0.574 | train_acc: 0.945
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=============================&gt;----------] 75.70% | train_error: 0.578 | train_acc: 0.944
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=============================&gt;----------] 75.80% | train_error: 0.560 | train_acc: 0.946
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=============================&gt;----------] 75.90% | train_error: 0.566 | train_acc: 0.945
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=============================&gt;----------] 76.00% | train_error: 0.564 | train_acc: 0.946
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=============================&gt;----------] 76.10% | train_error: 0.563 | train_acc: 0.946
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=============================&gt;----------] 76.20% | train_error: 0.559 | train_acc: 0.946
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=============================&gt;----------] 76.30% | train_error: 0.560 | train_acc: 0.946
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=============================&gt;----------] 76.40% | train_error: 0.549 | train_acc: 0.947
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=============================&gt;----------] 76.50% | train_error: 0.563 | train_acc: 0.946
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=============================&gt;----------] 76.60% | train_error: 0.542 | train_acc: 0.948
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=============================&gt;----------] 76.70% | train_error: 0.558 | train_acc: 0.946
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=============================&gt;----------] 76.80% | train_error: 0.542 | train_acc: 0.948
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=============================&gt;----------] 76.90% | train_error: 0.556 | train_acc: 0.946
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=============================&gt;----------] 77.00% | train_error: 0.536 | train_acc: 0.948
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=============================&gt;----------] 77.10% | train_error: 0.549 | train_acc: 0.947
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=============================&gt;----------] 77.20% | train_error: 0.529 | train_acc: 0.949
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=============================&gt;----------] 77.30% | train_error: 0.536 | train_acc: 0.948
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=============================&gt;----------] 77.40% | train_error: 0.534 | train_acc: 0.949
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==============================&gt;---------] 77.50% | train_error: 0.532 | train_acc: 0.949
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==============================&gt;---------] 77.60% | train_error: 0.528 | train_acc: 0.949
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==============================&gt;---------] 77.70% | train_error: 0.526 | train_acc: 0.949
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==============================&gt;---------] 77.80% | train_error: 0.515 | train_acc: 0.950
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==============================&gt;---------] 77.90% | train_error: 0.517 | train_acc: 0.950
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==============================&gt;---------] 78.00% | train_error: 0.510 | train_acc: 0.951
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==============================&gt;---------] 78.10% | train_error: 0.511 | train_acc: 0.951
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==============================&gt;---------] 78.20% | train_error: 0.510 | train_acc: 0.951
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==============================&gt;---------] 78.30% | train_error: 0.504 | train_acc: 0.951
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==============================&gt;---------] 78.40% | train_error: 0.510 | train_acc: 0.951
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==============================&gt;---------] 78.50% | train_error: 0.507 | train_acc: 0.951
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==============================&gt;---------] 78.60% | train_error: 0.509 | train_acc: 0.951
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==============================&gt;---------] 78.70% | train_error: 0.499 | train_acc: 0.952
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==============================&gt;---------] 78.80% | train_error: 0.506 | train_acc: 0.951
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==============================&gt;---------] 78.90% | train_error: 0.499 | train_acc: 0.952
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==============================&gt;---------] 79.00% | train_error: 0.504 | train_acc: 0.951
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==============================&gt;---------] 79.10% | train_error: 0.503 | train_acc: 0.952
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==============================&gt;---------] 79.20% | train_error: 0.495 | train_acc: 0.952
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==============================&gt;---------] 79.30% | train_error: 0.495 | train_acc: 0.952
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==============================&gt;---------] 79.40% | train_error: 0.488 | train_acc: 0.953
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==============================&gt;---------] 79.50% | train_error: 0.482 | train_acc: 0.953
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==============================&gt;---------] 79.60% | train_error: 0.473 | train_acc: 0.954
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==============================&gt;---------] 79.70% | train_error: 0.476 | train_acc: 0.954
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==============================&gt;---------] 79.80% | train_error: 0.477 | train_acc: 0.954
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==============================&gt;---------] 79.90% | train_error: 0.468 | train_acc: 0.955
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===============================&gt;--------] 80.00% | train_error: 0.472 | train_acc: 0.954
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===============================&gt;--------] 80.10% | train_error: 0.466 | train_acc: 0.955
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===============================&gt;--------] 80.20% | train_error: 0.473 | train_acc: 0.954
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===============================&gt;--------] 80.30% | train_error: 0.464 | train_acc: 0.955
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===============================&gt;--------] 80.40% | train_error: 0.467 | train_acc: 0.955
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===============================&gt;--------] 80.50% | train_error: 0.458 | train_acc: 0.956
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===============================&gt;--------] 80.60% | train_error: 0.461 | train_acc: 0.955
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===============================&gt;--------] 80.70% | train_error: 0.449 | train_acc: 0.957
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===============================&gt;--------] 80.80% | train_error: 0.464 | train_acc: 0.955
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===============================&gt;--------] 80.90% | train_error: 0.446 | train_acc: 0.957
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===============================&gt;--------] 81.00% | train_error: 0.456 | train_acc: 0.956
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===============================&gt;--------] 81.10% | train_error: 0.449 | train_acc: 0.957
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===============================&gt;--------] 81.20% | train_error: 0.454 | train_acc: 0.956
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===============================&gt;--------] 81.30% | train_error: 0.446 | train_acc: 0.957
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===============================&gt;--------] 81.40% | train_error: 0.443 | train_acc: 0.957
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===============================&gt;--------] 81.50% | train_error: 0.443 | train_acc: 0.957
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===============================&gt;--------] 81.60% | train_error: 0.429 | train_acc: 0.959
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===============================&gt;--------] 81.70% | train_error: 0.434 | train_acc: 0.958
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===============================&gt;--------] 81.80% | train_error: 0.427 | train_acc: 0.959
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===============================&gt;--------] 81.90% | train_error: 0.422 | train_acc: 0.959
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===============================&gt;--------] 82.00% | train_error: 0.419 | train_acc: 0.960
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===============================&gt;--------] 82.10% | train_error: 0.424 | train_acc: 0.959
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===============================&gt;--------] 82.20% | train_error: 0.424 | train_acc: 0.959
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===============================&gt;--------] 82.30% | train_error: 0.422 | train_acc: 0.959
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===============================&gt;--------] 82.40% | train_error: 0.417 | train_acc: 0.960
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [================================&gt;-------] 82.50% | train_error: 0.413 | train_acc: 0.960
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [================================&gt;-------] 82.60% | train_error: 0.408 | train_acc: 0.961
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [================================&gt;-------] 82.70% | train_error: 0.401 | train_acc: 0.961
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [================================&gt;-------] 82.80% | train_error: 0.402 | train_acc: 0.961
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [================================&gt;-------] 82.90% | train_error: 0.396 | train_acc: 0.962
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [================================&gt;-------] 83.00% | train_error: 0.402 | train_acc: 0.961
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [================================&gt;-------] 83.10% | train_error: 0.399 | train_acc: 0.962
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [================================&gt;-------] 83.20% | train_error: 0.401 | train_acc: 0.961
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [================================&gt;-------] 83.30% | train_error: 0.389 | train_acc: 0.962
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [================================&gt;-------] 83.40% | train_error: 0.397 | train_acc: 0.962
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [================================&gt;-------] 83.50% | train_error: 0.386 | train_acc: 0.963
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [================================&gt;-------] 83.60% | train_error: 0.389 | train_acc: 0.963
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [================================&gt;-------] 83.70% | train_error: 0.386 | train_acc: 0.963
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [================================&gt;-------] 83.80% | train_error: 0.385 | train_acc: 0.963
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [================================&gt;-------] 83.90% | train_error: 0.385 | train_acc: 0.963
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [================================&gt;-------] 84.00% | train_error: 0.382 | train_acc: 0.963
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [================================&gt;-------] 84.10% | train_error: 0.378 | train_acc: 0.963
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [================================&gt;-------] 84.20% | train_error: 0.374 | train_acc: 0.964
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [================================&gt;-------] 84.30% | train_error: 0.372 | train_acc: 0.964
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [================================&gt;-------] 84.40% | train_error: 0.371 | train_acc: 0.964
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [================================&gt;-------] 84.50% | train_error: 0.369 | train_acc: 0.964
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [================================&gt;-------] 84.60% | train_error: 0.364 | train_acc: 0.965
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [================================&gt;-------] 84.70% | train_error: 0.372 | train_acc: 0.964
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [================================&gt;-------] 84.80% | train_error: 0.368 | train_acc: 0.964
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [================================&gt;-------] 84.90% | train_error: 0.362 | train_acc: 0.965
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=================================&gt;------] 85.00% | train_error: 0.364 | train_acc: 0.965
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=================================&gt;------] 85.10% | train_error: 0.355 | train_acc: 0.966
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=================================&gt;------] 85.20% | train_error: 0.356 | train_acc: 0.966
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=================================&gt;------] 85.30% | train_error: 0.349 | train_acc: 0.966
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=================================&gt;------] 85.40% | train_error: 0.341 | train_acc: 0.967
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=================================&gt;------] 85.50% | train_error: 0.347 | train_acc: 0.966
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=================================&gt;------] 85.60% | train_error: 0.349 | train_acc: 0.966
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=================================&gt;------] 85.70% | train_error: 0.345 | train_acc: 0.967
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=================================&gt;------] 85.80% | train_error: 0.346 | train_acc: 0.967
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=================================&gt;------] 85.90% | train_error: 0.338 | train_acc: 0.968
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=================================&gt;------] 86.00% | train_error: 0.348 | train_acc: 0.966
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=================================&gt;------] 86.10% | train_error: 0.344 | train_acc: 0.967
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=================================&gt;------] 86.20% | train_error: 0.346 | train_acc: 0.967
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=================================&gt;------] 86.30% | train_error: 0.340 | train_acc: 0.967
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=================================&gt;------] 86.40% | train_error: 0.339 | train_acc: 0.967
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=================================&gt;------] 86.50% | train_error: 0.336 | train_acc: 0.968
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=================================&gt;------] 86.60% | train_error: 0.343 | train_acc: 0.967
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=================================&gt;------] 86.70% | train_error: 0.336 | train_acc: 0.968
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=================================&gt;------] 86.80% | train_error: 0.339 | train_acc: 0.967
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=================================&gt;------] 86.90% | train_error: 0.330 | train_acc: 0.968
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=================================&gt;------] 87.00% | train_error: 0.338 | train_acc: 0.967
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=================================&gt;------] 87.10% | train_error: 0.328 | train_acc: 0.969
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=================================&gt;------] 87.20% | train_error: 0.326 | train_acc: 0.969
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=================================&gt;------] 87.30% | train_error: 0.317 | train_acc: 0.969
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=================================&gt;------] 87.40% | train_error: 0.329 | train_acc: 0.968
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==================================&gt;-----] 87.50% | train_error: 0.317 | train_acc: 0.969
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==================================&gt;-----] 87.60% | train_error: 0.316 | train_acc: 0.970
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==================================&gt;-----] 87.70% | train_error: 0.318 | train_acc: 0.969
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==================================&gt;-----] 87.80% | train_error: 0.315 | train_acc: 0.970
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==================================&gt;-----] 87.90% | train_error: 0.309 | train_acc: 0.970
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==================================&gt;-----] 88.00% | train_error: 0.308 | train_acc: 0.970
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==================================&gt;-----] 88.10% | train_error: 0.296 | train_acc: 0.972
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==================================&gt;-----] 88.20% | train_error: 0.302 | train_acc: 0.971
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==================================&gt;-----] 88.30% | train_error: 0.298 | train_acc: 0.971
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==================================&gt;-----] 88.40% | train_error: 0.300 | train_acc: 0.971
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==================================&gt;-----] 88.50% | train_error: 0.296 | train_acc: 0.971
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==================================&gt;-----] 88.60% | train_error: 0.291 | train_acc: 0.972
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==================================&gt;-----] 88.70% | train_error: 0.287 | train_acc: 0.972
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==================================&gt;-----] 88.80% | train_error: 0.283 | train_acc: 0.973
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==================================&gt;-----] 88.90% | train_error: 0.280 | train_acc: 0.973
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==================================&gt;-----] 89.00% | train_error: 0.285 | train_acc: 0.973
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==================================&gt;-----] 89.10% | train_error: 0.277 | train_acc: 0.973
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==================================&gt;-----] 89.20% | train_error: 0.292 | train_acc: 0.972
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==================================&gt;-----] 89.30% | train_error: 0.289 | train_acc: 0.972
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==================================&gt;-----] 89.40% | train_error: 0.292 | train_acc: 0.972
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==================================&gt;-----] 89.50% | train_error: 0.287 | train_acc: 0.972
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==================================&gt;-----] 89.60% | train_error: 0.285 | train_acc: 0.973
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==================================&gt;-----] 89.70% | train_error: 0.280 | train_acc: 0.973
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==================================&gt;-----] 89.80% | train_error: 0.283 | train_acc: 0.973
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==================================&gt;-----] 89.90% | train_error: 0.277 | train_acc: 0.973
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===================================&gt;----] 90.00% | train_error: 0.285 | train_acc: 0.973
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===================================&gt;----] 90.10% | train_error: 0.278 | train_acc: 0.973
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===================================&gt;----] 90.20% | train_error: 0.266 | train_acc: 0.974
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===================================&gt;----] 90.30% | train_error: 0.264 | train_acc: 0.975
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===================================&gt;----] 90.40% | train_error: 0.271 | train_acc: 0.974
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===================================&gt;----] 90.50% | train_error: 0.265 | train_acc: 0.974
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===================================&gt;----] 90.60% | train_error: 0.265 | train_acc: 0.974
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===================================&gt;----] 90.70% | train_error: 0.258 | train_acc: 0.975
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===================================&gt;----] 90.80% | train_error: 0.263 | train_acc: 0.975
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===================================&gt;----] 90.90% | train_error: 0.251 | train_acc: 0.976
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===================================&gt;----] 91.00% | train_error: 0.248 | train_acc: 0.976
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===================================&gt;----] 91.10% | train_error: 0.248 | train_acc: 0.976
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===================================&gt;----] 91.20% | train_error: 0.250 | train_acc: 0.976
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===================================&gt;----] 91.30% | train_error: 0.243 | train_acc: 0.977
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===================================&gt;----] 91.40% | train_error: 0.241 | train_acc: 0.977
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===================================&gt;----] 91.50% | train_error: 0.239 | train_acc: 0.977
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===================================&gt;----] 91.60% | train_error: 0.240 | train_acc: 0.977
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===================================&gt;----] 91.70% | train_error: 0.239 | train_acc: 0.977
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===================================&gt;----] 91.80% | train_error: 0.239 | train_acc: 0.977
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===================================&gt;----] 91.90% | train_error: 0.235 | train_acc: 0.977
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===================================&gt;----] 92.00% | train_error: 0.233 | train_acc: 0.978
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===================================&gt;----] 92.10% | train_error: 0.234 | train_acc: 0.977
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===================================&gt;----] 92.20% | train_error: 0.240 | train_acc: 0.977
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===================================&gt;----] 92.30% | train_error: 0.238 | train_acc: 0.977
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===================================&gt;----] 92.40% | train_error: 0.226 | train_acc: 0.978
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [====================================&gt;---] 92.50% | train_error: 0.226 | train_acc: 0.978
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [====================================&gt;---] 92.60% | train_error: 0.229 | train_acc: 0.978
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [====================================&gt;---] 92.70% | train_error: 0.226 | train_acc: 0.978
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [====================================&gt;---] 92.80% | train_error: 0.218 | train_acc: 0.979
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [====================================&gt;---] 92.90% | train_error: 0.219 | train_acc: 0.979
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [====================================&gt;---] 93.00% | train_error: 0.220 | train_acc: 0.979
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [====================================&gt;---] 93.10% | train_error: 0.216 | train_acc: 0.979
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [====================================&gt;---] 93.20% | train_error: 0.217 | train_acc: 0.979
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [====================================&gt;---] 93.30% | train_error: 0.216 | train_acc: 0.979
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [====================================&gt;---] 93.40% | train_error: 0.216 | train_acc: 0.979
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [====================================&gt;---] 93.50% | train_error: 0.213 | train_acc: 0.979
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [====================================&gt;---] 93.60% | train_error: 0.213 | train_acc: 0.979
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [====================================&gt;---] 93.70% | train_error: 0.213 | train_acc: 0.979
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [====================================&gt;---] 93.80% | train_error: 0.205 | train_acc: 0.980
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [====================================&gt;---] 93.90% | train_error: 0.210 | train_acc: 0.980
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [====================================&gt;---] 94.00% | train_error: 0.211 | train_acc: 0.980
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [====================================&gt;---] 94.10% | train_error: 0.209 | train_acc: 0.980
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [====================================&gt;---] 94.20% | train_error: 0.206 | train_acc: 0.980
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [====================================&gt;---] 94.30% | train_error: 0.204 | train_acc: 0.980
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [====================================&gt;---] 94.40% | train_error: 0.210 | train_acc: 0.980
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [====================================&gt;---] 94.50% | train_error: 0.198 | train_acc: 0.981
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [====================================&gt;---] 94.60% | train_error: 0.198 | train_acc: 0.981
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [====================================&gt;---] 94.70% | train_error: 0.202 | train_acc: 0.981
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [====================================&gt;---] 94.80% | train_error: 0.201 | train_acc: 0.981
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [====================================&gt;---] 94.90% | train_error: 0.202 | train_acc: 0.981
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=====================================&gt;--] 95.00% | train_error: 0.195 | train_acc: 0.981
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=====================================&gt;--] 95.10% | train_error: 0.203 | train_acc: 0.980
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=====================================&gt;--] 95.20% | train_error: 0.197 | train_acc: 0.981
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=====================================&gt;--] 95.30% | train_error: 0.201 | train_acc: 0.981
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=====================================&gt;--] 95.40% | train_error: 0.187 | train_acc: 0.982
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=====================================&gt;--] 95.50% | train_error: 0.197 | train_acc: 0.981
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=====================================&gt;--] 95.60% | train_error: 0.187 | train_acc: 0.982
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=====================================&gt;--] 95.70% | train_error: 0.195 | train_acc: 0.981
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=====================================&gt;--] 95.80% | train_error: 0.195 | train_acc: 0.981
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=====================================&gt;--] 95.90% | train_error: 0.190 | train_acc: 0.982
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=====================================&gt;--] 96.00% | train_error: 0.190 | train_acc: 0.982
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=====================================&gt;--] 96.10% | train_error: 0.194 | train_acc: 0.981
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=====================================&gt;--] 96.20% | train_error: 0.187 | train_acc: 0.982
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=====================================&gt;--] 96.30% | train_error: 0.190 | train_acc: 0.982
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=====================================&gt;--] 96.40% | train_error: 0.190 | train_acc: 0.982
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=====================================&gt;--] 96.50% | train_error: 0.195 | train_acc: 0.981
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=====================================&gt;--] 96.60% | train_error: 0.188 | train_acc: 0.982
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=====================================&gt;--] 96.70% | train_error: 0.188 | train_acc: 0.982
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=====================================&gt;--] 96.80% | train_error: 0.190 | train_acc: 0.982
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=====================================&gt;--] 96.90% | train_error: 0.191 | train_acc: 0.982
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=====================================&gt;--] 97.00% | train_error: 0.188 | train_acc: 0.982
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=====================================&gt;--] 97.10% | train_error: 0.189 | train_acc: 0.982
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=====================================&gt;--] 97.20% | train_error: 0.175 | train_acc: 0.983
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=====================================&gt;--] 97.30% | train_error: 0.189 | train_acc: 0.982
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=====================================&gt;--] 97.40% | train_error: 0.183 | train_acc: 0.982
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [======================================&gt;-] 97.50% | train_error: 0.189 | train_acc: 0.982
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [======================================&gt;-] 97.60% | train_error: 0.181 | train_acc: 0.983
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [======================================&gt;-] 97.70% | train_error: 0.188 | train_acc: 0.982
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [======================================&gt;-] 97.80% | train_error: 0.179 | train_acc: 0.983
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [======================================&gt;-] 97.90% | train_error: 0.190 | train_acc: 0.982
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [======================================&gt;-] 98.00% | train_error: 0.186 | train_acc: 0.982
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [======================================&gt;-] 98.10% | train_error: 0.185 | train_acc: 0.982
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [======================================&gt;-] 98.20% | train_error: 0.182 | train_acc: 0.982
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [======================================&gt;-] 98.30% | train_error: 0.182 | train_acc: 0.982
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [======================================&gt;-] 98.40% | train_error: 0.171 | train_acc: 0.984
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [======================================&gt;-] 98.50% | train_error: 0.178 | train_acc: 0.983
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [======================================&gt;-] 98.60% | train_error: 0.170 | train_acc: 0.984
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [======================================&gt;-] 98.70% | train_error: 0.181 | train_acc: 0.983
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [======================================&gt;-] 98.80% | train_error: 0.166 | train_acc: 0.984
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [======================================&gt;-] 98.90% | train_error: 0.175 | train_acc: 0.983
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [======================================&gt;-] 99.00% | train_error: 0.173 | train_acc: 0.983
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [======================================&gt;-] 99.10% | train_error: 0.173 | train_acc: 0.983
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [======================================&gt;-] 99.20% | train_error: 0.171 | train_acc: 0.984
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [======================================&gt;-] 99.30% | train_error: 0.168 | train_acc: 0.984
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [======================================&gt;-] 99.40% | train_error: 0.167 | train_acc: 0.984
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [======================================&gt;-] 99.50% | train_error: 0.174 | train_acc: 0.983
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [======================================&gt;-] 99.60% | train_error: 0.158 | train_acc: 0.985
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [======================================&gt;-] 99.70% | train_error: 0.173 | train_acc: 0.983
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [======================================&gt;-] 99.80% | train_error: 0.164 | train_acc: 0.984
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [======================================&gt;-] 99.90% | train_error: 0.175 | train_acc: 0.983
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=======================================&gt;] 100.0% | train_error: 0.175 | train_acc: 0.983
</pre></div>
</div>
</div>
</div>
</div>
</div>
<div class="section" id="testing-the-xor-gate-and-other-gates">
<h2>Testing the XOR gate and other gates<a class="headerlink" href="#testing-the-xor-gate-and-other-gates" title="Permalink to this headline">¶</a></h2>
<p>Let us now use our code to test the XOR gate.</p>
<div class="cell docutils container">
<div class="cell_input docutils container">
<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">X</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span> <span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">],</span> <span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">],</span> <span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">0</span><span class="p">],[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">]],</span><span class="n">dtype</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">float64</span><span class="p">)</span>
<span class="c1"># The XOR gate</span>
<span class="n">yXOR</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span> <span class="p">[[</span> <span class="mi">0</span><span class="p">],</span> <span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="p">,[</span><span class="mi">1</span><span class="p">],</span> <span class="p">[</span><span class="mi">0</span><span class="p">]])</span>
<span class="n">input_nodes</span> <span class="o">=</span> <span class="n">X</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span>
<span class="n">output_nodes</span> <span class="o">=</span> <span class="mi">1</span>
<span class="n">logistic_regression</span> <span class="o">=</span> <span class="n">FFNN</span><span class="p">((</span><span class="n">input_nodes</span><span class="p">,</span> <span class="n">output_nodes</span><span class="p">),</span> <span class="n">output_func</span><span class="o">=</span><span class="n">sigmoid</span><span class="p">,</span> <span class="n">cost_func</span><span class="o">=</span><span class="n">CostLogReg</span><span class="p">,</span> <span class="n">seed</span><span class="o">=</span><span class="mi">2023</span><span class="p">)</span>
<span class="n">logistic_regression</span><span class="o">.</span><span class="n">reset_weights</span><span class="p">()</span> <span class="c1"># reset weights such that previous runs or reruns don&#39;t affect the weights</span>
<span class="n">scheduler</span> <span class="o">=</span> <span class="n">Adam</span><span class="p">(</span><span class="n">eta</span><span class="o">=</span><span class="mf">1e-1</span><span class="p">,</span> <span class="n">rho</span><span class="o">=</span><span class="mf">0.9</span><span class="p">,</span> <span class="n">rho2</span><span class="o">=</span><span class="mf">0.999</span><span class="p">)</span>
<span class="n">scores</span> <span class="o">=</span> <span class="n">logistic_regression</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">yXOR</span><span class="p">,</span> <span class="n">scheduler</span><span class="p">,</span> <span class="n">epochs</span><span class="o">=</span><span class="mi">1000</span><span class="p">)</span>
</pre></div>
</div>
</div>
<div class="cell_output docutils container">
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Adam: Eta=0.1, Lambda=0
[----------------------------------------] 0.000% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [----------------------------------------] 0.1000% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [----------------------------------------] 0.2000% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [----------------------------------------] 0.3000% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [----------------------------------------] 0.4000% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [----------------------------------------] 0.5000% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [----------------------------------------] 0.6000% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [----------------------------------------] 0.7000% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [----------------------------------------] 0.8000% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [----------------------------------------] 0.9000% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [----------------------------------------] 1.000% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [----------------------------------------] 1.100% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [----------------------------------------] 1.200% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [----------------------------------------] 1.300% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [----------------------------------------] 1.400% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [----------------------------------------] 1.500% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [----------------------------------------] 1.600% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [----------------------------------------] 1.700% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [----------------------------------------] 1.800% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [----------------------------------------] 1.900% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [----------------------------------------] 2.000% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [----------------------------------------] 2.100% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [----------------------------------------] 2.200% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [----------------------------------------] 2.300% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [----------------------------------------] 2.400% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [&gt;---------------------------------------] 2.500% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [&gt;---------------------------------------] 2.600% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [&gt;---------------------------------------] 2.700% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [&gt;---------------------------------------] 2.800% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [&gt;---------------------------------------] 2.900% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [&gt;---------------------------------------] 3.000% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [&gt;---------------------------------------] 3.100% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [&gt;---------------------------------------] 3.200% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [&gt;---------------------------------------] 3.300% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [&gt;---------------------------------------] 3.400% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [&gt;---------------------------------------] 3.500% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [&gt;---------------------------------------] 3.600% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [&gt;---------------------------------------] 3.700% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [&gt;---------------------------------------] 3.800% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [&gt;---------------------------------------] 3.900% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [&gt;---------------------------------------] 4.000% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [&gt;---------------------------------------] 4.100% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [&gt;---------------------------------------] 4.200% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [&gt;---------------------------------------] 4.300% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [&gt;---------------------------------------] 4.400% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [&gt;---------------------------------------] 4.500% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [&gt;---------------------------------------] 4.600% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [&gt;---------------------------------------] 4.700% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [&gt;---------------------------------------] 4.800% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [&gt;---------------------------------------] 4.900% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=&gt;--------------------------------------] 5.000% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=&gt;--------------------------------------] 5.100% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=&gt;--------------------------------------] 5.200% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=&gt;--------------------------------------] 5.300% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=&gt;--------------------------------------] 5.400% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=&gt;--------------------------------------] 5.500% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=&gt;--------------------------------------] 5.600% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=&gt;--------------------------------------] 5.700% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=&gt;--------------------------------------] 5.800% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=&gt;--------------------------------------] 5.900% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=&gt;--------------------------------------] 6.000% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=&gt;--------------------------------------] 6.100% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=&gt;--------------------------------------] 6.200% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=&gt;--------------------------------------] 6.300% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=&gt;--------------------------------------] 6.400% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=&gt;--------------------------------------] 6.500% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=&gt;--------------------------------------] 6.600% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=&gt;--------------------------------------] 6.700% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=&gt;--------------------------------------] 6.800% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=&gt;--------------------------------------] 6.900% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=&gt;--------------------------------------] 7.000% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=&gt;--------------------------------------] 7.100% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=&gt;--------------------------------------] 7.200% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=&gt;--------------------------------------] 7.300% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=&gt;--------------------------------------] 7.400% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==&gt;-------------------------------------] 7.500% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==&gt;-------------------------------------] 7.600% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==&gt;-------------------------------------] 7.700% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==&gt;-------------------------------------] 7.800% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==&gt;-------------------------------------] 7.900% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==&gt;-------------------------------------] 8.000% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==&gt;-------------------------------------] 8.100% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==&gt;-------------------------------------] 8.200% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==&gt;-------------------------------------] 8.300% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==&gt;-------------------------------------] 8.400% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==&gt;-------------------------------------] 8.500% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==&gt;-------------------------------------] 8.600% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==&gt;-------------------------------------] 8.700% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==&gt;-------------------------------------] 8.800% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==&gt;-------------------------------------] 8.900% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==&gt;-------------------------------------] 9.000% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==&gt;-------------------------------------] 9.100% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==&gt;-------------------------------------] 9.200% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==&gt;-------------------------------------] 9.300% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==&gt;-------------------------------------] 9.400% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==&gt;-------------------------------------] 9.500% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==&gt;-------------------------------------] 9.600% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==&gt;-------------------------------------] 9.700% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==&gt;-------------------------------------] 9.800% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==&gt;-------------------------------------] 9.900% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===&gt;------------------------------------] 10.00% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===&gt;------------------------------------] 10.10% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===&gt;------------------------------------] 10.20% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===&gt;------------------------------------] 10.30% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===&gt;------------------------------------] 10.40% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===&gt;------------------------------------] 10.50% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===&gt;------------------------------------] 10.60% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===&gt;------------------------------------] 10.70% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===&gt;------------------------------------] 10.80% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===&gt;------------------------------------] 10.90% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===&gt;------------------------------------] 11.00% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===&gt;------------------------------------] 11.10% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===&gt;------------------------------------] 11.20% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===&gt;------------------------------------] 11.30% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===&gt;------------------------------------] 11.40% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===&gt;------------------------------------] 11.50% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===&gt;------------------------------------] 11.60% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===&gt;------------------------------------] 11.70% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===&gt;------------------------------------] 11.80% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===&gt;------------------------------------] 11.90% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===&gt;------------------------------------] 12.00% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===&gt;------------------------------------] 12.10% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===&gt;------------------------------------] 12.20% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===&gt;------------------------------------] 12.30% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===&gt;------------------------------------] 12.40% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [====&gt;-----------------------------------] 12.50% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [====&gt;-----------------------------------] 12.60% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [====&gt;-----------------------------------] 12.70% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [====&gt;-----------------------------------] 12.80% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [====&gt;-----------------------------------] 12.90% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [====&gt;-----------------------------------] 13.00% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [====&gt;-----------------------------------] 13.10% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [====&gt;-----------------------------------] 13.20% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [====&gt;-----------------------------------] 13.30% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [====&gt;-----------------------------------] 13.40% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [====&gt;-----------------------------------] 13.50% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [====&gt;-----------------------------------] 13.60% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [====&gt;-----------------------------------] 13.70% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [====&gt;-----------------------------------] 13.80% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [====&gt;-----------------------------------] 13.90% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [====&gt;-----------------------------------] 14.00% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [====&gt;-----------------------------------] 14.10% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [====&gt;-----------------------------------] 14.20% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [====&gt;-----------------------------------] 14.30% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [====&gt;-----------------------------------] 14.40% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [====&gt;-----------------------------------] 14.50% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [====&gt;-----------------------------------] 14.60% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [====&gt;-----------------------------------] 14.70% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [====&gt;-----------------------------------] 14.80% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [====&gt;-----------------------------------] 14.90% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=====&gt;----------------------------------] 15.00% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=====&gt;----------------------------------] 15.10% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=====&gt;----------------------------------] 15.20% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=====&gt;----------------------------------] 15.30% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=====&gt;----------------------------------] 15.40% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=====&gt;----------------------------------] 15.50% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=====&gt;----------------------------------] 15.60% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=====&gt;----------------------------------] 15.70% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=====&gt;----------------------------------] 15.80% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=====&gt;----------------------------------] 15.90% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=====&gt;----------------------------------] 16.00% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=====&gt;----------------------------------] 16.10% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=====&gt;----------------------------------] 16.20% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=====&gt;----------------------------------] 16.30% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=====&gt;----------------------------------] 16.40% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=====&gt;----------------------------------] 16.50% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=====&gt;----------------------------------] 16.60% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=====&gt;----------------------------------] 16.70% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=====&gt;----------------------------------] 16.80% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=====&gt;----------------------------------] 16.90% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=====&gt;----------------------------------] 17.00% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=====&gt;----------------------------------] 17.10% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=====&gt;----------------------------------] 17.20% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=====&gt;----------------------------------] 17.30% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=====&gt;----------------------------------] 17.40% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [======&gt;---------------------------------] 17.50% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [======&gt;---------------------------------] 17.60% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [======&gt;---------------------------------] 17.70% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [======&gt;---------------------------------] 17.80% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [======&gt;---------------------------------] 17.90% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [======&gt;---------------------------------] 18.00% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [======&gt;---------------------------------] 18.10% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [======&gt;---------------------------------] 18.20% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [======&gt;---------------------------------] 18.30% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [======&gt;---------------------------------] 18.40% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [======&gt;---------------------------------] 18.50% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [======&gt;---------------------------------] 18.60% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [======&gt;---------------------------------] 18.70% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [======&gt;---------------------------------] 18.80% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [======&gt;---------------------------------] 18.90% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [======&gt;---------------------------------] 19.00% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [======&gt;---------------------------------] 19.10% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [======&gt;---------------------------------] 19.20% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [======&gt;---------------------------------] 19.30% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [======&gt;---------------------------------] 19.40% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [======&gt;---------------------------------] 19.50% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [======&gt;---------------------------------] 19.60% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [======&gt;---------------------------------] 19.70% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [======&gt;---------------------------------] 19.80% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [======&gt;---------------------------------] 19.90% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=======&gt;--------------------------------] 20.00% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=======&gt;--------------------------------] 20.10% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=======&gt;--------------------------------] 20.20% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=======&gt;--------------------------------] 20.30% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=======&gt;--------------------------------] 20.40% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=======&gt;--------------------------------] 20.50% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=======&gt;--------------------------------] 20.60% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=======&gt;--------------------------------] 20.70% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=======&gt;--------------------------------] 20.80% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=======&gt;--------------------------------] 20.90% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=======&gt;--------------------------------] 21.00% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=======&gt;--------------------------------] 21.10% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=======&gt;--------------------------------] 21.20% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=======&gt;--------------------------------] 21.30% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=======&gt;--------------------------------] 21.40% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=======&gt;--------------------------------] 21.50% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=======&gt;--------------------------------] 21.60% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=======&gt;--------------------------------] 21.70% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=======&gt;--------------------------------] 21.80% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=======&gt;--------------------------------] 21.90% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=======&gt;--------------------------------] 22.00% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=======&gt;--------------------------------] 22.10% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=======&gt;--------------------------------] 22.20% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=======&gt;--------------------------------] 22.30% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=======&gt;--------------------------------] 22.40% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [========&gt;-------------------------------] 22.50% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [========&gt;-------------------------------] 22.60% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [========&gt;-------------------------------] 22.70% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [========&gt;-------------------------------] 22.80% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [========&gt;-------------------------------] 22.90% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [========&gt;-------------------------------] 23.00% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [========&gt;-------------------------------] 23.10% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [========&gt;-------------------------------] 23.20% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [========&gt;-------------------------------] 23.30% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [========&gt;-------------------------------] 23.40% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [========&gt;-------------------------------] 23.50% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [========&gt;-------------------------------] 23.60% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [========&gt;-------------------------------] 23.70% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [========&gt;-------------------------------] 23.80% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [========&gt;-------------------------------] 23.90% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [========&gt;-------------------------------] 24.00% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [========&gt;-------------------------------] 24.10% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [========&gt;-------------------------------] 24.20% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [========&gt;-------------------------------] 24.30% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [========&gt;-------------------------------] 24.40% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [========&gt;-------------------------------] 24.50% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [========&gt;-------------------------------] 24.60% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [========&gt;-------------------------------] 24.70% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [========&gt;-------------------------------] 24.80% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [========&gt;-------------------------------] 24.90% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=========&gt;------------------------------] 25.00% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=========&gt;------------------------------] 25.10% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=========&gt;------------------------------] 25.20% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=========&gt;------------------------------] 25.30% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=========&gt;------------------------------] 25.40% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=========&gt;------------------------------] 25.50% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=========&gt;------------------------------] 25.60% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=========&gt;------------------------------] 25.70% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=========&gt;------------------------------] 25.80% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=========&gt;------------------------------] 25.90% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=========&gt;------------------------------] 26.00% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=========&gt;------------------------------] 26.10% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=========&gt;------------------------------] 26.20% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=========&gt;------------------------------] 26.30% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=========&gt;------------------------------] 26.40% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=========&gt;------------------------------] 26.50% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=========&gt;------------------------------] 26.60% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=========&gt;------------------------------] 26.70% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=========&gt;------------------------------] 26.80% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=========&gt;------------------------------] 26.90% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=========&gt;------------------------------] 27.00% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=========&gt;------------------------------] 27.10% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=========&gt;------------------------------] 27.20% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=========&gt;------------------------------] 27.30% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=========&gt;------------------------------] 27.40% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==========&gt;-----------------------------] 27.50% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==========&gt;-----------------------------] 27.60% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==========&gt;-----------------------------] 27.70% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==========&gt;-----------------------------] 27.80% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==========&gt;-----------------------------] 27.90% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==========&gt;-----------------------------] 28.00% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==========&gt;-----------------------------] 28.10% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==========&gt;-----------------------------] 28.20% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==========&gt;-----------------------------] 28.30% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==========&gt;-----------------------------] 28.40% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==========&gt;-----------------------------] 28.50% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==========&gt;-----------------------------] 28.60% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==========&gt;-----------------------------] 28.70% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==========&gt;-----------------------------] 28.80% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==========&gt;-----------------------------] 28.90% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==========&gt;-----------------------------] 29.00% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==========&gt;-----------------------------] 29.10% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==========&gt;-----------------------------] 29.20% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==========&gt;-----------------------------] 29.30% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==========&gt;-----------------------------] 29.40% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==========&gt;-----------------------------] 29.50% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==========&gt;-----------------------------] 29.60% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==========&gt;-----------------------------] 29.70% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==========&gt;-----------------------------] 29.80% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==========&gt;-----------------------------] 29.90% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===========&gt;----------------------------] 30.00% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===========&gt;----------------------------] 30.10% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===========&gt;----------------------------] 30.20% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===========&gt;----------------------------] 30.30% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===========&gt;----------------------------] 30.40% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===========&gt;----------------------------] 30.50% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===========&gt;----------------------------] 30.60% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===========&gt;----------------------------] 30.70% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===========&gt;----------------------------] 30.80% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===========&gt;----------------------------] 30.90% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===========&gt;----------------------------] 31.00% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===========&gt;----------------------------] 31.10% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===========&gt;----------------------------] 31.20% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===========&gt;----------------------------] 31.30% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===========&gt;----------------------------] 31.40% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===========&gt;----------------------------] 31.50% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===========&gt;----------------------------] 31.60% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===========&gt;----------------------------] 31.70% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===========&gt;----------------------------] 31.80% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===========&gt;----------------------------] 31.90% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===========&gt;----------------------------] 32.00% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===========&gt;----------------------------] 32.10% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===========&gt;----------------------------] 32.20% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===========&gt;----------------------------] 32.30% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===========&gt;----------------------------] 32.40% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [============&gt;---------------------------] 32.50% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [============&gt;---------------------------] 32.60% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [============&gt;---------------------------] 32.70% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [============&gt;---------------------------] 32.80% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [============&gt;---------------------------] 32.90% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [============&gt;---------------------------] 33.00% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [============&gt;---------------------------] 33.10% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [============&gt;---------------------------] 33.20% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [============&gt;---------------------------] 33.30% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [============&gt;---------------------------] 33.40% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [============&gt;---------------------------] 33.50% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [============&gt;---------------------------] 33.60% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [============&gt;---------------------------] 33.70% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [============&gt;---------------------------] 33.80% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [============&gt;---------------------------] 33.90% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [============&gt;---------------------------] 34.00% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [============&gt;---------------------------] 34.10% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [============&gt;---------------------------] 34.20% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [============&gt;---------------------------] 34.30% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [============&gt;---------------------------] 34.40% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [============&gt;---------------------------] 34.50% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [============&gt;---------------------------] 34.60% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [============&gt;---------------------------] 34.70% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [============&gt;---------------------------] 34.80% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [============&gt;---------------------------] 34.90% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=============&gt;--------------------------] 35.00% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=============&gt;--------------------------] 35.10% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=============&gt;--------------------------] 35.20% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=============&gt;--------------------------] 35.30% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=============&gt;--------------------------] 35.40% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=============&gt;--------------------------] 35.50% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=============&gt;--------------------------] 35.60% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=============&gt;--------------------------] 35.70% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=============&gt;--------------------------] 35.80% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=============&gt;--------------------------] 35.90% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=============&gt;--------------------------] 36.00% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=============&gt;--------------------------] 36.10% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=============&gt;--------------------------] 36.20% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=============&gt;--------------------------] 36.30% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=============&gt;--------------------------] 36.40% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=============&gt;--------------------------] 36.50% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=============&gt;--------------------------] 36.60% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=============&gt;--------------------------] 36.70% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=============&gt;--------------------------] 36.80% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=============&gt;--------------------------] 36.90% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=============&gt;--------------------------] 37.00% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=============&gt;--------------------------] 37.10% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=============&gt;--------------------------] 37.20% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=============&gt;--------------------------] 37.30% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=============&gt;--------------------------] 37.40% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==============&gt;-------------------------] 37.50% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==============&gt;-------------------------] 37.60% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==============&gt;-------------------------] 37.70% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==============&gt;-------------------------] 37.80% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==============&gt;-------------------------] 37.90% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==============&gt;-------------------------] 38.00% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==============&gt;-------------------------] 38.10% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==============&gt;-------------------------] 38.20% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==============&gt;-------------------------] 38.30% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==============&gt;-------------------------] 38.40% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==============&gt;-------------------------] 38.50% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==============&gt;-------------------------] 38.60% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==============&gt;-------------------------] 38.70% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==============&gt;-------------------------] 38.80% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==============&gt;-------------------------] 38.90% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==============&gt;-------------------------] 39.00% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==============&gt;-------------------------] 39.10% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==============&gt;-------------------------] 39.20% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==============&gt;-------------------------] 39.30% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==============&gt;-------------------------] 39.40% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==============&gt;-------------------------] 39.50% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==============&gt;-------------------------] 39.60% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==============&gt;-------------------------] 39.70% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==============&gt;-------------------------] 39.80% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==============&gt;-------------------------] 39.90% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===============&gt;------------------------] 40.00% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===============&gt;------------------------] 40.10% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===============&gt;------------------------] 40.20% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===============&gt;------------------------] 40.30% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===============&gt;------------------------] 40.40% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===============&gt;------------------------] 40.50% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===============&gt;------------------------] 40.60% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===============&gt;------------------------] 40.70% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===============&gt;------------------------] 40.80% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===============&gt;------------------------] 40.90% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===============&gt;------------------------] 41.00% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===============&gt;------------------------] 41.10% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===============&gt;------------------------] 41.20% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===============&gt;------------------------] 41.30% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===============&gt;------------------------] 41.40% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===============&gt;------------------------] 41.50% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===============&gt;------------------------] 41.60% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===============&gt;------------------------] 41.70% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===============&gt;------------------------] 41.80% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===============&gt;------------------------] 41.90% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===============&gt;------------------------] 42.00% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===============&gt;------------------------] 42.10% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===============&gt;------------------------] 42.20% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===============&gt;------------------------] 42.30% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===============&gt;------------------------] 42.40% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [================&gt;-----------------------] 42.50% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [================&gt;-----------------------] 42.60% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [================&gt;-----------------------] 42.70% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [================&gt;-----------------------] 42.80% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [================&gt;-----------------------] 42.90% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [================&gt;-----------------------] 43.00% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [================&gt;-----------------------] 43.10% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [================&gt;-----------------------] 43.20% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [================&gt;-----------------------] 43.30% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [================&gt;-----------------------] 43.40% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [================&gt;-----------------------] 43.50% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [================&gt;-----------------------] 43.60% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [================&gt;-----------------------] 43.70% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [================&gt;-----------------------] 43.80% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [================&gt;-----------------------] 43.90% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [================&gt;-----------------------] 44.00% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [================&gt;-----------------------] 44.10% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [================&gt;-----------------------] 44.20% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [================&gt;-----------------------] 44.30% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [================&gt;-----------------------] 44.40% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [================&gt;-----------------------] 44.50% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [================&gt;-----------------------] 44.60% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [================&gt;-----------------------] 44.70% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [================&gt;-----------------------] 44.80% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [================&gt;-----------------------] 44.90% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=================&gt;----------------------] 45.00% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=================&gt;----------------------] 45.10% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=================&gt;----------------------] 45.20% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=================&gt;----------------------] 45.30% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=================&gt;----------------------] 45.40% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=================&gt;----------------------] 45.50% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=================&gt;----------------------] 45.60% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=================&gt;----------------------] 45.70% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=================&gt;----------------------] 45.80% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=================&gt;----------------------] 45.90% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=================&gt;----------------------] 46.00% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=================&gt;----------------------] 46.10% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=================&gt;----------------------] 46.20% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=================&gt;----------------------] 46.30% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=================&gt;----------------------] 46.40% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=================&gt;----------------------] 46.50% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=================&gt;----------------------] 46.60% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=================&gt;----------------------] 46.70% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=================&gt;----------------------] 46.80% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=================&gt;----------------------] 46.90% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=================&gt;----------------------] 47.00% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=================&gt;----------------------] 47.10% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=================&gt;----------------------] 47.20% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=================&gt;----------------------] 47.30% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=================&gt;----------------------] 47.40% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==================&gt;---------------------] 47.50% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==================&gt;---------------------] 47.60% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==================&gt;---------------------] 47.70% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==================&gt;---------------------] 47.80% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==================&gt;---------------------] 47.90% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==================&gt;---------------------] 48.00% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==================&gt;---------------------] 48.10% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==================&gt;---------------------] 48.20% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==================&gt;---------------------] 48.30% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==================&gt;---------------------] 48.40% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==================&gt;---------------------] 48.50% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==================&gt;---------------------] 48.60% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==================&gt;---------------------] 48.70% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==================&gt;---------------------] 48.80% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==================&gt;---------------------] 48.90% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==================&gt;---------------------] 49.00% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==================&gt;---------------------] 49.10% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==================&gt;---------------------] 49.20% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==================&gt;---------------------] 49.30% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==================&gt;---------------------] 49.40% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==================&gt;---------------------] 49.50% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==================&gt;---------------------] 49.60% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==================&gt;---------------------] 49.70% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==================&gt;---------------------] 49.80% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==================&gt;---------------------] 49.90% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===================&gt;--------------------] 50.00% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===================&gt;--------------------] 50.10% | train_error: -0.0000000010 | train_acc: 1.00
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===================&gt;--------------------] 50.20% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===================&gt;--------------------] 50.30% | train_error: -0.0000000010 | train_acc: 1.00
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===================&gt;--------------------] 50.40% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===================&gt;--------------------] 50.50% | train_error: -0.0000000010 | train_acc: 1.00
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===================&gt;--------------------] 50.60% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===================&gt;--------------------] 50.70% | train_error: -0.0000000010 | train_acc: 1.00
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===================&gt;--------------------] 50.80% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===================&gt;--------------------] 50.90% | train_error: -0.0000000010 | train_acc: 1.00
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===================&gt;--------------------] 51.00% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===================&gt;--------------------] 51.10% | train_error: -0.0000000010 | train_acc: 1.00
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===================&gt;--------------------] 51.20% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===================&gt;--------------------] 51.30% | train_error: -0.0000000010 | train_acc: 1.00
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===================&gt;--------------------] 51.40% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===================&gt;--------------------] 51.50% | train_error: -0.0000000010 | train_acc: 1.00
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===================&gt;--------------------] 51.60% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===================&gt;--------------------] 51.70% | train_error: -0.0000000010 | train_acc: 1.00
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===================&gt;--------------------] 51.80% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===================&gt;--------------------] 51.90% | train_error: -0.0000000010 | train_acc: 1.00
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===================&gt;--------------------] 52.00% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===================&gt;--------------------] 52.10% | train_error: -0.0000000010 | train_acc: 1.00
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===================&gt;--------------------] 52.20% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===================&gt;--------------------] 52.30% | train_error: -0.0000000010 | train_acc: 1.00
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===================&gt;--------------------] 52.40% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [====================&gt;-------------------] 52.50% | train_error: -0.0000000010 | train_acc: 1.00
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [====================&gt;-------------------] 52.60% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [====================&gt;-------------------] 52.70% | train_error: -0.0000000010 | train_acc: 1.00
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [====================&gt;-------------------] 52.80% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [====================&gt;-------------------] 52.90% | train_error: -0.0000000010 | train_acc: 1.00
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [====================&gt;-------------------] 53.00% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [====================&gt;-------------------] 53.10% | train_error: -0.0000000010 | train_acc: 1.00
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [====================&gt;-------------------] 53.20% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [====================&gt;-------------------] 53.30% | train_error: -0.0000000010 | train_acc: 1.00
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [====================&gt;-------------------] 53.40% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [====================&gt;-------------------] 53.50% | train_error: -0.0000000010 | train_acc: 1.00
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [====================&gt;-------------------] 53.60% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [====================&gt;-------------------] 53.70% | train_error: -0.0000000010 | train_acc: 1.00
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [====================&gt;-------------------] 53.80% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [====================&gt;-------------------] 53.90% | train_error: -0.0000000010 | train_acc: 1.00
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [====================&gt;-------------------] 54.00% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [====================&gt;-------------------] 54.10% | train_error: -0.0000000010 | train_acc: 1.00
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [====================&gt;-------------------] 54.20% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [====================&gt;-------------------] 54.30% | train_error: -0.0000000010 | train_acc: 1.00
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [====================&gt;-------------------] 54.40% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [====================&gt;-------------------] 54.50% | train_error: -0.0000000010 | train_acc: 1.00
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [====================&gt;-------------------] 54.60% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [====================&gt;-------------------] 54.70% | train_error: -0.0000000010 | train_acc: 1.00
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [====================&gt;-------------------] 54.80% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [====================&gt;-------------------] 54.90% | train_error: -0.0000000010 | train_acc: 1.00
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=====================&gt;------------------] 55.00% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=====================&gt;------------------] 55.10% | train_error: -0.0000000010 | train_acc: 1.00
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=====================&gt;------------------] 55.20% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=====================&gt;------------------] 55.30% | train_error: -0.0000000010 | train_acc: 1.00
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=====================&gt;------------------] 55.40% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=====================&gt;------------------] 55.50% | train_error: -0.0000000010 | train_acc: 1.00
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=====================&gt;------------------] 55.60% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=====================&gt;------------------] 55.70% | train_error: -0.0000000010 | train_acc: 1.00
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=====================&gt;------------------] 55.80% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=====================&gt;------------------] 55.90% | train_error: -0.0000000010 | train_acc: 1.00
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=====================&gt;------------------] 56.00% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=====================&gt;------------------] 56.10% | train_error: -0.0000000010 | train_acc: 1.00
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=====================&gt;------------------] 56.20% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=====================&gt;------------------] 56.30% | train_error: -0.0000000010 | train_acc: 1.00
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=====================&gt;------------------] 56.40% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=====================&gt;------------------] 56.50% | train_error: -0.0000000010 | train_acc: 1.00
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=====================&gt;------------------] 56.60% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=====================&gt;------------------] 56.70% | train_error: -0.0000000010 | train_acc: 1.00
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=====================&gt;------------------] 56.80% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=====================&gt;------------------] 56.90% | train_error: -0.0000000010 | train_acc: 1.00
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=====================&gt;------------------] 57.00% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=====================&gt;------------------] 57.10% | train_error: -0.0000000010 | train_acc: 1.00
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=====================&gt;------------------] 57.20% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=====================&gt;------------------] 57.30% | train_error: -0.0000000010 | train_acc: 1.00
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=====================&gt;------------------] 57.40% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [======================&gt;-----------------] 57.50% | train_error: -0.0000000010 | train_acc: 1.00
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [======================&gt;-----------------] 57.60% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [======================&gt;-----------------] 57.70% | train_error: -0.0000000010 | train_acc: 1.00
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [======================&gt;-----------------] 57.80% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [======================&gt;-----------------] 57.90% | train_error: -0.0000000010 | train_acc: 1.00
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [======================&gt;-----------------] 58.00% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [======================&gt;-----------------] 58.10% | train_error: -0.0000000010 | train_acc: 1.00
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [======================&gt;-----------------] 58.20% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [======================&gt;-----------------] 58.30% | train_error: -0.0000000010 | train_acc: 1.00
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [======================&gt;-----------------] 58.40% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [======================&gt;-----------------] 58.50% | train_error: -0.0000000010 | train_acc: 1.00
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [======================&gt;-----------------] 58.60% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [======================&gt;-----------------] 58.70% | train_error: -0.0000000010 | train_acc: 1.00
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [======================&gt;-----------------] 58.80% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [======================&gt;-----------------] 58.90% | train_error: -0.0000000010 | train_acc: 1.00
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [======================&gt;-----------------] 59.00% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [======================&gt;-----------------] 59.10% | train_error: -0.0000000010 | train_acc: 1.00
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [======================&gt;-----------------] 59.20% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [======================&gt;-----------------] 59.30% | train_error: -0.0000000010 | train_acc: 1.00
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [======================&gt;-----------------] 59.40% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [======================&gt;-----------------] 59.50% | train_error: -0.0000000010 | train_acc: 1.00
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [======================&gt;-----------------] 59.60% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [======================&gt;-----------------] 59.70% | train_error: -0.0000000010 | train_acc: 1.00
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [======================&gt;-----------------] 59.80% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [======================&gt;-----------------] 59.90% | train_error: -0.0000000010 | train_acc: 1.00
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=======================&gt;----------------] 60.00% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=======================&gt;----------------] 60.10% | train_error: -0.0000000010 | train_acc: 1.00
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=======================&gt;----------------] 60.20% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=======================&gt;----------------] 60.30% | train_error: -0.0000000010 | train_acc: 1.00
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=======================&gt;----------------] 60.40% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=======================&gt;----------------] 60.50% | train_error: -0.0000000010 | train_acc: 1.00
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=======================&gt;----------------] 60.60% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=======================&gt;----------------] 60.70% | train_error: -0.0000000010 | train_acc: 1.00
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=======================&gt;----------------] 60.80% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=======================&gt;----------------] 60.90% | train_error: -0.0000000010 | train_acc: 1.00
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=======================&gt;----------------] 61.00% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=======================&gt;----------------] 61.10% | train_error: -0.0000000010 | train_acc: 1.00
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=======================&gt;----------------] 61.20% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=======================&gt;----------------] 61.30% | train_error: -0.0000000010 | train_acc: 1.00
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=======================&gt;----------------] 61.40% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=======================&gt;----------------] 61.50% | train_error: -0.0000000010 | train_acc: 1.00
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=======================&gt;----------------] 61.60% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=======================&gt;----------------] 61.70% | train_error: -0.0000000010 | train_acc: 1.00
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=======================&gt;----------------] 61.80% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=======================&gt;----------------] 61.90% | train_error: -0.0000000010 | train_acc: 1.00
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=======================&gt;----------------] 62.00% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=======================&gt;----------------] 62.10% | train_error: -0.0000000010 | train_acc: 1.00
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=======================&gt;----------------] 62.20% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=======================&gt;----------------] 62.30% | train_error: -0.0000000010 | train_acc: 1.00
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=======================&gt;----------------] 62.40% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [========================&gt;---------------] 62.50% | train_error: -0.0000000010 | train_acc: 1.00
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [========================&gt;---------------] 62.60% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [========================&gt;---------------] 62.70% | train_error: -0.0000000010 | train_acc: 1.00
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [========================&gt;---------------] 62.80% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [========================&gt;---------------] 62.90% | train_error: -0.0000000010 | train_acc: 1.00
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [========================&gt;---------------] 63.00% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [========================&gt;---------------] 63.10% | train_error: -0.0000000010 | train_acc: 1.00
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [========================&gt;---------------] 63.20% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [========================&gt;---------------] 63.30% | train_error: -0.0000000010 | train_acc: 1.00
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [========================&gt;---------------] 63.40% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [========================&gt;---------------] 63.50% | train_error: -0.0000000010 | train_acc: 1.00
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [========================&gt;---------------] 63.60% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [========================&gt;---------------] 63.70% | train_error: -0.0000000010 | train_acc: 1.00
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [========================&gt;---------------] 63.80% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [========================&gt;---------------] 63.90% | train_error: -0.0000000010 | train_acc: 1.00
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [========================&gt;---------------] 64.00% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [========================&gt;---------------] 64.10% | train_error: -0.0000000010 | train_acc: 1.00
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [========================&gt;---------------] 64.20% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [========================&gt;---------------] 64.30% | train_error: -0.0000000010 | train_acc: 1.00
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [========================&gt;---------------] 64.40% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [========================&gt;---------------] 64.50% | train_error: -0.0000000010 | train_acc: 1.00
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [========================&gt;---------------] 64.60% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [========================&gt;---------------] 64.70% | train_error: -0.0000000010 | train_acc: 1.00
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [========================&gt;---------------] 64.80% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [========================&gt;---------------] 64.90% | train_error: -0.0000000010 | train_acc: 1.00
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=========================&gt;--------------] 65.00% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=========================&gt;--------------] 65.10% | train_error: -0.0000000010 | train_acc: 1.00
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=========================&gt;--------------] 65.20% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=========================&gt;--------------] 65.30% | train_error: -0.0000000010 | train_acc: 1.00
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=========================&gt;--------------] 65.40% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=========================&gt;--------------] 65.50% | train_error: -0.0000000010 | train_acc: 1.00
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=========================&gt;--------------] 65.60% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=========================&gt;--------------] 65.70% | train_error: -0.0000000010 | train_acc: 1.00
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=========================&gt;--------------] 65.80% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=========================&gt;--------------] 65.90% | train_error: -0.0000000010 | train_acc: 1.00
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=========================&gt;--------------] 66.00% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=========================&gt;--------------] 66.10% | train_error: -0.0000000010 | train_acc: 1.00
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=========================&gt;--------------] 66.20% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=========================&gt;--------------] 66.30% | train_error: -0.0000000010 | train_acc: 1.00
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=========================&gt;--------------] 66.40% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=========================&gt;--------------] 66.50% | train_error: -0.0000000010 | train_acc: 1.00
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=========================&gt;--------------] 66.60% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=========================&gt;--------------] 66.70% | train_error: -0.0000000010 | train_acc: 1.00
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=========================&gt;--------------] 66.80% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=========================&gt;--------------] 66.90% | train_error: -0.0000000010 | train_acc: 1.00
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=========================&gt;--------------] 67.00% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=========================&gt;--------------] 67.10% | train_error: -0.0000000010 | train_acc: 1.00
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=========================&gt;--------------] 67.20% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=========================&gt;--------------] 67.30% | train_error: -0.0000000010 | train_acc: 1.00
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=========================&gt;--------------] 67.40% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==========================&gt;-------------] 67.50% | train_error: -0.0000000010 | train_acc: 1.00
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==========================&gt;-------------] 67.60% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==========================&gt;-------------] 67.70% | train_error: -0.0000000010 | train_acc: 1.00
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==========================&gt;-------------] 67.80% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==========================&gt;-------------] 67.90% | train_error: -0.0000000010 | train_acc: 1.00
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==========================&gt;-------------] 68.00% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==========================&gt;-------------] 68.10% | train_error: -0.0000000010 | train_acc: 1.00
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==========================&gt;-------------] 68.20% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==========================&gt;-------------] 68.30% | train_error: -0.0000000010 | train_acc: 1.00
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==========================&gt;-------------] 68.40% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==========================&gt;-------------] 68.50% | train_error: -0.0000000010 | train_acc: 1.00
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==========================&gt;-------------] 68.60% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==========================&gt;-------------] 68.70% | train_error: -0.0000000010 | train_acc: 1.00
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==========================&gt;-------------] 68.80% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==========================&gt;-------------] 68.90% | train_error: -0.0000000010 | train_acc: 1.00
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==========================&gt;-------------] 69.00% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==========================&gt;-------------] 69.10% | train_error: -0.0000000010 | train_acc: 1.00
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==========================&gt;-------------] 69.20% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==========================&gt;-------------] 69.30% | train_error: -0.0000000010 | train_acc: 1.00
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==========================&gt;-------------] 69.40% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==========================&gt;-------------] 69.50% | train_error: -0.0000000010 | train_acc: 1.00
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==========================&gt;-------------] 69.60% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==========================&gt;-------------] 69.70% | train_error: -0.0000000010 | train_acc: 1.00
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==========================&gt;-------------] 69.80% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==========================&gt;-------------] 69.90% | train_error: -0.0000000010 | train_acc: 1.00
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===========================&gt;------------] 70.00% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===========================&gt;------------] 70.10% | train_error: -0.0000000010 | train_acc: 1.00
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===========================&gt;------------] 70.20% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===========================&gt;------------] 70.30% | train_error: -0.0000000010 | train_acc: 1.00
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===========================&gt;------------] 70.40% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===========================&gt;------------] 70.50% | train_error: -0.0000000010 | train_acc: 1.00
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===========================&gt;------------] 70.60% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===========================&gt;------------] 70.70% | train_error: -0.0000000010 | train_acc: 1.00
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===========================&gt;------------] 70.80% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===========================&gt;------------] 70.90% | train_error: -0.0000000010 | train_acc: 1.00
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===========================&gt;------------] 71.00% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===========================&gt;------------] 71.10% | train_error: -0.0000000010 | train_acc: 1.00
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===========================&gt;------------] 71.20% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===========================&gt;------------] 71.30% | train_error: -0.0000000010 | train_acc: 1.00
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===========================&gt;------------] 71.40% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===========================&gt;------------] 71.50% | train_error: -0.0000000010 | train_acc: 1.00
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===========================&gt;------------] 71.60% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===========================&gt;------------] 71.70% | train_error: -0.0000000010 | train_acc: 1.00
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===========================&gt;------------] 71.80% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===========================&gt;------------] 71.90% | train_error: -0.0000000010 | train_acc: 1.00
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===========================&gt;------------] 72.00% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===========================&gt;------------] 72.10% | train_error: -0.0000000010 | train_acc: 1.00
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===========================&gt;------------] 72.20% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===========================&gt;------------] 72.30% | train_error: -0.0000000010 | train_acc: 1.00
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===========================&gt;------------] 72.40% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [============================&gt;-----------] 72.50% | train_error: -0.0000000010 | train_acc: 1.00
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [============================&gt;-----------] 72.60% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [============================&gt;-----------] 72.70% | train_error: -0.0000000010 | train_acc: 1.00
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [============================&gt;-----------] 72.80% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [============================&gt;-----------] 72.90% | train_error: -0.0000000010 | train_acc: 1.00
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [============================&gt;-----------] 73.00% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [============================&gt;-----------] 73.10% | train_error: -0.0000000010 | train_acc: 1.00
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [============================&gt;-----------] 73.20% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [============================&gt;-----------] 73.30% | train_error: -0.0000000010 | train_acc: 1.00
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [============================&gt;-----------] 73.40% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [============================&gt;-----------] 73.50% | train_error: -0.0000000010 | train_acc: 1.00
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [============================&gt;-----------] 73.60% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [============================&gt;-----------] 73.70% | train_error: -0.0000000010 | train_acc: 1.00
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [============================&gt;-----------] 73.80% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [============================&gt;-----------] 73.90% | train_error: -0.0000000010 | train_acc: 1.00
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [============================&gt;-----------] 74.00% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [============================&gt;-----------] 74.10% | train_error: -0.0000000010 | train_acc: 1.00
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [============================&gt;-----------] 74.20% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [============================&gt;-----------] 74.30% | train_error: -0.0000000010 | train_acc: 1.00
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [============================&gt;-----------] 74.40% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [============================&gt;-----------] 74.50% | train_error: -0.0000000010 | train_acc: 1.00
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [============================&gt;-----------] 74.60% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [============================&gt;-----------] 74.70% | train_error: -0.0000000010 | train_acc: 1.00
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [============================&gt;-----------] 74.80% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [============================&gt;-----------] 74.90% | train_error: -0.0000000010 | train_acc: 1.00
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=============================&gt;----------] 75.00% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=============================&gt;----------] 75.10% | train_error: -0.0000000010 | train_acc: 1.00
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=============================&gt;----------] 75.20% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=============================&gt;----------] 75.30% | train_error: -0.0000000010 | train_acc: 1.00
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=============================&gt;----------] 75.40% | train_error: 10.4 | train_acc: 0.500
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=============================&gt;----------] 75.50% | train_error: -0.0000000010 | train_acc: 1.00
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=============================&gt;----------] 75.60% | train_error: -0.0000000010 | train_acc: 1.00
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=============================&gt;----------] 75.70% | train_error: -0.0000000010 | train_acc: 1.00
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=============================&gt;----------] 75.80% | train_error: -0.0000000010 | train_acc: 1.00
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=============================&gt;----------] 75.90% | train_error: -0.0000000010 | train_acc: 1.00
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=============================&gt;----------] 76.00% | train_error: -0.0000000010 | train_acc: 1.00
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=============================&gt;----------] 76.10% | train_error: -0.0000000010 | train_acc: 1.00
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=============================&gt;----------] 76.20% | train_error: -0.0000000010 | train_acc: 1.00
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=============================&gt;----------] 76.30% | train_error: -0.0000000010 | train_acc: 1.00
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=============================&gt;----------] 76.40% | train_error: -0.0000000010 | train_acc: 1.00
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=============================&gt;----------] 76.50% | train_error: -0.0000000010 | train_acc: 1.00
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=============================&gt;----------] 76.60% | train_error: -0.0000000010 | train_acc: 1.00
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=============================&gt;----------] 76.70% | train_error: -0.0000000010 | train_acc: 1.00
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=============================&gt;----------] 76.80% | train_error: -0.0000000010 | train_acc: 1.00
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=============================&gt;----------] 76.90% | train_error: -0.0000000010 | train_acc: 1.00
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=============================&gt;----------] 77.00% | train_error: -0.0000000010 | train_acc: 1.00
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=============================&gt;----------] 77.10% | train_error: -0.0000000010 | train_acc: 1.00
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=============================&gt;----------] 77.20% | train_error: -0.0000000010 | train_acc: 1.00
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=============================&gt;----------] 77.30% | train_error: -0.0000000010 | train_acc: 1.00
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=============================&gt;----------] 77.40% | train_error: -0.0000000010 | train_acc: 1.00
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==============================&gt;---------] 77.50% | train_error: -0.0000000010 | train_acc: 1.00
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==============================&gt;---------] 77.60% | train_error: -0.0000000010 | train_acc: 1.00
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==============================&gt;---------] 77.70% | train_error: -0.0000000010 | train_acc: 1.00
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==============================&gt;---------] 77.80% | train_error: -0.0000000010 | train_acc: 1.00
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==============================&gt;---------] 77.90% | train_error: -0.0000000010 | train_acc: 1.00
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==============================&gt;---------] 78.00% | train_error: -0.0000000010 | train_acc: 1.00
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==============================&gt;---------] 78.10% | train_error: -0.0000000010 | train_acc: 1.00
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==============================&gt;---------] 78.20% | train_error: -0.0000000010 | train_acc: 1.00
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==============================&gt;---------] 78.30% | train_error: -0.0000000010 | train_acc: 1.00
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==============================&gt;---------] 78.40% | train_error: -0.0000000010 | train_acc: 1.00
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==============================&gt;---------] 78.50% | train_error: -0.0000000010 | train_acc: 1.00
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==============================&gt;---------] 78.60% | train_error: -0.0000000010 | train_acc: 1.00
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==============================&gt;---------] 78.70% | train_error: -0.0000000010 | train_acc: 1.00
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==============================&gt;---------] 78.80% | train_error: -0.0000000010 | train_acc: 1.00
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==============================&gt;---------] 78.90% | train_error: -0.0000000010 | train_acc: 1.00
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==============================&gt;---------] 79.00% | train_error: -0.0000000010 | train_acc: 1.00
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==============================&gt;---------] 79.10% | train_error: -0.0000000010 | train_acc: 1.00
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==============================&gt;---------] 79.20% | train_error: -0.0000000010 | train_acc: 1.00
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==============================&gt;---------] 79.30% | train_error: -0.0000000010 | train_acc: 1.00
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==============================&gt;---------] 79.40% | train_error: -0.0000000010 | train_acc: 1.00
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==============================&gt;---------] 79.50% | train_error: -0.0000000010 | train_acc: 1.00
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==============================&gt;---------] 79.60% | train_error: -0.0000000010 | train_acc: 1.00
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==============================&gt;---------] 79.70% | train_error: -0.0000000010 | train_acc: 1.00
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==============================&gt;---------] 79.80% | train_error: -0.0000000010 | train_acc: 1.00
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==============================&gt;---------] 79.90% | train_error: -0.0000000010 | train_acc: 1.00
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===============================&gt;--------] 80.00% | train_error: -0.0000000010 | train_acc: 1.00
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===============================&gt;--------] 80.10% | train_error: -0.0000000010 | train_acc: 1.00
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===============================&gt;--------] 80.20% | train_error: -0.0000000010 | train_acc: 1.00
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===============================&gt;--------] 80.30% | train_error: -0.0000000010 | train_acc: 1.00
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===============================&gt;--------] 80.40% | train_error: -0.0000000010 | train_acc: 1.00
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===============================&gt;--------] 80.50% | train_error: -0.0000000010 | train_acc: 1.00
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===============================&gt;--------] 80.60% | train_error: -0.0000000010 | train_acc: 1.00
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===============================&gt;--------] 80.70% | train_error: -0.0000000010 | train_acc: 1.00
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===============================&gt;--------] 80.80% | train_error: -0.0000000010 | train_acc: 1.00
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===============================&gt;--------] 80.90% | train_error: -0.0000000010 | train_acc: 1.00
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===============================&gt;--------] 81.00% | train_error: -0.0000000010 | train_acc: 1.00
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===============================&gt;--------] 81.10% | train_error: -0.0000000010 | train_acc: 1.00
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===============================&gt;--------] 81.20% | train_error: -0.0000000010 | train_acc: 1.00
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===============================&gt;--------] 81.30% | train_error: -0.0000000010 | train_acc: 1.00
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===============================&gt;--------] 81.40% | train_error: -0.0000000010 | train_acc: 1.00
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===============================&gt;--------] 81.50% | train_error: -0.0000000010 | train_acc: 1.00
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===============================&gt;--------] 81.60% | train_error: -0.0000000010 | train_acc: 1.00
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===============================&gt;--------] 81.70% | train_error: -0.0000000010 | train_acc: 1.00
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===============================&gt;--------] 81.80% | train_error: -0.0000000010 | train_acc: 1.00
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===============================&gt;--------] 81.90% | train_error: -0.0000000010 | train_acc: 1.00
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===============================&gt;--------] 82.00% | train_error: -0.0000000010 | train_acc: 1.00
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===============================&gt;--------] 82.10% | train_error: -0.0000000010 | train_acc: 1.00
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===============================&gt;--------] 82.20% | train_error: -0.0000000010 | train_acc: 1.00
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===============================&gt;--------] 82.30% | train_error: -0.0000000010 | train_acc: 1.00
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===============================&gt;--------] 82.40% | train_error: -0.0000000010 | train_acc: 1.00
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [================================&gt;-------] 82.50% | train_error: -0.0000000010 | train_acc: 1.00
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [================================&gt;-------] 82.60% | train_error: -0.0000000010 | train_acc: 1.00
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [================================&gt;-------] 82.70% | train_error: -0.0000000010 | train_acc: 1.00
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [================================&gt;-------] 82.80% | train_error: -0.0000000010 | train_acc: 1.00
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [================================&gt;-------] 82.90% | train_error: -0.0000000010 | train_acc: 1.00
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [================================&gt;-------] 83.00% | train_error: -0.0000000010 | train_acc: 1.00
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [================================&gt;-------] 83.10% | train_error: -0.0000000010 | train_acc: 1.00
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [================================&gt;-------] 83.20% | train_error: -0.0000000010 | train_acc: 1.00
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [================================&gt;-------] 83.30% | train_error: -0.0000000010 | train_acc: 1.00
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [================================&gt;-------] 83.40% | train_error: -0.0000000010 | train_acc: 1.00
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [================================&gt;-------] 83.50% | train_error: -0.0000000010 | train_acc: 1.00
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [================================&gt;-------] 83.60% | train_error: -0.0000000010 | train_acc: 1.00
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [================================&gt;-------] 83.70% | train_error: -0.0000000010 | train_acc: 1.00
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [================================&gt;-------] 83.80% | train_error: -0.0000000010 | train_acc: 1.00
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [================================&gt;-------] 83.90% | train_error: -0.0000000010 | train_acc: 1.00
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [================================&gt;-------] 84.00% | train_error: -0.0000000010 | train_acc: 1.00
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [================================&gt;-------] 84.10% | train_error: -0.0000000010 | train_acc: 1.00
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [================================&gt;-------] 84.20% | train_error: -0.0000000010 | train_acc: 1.00
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [================================&gt;-------] 84.30% | train_error: -0.0000000010 | train_acc: 1.00
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [================================&gt;-------] 84.40% | train_error: -0.0000000010 | train_acc: 1.00
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [================================&gt;-------] 84.50% | train_error: -0.0000000010 | train_acc: 1.00
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [================================&gt;-------] 84.60% | train_error: -0.0000000010 | train_acc: 1.00
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [================================&gt;-------] 84.70% | train_error: -0.0000000010 | train_acc: 1.00
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [================================&gt;-------] 84.80% | train_error: -0.0000000010 | train_acc: 1.00
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [================================&gt;-------] 84.90% | train_error: -0.0000000010 | train_acc: 1.00
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=================================&gt;------] 85.00% | train_error: -0.0000000010 | train_acc: 1.00
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=================================&gt;------] 85.10% | train_error: -0.0000000010 | train_acc: 1.00
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=================================&gt;------] 85.20% | train_error: -0.0000000010 | train_acc: 1.00
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=================================&gt;------] 85.30% | train_error: -0.0000000010 | train_acc: 1.00
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=================================&gt;------] 85.40% | train_error: -0.0000000010 | train_acc: 1.00
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=================================&gt;------] 85.50% | train_error: -0.0000000010 | train_acc: 1.00
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=================================&gt;------] 85.60% | train_error: -0.0000000010 | train_acc: 1.00
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=================================&gt;------] 85.70% | train_error: -0.0000000010 | train_acc: 1.00
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=================================&gt;------] 85.80% | train_error: -0.0000000010 | train_acc: 1.00
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=================================&gt;------] 85.90% | train_error: -0.0000000010 | train_acc: 1.00
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=================================&gt;------] 86.00% | train_error: -0.0000000010 | train_acc: 1.00
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=================================&gt;------] 86.10% | train_error: -0.0000000010 | train_acc: 1.00
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=================================&gt;------] 86.20% | train_error: -0.0000000010 | train_acc: 1.00
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=================================&gt;------] 86.30% | train_error: -0.0000000010 | train_acc: 1.00
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=================================&gt;------] 86.40% | train_error: -0.0000000010 | train_acc: 1.00
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=================================&gt;------] 86.50% | train_error: -0.0000000010 | train_acc: 1.00
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=================================&gt;------] 86.60% | train_error: -0.0000000010 | train_acc: 1.00
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=================================&gt;------] 86.70% | train_error: -0.0000000010 | train_acc: 1.00
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=================================&gt;------] 86.80% | train_error: -0.0000000010 | train_acc: 1.00
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=================================&gt;------] 86.90% | train_error: -0.0000000010 | train_acc: 1.00
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=================================&gt;------] 87.00% | train_error: -0.0000000010 | train_acc: 1.00
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=================================&gt;------] 87.10% | train_error: -0.0000000010 | train_acc: 1.00
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=================================&gt;------] 87.20% | train_error: -0.0000000010 | train_acc: 1.00
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=================================&gt;------] 87.30% | train_error: -0.0000000010 | train_acc: 1.00
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=================================&gt;------] 87.40% | train_error: -0.0000000010 | train_acc: 1.00
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==================================&gt;-----] 87.50% | train_error: -0.0000000010 | train_acc: 1.00
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==================================&gt;-----] 87.60% | train_error: -0.0000000010 | train_acc: 1.00
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==================================&gt;-----] 87.70% | train_error: -0.0000000010 | train_acc: 1.00
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==================================&gt;-----] 87.80% | train_error: -0.0000000010 | train_acc: 1.00
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==================================&gt;-----] 87.90% | train_error: -0.0000000010 | train_acc: 1.00
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==================================&gt;-----] 88.00% | train_error: -0.0000000010 | train_acc: 1.00
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==================================&gt;-----] 88.10% | train_error: -0.0000000010 | train_acc: 1.00
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==================================&gt;-----] 88.20% | train_error: -0.0000000010 | train_acc: 1.00
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==================================&gt;-----] 88.30% | train_error: -0.0000000010 | train_acc: 1.00
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==================================&gt;-----] 88.40% | train_error: -0.0000000010 | train_acc: 1.00
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==================================&gt;-----] 88.50% | train_error: -0.0000000010 | train_acc: 1.00
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==================================&gt;-----] 88.60% | train_error: -0.0000000010 | train_acc: 1.00
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==================================&gt;-----] 88.70% | train_error: -0.0000000010 | train_acc: 1.00
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==================================&gt;-----] 88.80% | train_error: -0.0000000010 | train_acc: 1.00
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==================================&gt;-----] 88.90% | train_error: -0.0000000010 | train_acc: 1.00
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==================================&gt;-----] 89.00% | train_error: -0.0000000010 | train_acc: 1.00
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==================================&gt;-----] 89.10% | train_error: -0.0000000010 | train_acc: 1.00
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==================================&gt;-----] 89.20% | train_error: -0.0000000010 | train_acc: 1.00
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==================================&gt;-----] 89.30% | train_error: -0.0000000010 | train_acc: 1.00
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==================================&gt;-----] 89.40% | train_error: -0.0000000010 | train_acc: 1.00
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==================================&gt;-----] 89.50% | train_error: -0.0000000010 | train_acc: 1.00
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==================================&gt;-----] 89.60% | train_error: -0.0000000010 | train_acc: 1.00
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==================================&gt;-----] 89.70% | train_error: -0.0000000010 | train_acc: 1.00
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==================================&gt;-----] 89.80% | train_error: -0.0000000010 | train_acc: 1.00
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==================================&gt;-----] 89.90% | train_error: -0.0000000010 | train_acc: 1.00
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===================================&gt;----] 90.00% | train_error: -0.0000000010 | train_acc: 1.00
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===================================&gt;----] 90.10% | train_error: -0.0000000010 | train_acc: 1.00
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===================================&gt;----] 90.20% | train_error: -0.0000000010 | train_acc: 1.00
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===================================&gt;----] 90.30% | train_error: -0.0000000010 | train_acc: 1.00
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===================================&gt;----] 90.40% | train_error: -0.0000000010 | train_acc: 1.00
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===================================&gt;----] 90.50% | train_error: -0.0000000010 | train_acc: 1.00
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===================================&gt;----] 90.60% | train_error: -0.0000000010 | train_acc: 1.00
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===================================&gt;----] 90.70% | train_error: -0.0000000010 | train_acc: 1.00
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===================================&gt;----] 90.80% | train_error: -0.0000000010 | train_acc: 1.00
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===================================&gt;----] 90.90% | train_error: -0.0000000010 | train_acc: 1.00
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===================================&gt;----] 91.00% | train_error: -0.0000000010 | train_acc: 1.00
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===================================&gt;----] 91.10% | train_error: -0.0000000010 | train_acc: 1.00
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===================================&gt;----] 91.20% | train_error: -0.0000000010 | train_acc: 1.00
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===================================&gt;----] 91.30% | train_error: -0.0000000010 | train_acc: 1.00
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===================================&gt;----] 91.40% | train_error: -0.0000000010 | train_acc: 1.00
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===================================&gt;----] 91.50% | train_error: -0.0000000010 | train_acc: 1.00
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===================================&gt;----] 91.60% | train_error: -0.0000000010 | train_acc: 1.00
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===================================&gt;----] 91.70% | train_error: -0.0000000010 | train_acc: 1.00
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===================================&gt;----] 91.80% | train_error: -0.0000000010 | train_acc: 1.00
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===================================&gt;----] 91.90% | train_error: -0.0000000010 | train_acc: 1.00
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===================================&gt;----] 92.00% | train_error: -0.0000000010 | train_acc: 1.00
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===================================&gt;----] 92.10% | train_error: -0.0000000010 | train_acc: 1.00
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===================================&gt;----] 92.20% | train_error: -0.0000000010 | train_acc: 1.00
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===================================&gt;----] 92.30% | train_error: -0.0000000010 | train_acc: 1.00
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===================================&gt;----] 92.40% | train_error: -0.0000000010 | train_acc: 1.00
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [====================================&gt;---] 92.50% | train_error: -0.0000000010 | train_acc: 1.00
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [====================================&gt;---] 92.60% | train_error: -0.0000000010 | train_acc: 1.00
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [====================================&gt;---] 92.70% | train_error: -0.0000000010 | train_acc: 1.00
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [====================================&gt;---] 92.80% | train_error: -0.0000000010 | train_acc: 1.00
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [====================================&gt;---] 92.90% | train_error: -0.0000000010 | train_acc: 1.00
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [====================================&gt;---] 93.00% | train_error: -0.0000000010 | train_acc: 1.00
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [====================================&gt;---] 93.10% | train_error: -0.0000000010 | train_acc: 1.00
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [====================================&gt;---] 93.20% | train_error: -0.0000000010 | train_acc: 1.00
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [====================================&gt;---] 93.30% | train_error: -0.0000000010 | train_acc: 1.00
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [====================================&gt;---] 93.40% | train_error: -0.0000000010 | train_acc: 1.00
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [====================================&gt;---] 93.50% | train_error: -0.0000000010 | train_acc: 1.00
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [====================================&gt;---] 93.60% | train_error: -0.0000000010 | train_acc: 1.00
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [====================================&gt;---] 93.70% | train_error: -0.0000000010 | train_acc: 1.00
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [====================================&gt;---] 93.80% | train_error: -0.0000000010 | train_acc: 1.00
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [====================================&gt;---] 93.90% | train_error: -0.0000000010 | train_acc: 1.00
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [====================================&gt;---] 94.00% | train_error: -0.0000000010 | train_acc: 1.00
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [====================================&gt;---] 94.10% | train_error: -0.0000000010 | train_acc: 1.00
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [====================================&gt;---] 94.20% | train_error: -0.0000000010 | train_acc: 1.00
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [====================================&gt;---] 94.30% | train_error: -0.0000000010 | train_acc: 1.00
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [====================================&gt;---] 94.40% | train_error: -0.0000000010 | train_acc: 1.00
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [====================================&gt;---] 94.50% | train_error: -0.0000000010 | train_acc: 1.00
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [====================================&gt;---] 94.60% | train_error: -0.0000000010 | train_acc: 1.00
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [====================================&gt;---] 94.70% | train_error: -0.0000000010 | train_acc: 1.00
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [====================================&gt;---] 94.80% | train_error: -0.0000000010 | train_acc: 1.00
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [====================================&gt;---] 94.90% | train_error: -0.0000000010 | train_acc: 1.00
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=====================================&gt;--] 95.00% | train_error: -0.0000000010 | train_acc: 1.00
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=====================================&gt;--] 95.10% | train_error: -0.0000000010 | train_acc: 1.00
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=====================================&gt;--] 95.20% | train_error: -0.0000000010 | train_acc: 1.00
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=====================================&gt;--] 95.30% | train_error: -0.0000000010 | train_acc: 1.00
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=====================================&gt;--] 95.40% | train_error: -0.0000000010 | train_acc: 1.00
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=====================================&gt;--] 95.50% | train_error: -0.0000000010 | train_acc: 1.00
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=====================================&gt;--] 95.60% | train_error: -0.0000000010 | train_acc: 1.00
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=====================================&gt;--] 95.70% | train_error: -0.0000000010 | train_acc: 1.00
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=====================================&gt;--] 95.80% | train_error: -0.0000000010 | train_acc: 1.00
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=====================================&gt;--] 95.90% | train_error: -0.0000000010 | train_acc: 1.00
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=====================================&gt;--] 96.00% | train_error: -0.0000000010 | train_acc: 1.00
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=====================================&gt;--] 96.10% | train_error: -0.0000000010 | train_acc: 1.00
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=====================================&gt;--] 96.20% | train_error: -0.0000000010 | train_acc: 1.00
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=====================================&gt;--] 96.30% | train_error: -0.0000000010 | train_acc: 1.00
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=====================================&gt;--] 96.40% | train_error: -0.0000000010 | train_acc: 1.00
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=====================================&gt;--] 96.50% | train_error: -0.0000000010 | train_acc: 1.00
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=====================================&gt;--] 96.60% | train_error: -0.0000000010 | train_acc: 1.00
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=====================================&gt;--] 96.70% | train_error: -0.0000000010 | train_acc: 1.00
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=====================================&gt;--] 96.80% | train_error: -0.0000000010 | train_acc: 1.00
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=====================================&gt;--] 96.90% | train_error: -0.0000000010 | train_acc: 1.00
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=====================================&gt;--] 97.00% | train_error: -0.0000000010 | train_acc: 1.00
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=====================================&gt;--] 97.10% | train_error: -0.0000000010 | train_acc: 1.00
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=====================================&gt;--] 97.20% | train_error: -0.0000000010 | train_acc: 1.00
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=====================================&gt;--] 97.30% | train_error: -0.0000000010 | train_acc: 1.00
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=====================================&gt;--] 97.40% | train_error: -0.0000000010 | train_acc: 1.00
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [======================================&gt;-] 97.50% | train_error: -0.0000000010 | train_acc: 1.00
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [======================================&gt;-] 97.60% | train_error: -0.0000000010 | train_acc: 1.00
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [======================================&gt;-] 97.70% | train_error: -0.0000000010 | train_acc: 1.00
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [======================================&gt;-] 97.80% | train_error: -0.0000000010 | train_acc: 1.00
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [======================================&gt;-] 97.90% | train_error: -0.0000000010 | train_acc: 1.00
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [======================================&gt;-] 98.00% | train_error: -0.0000000010 | train_acc: 1.00
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [======================================&gt;-] 98.10% | train_error: -0.0000000010 | train_acc: 1.00
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [======================================&gt;-] 98.20% | train_error: -0.0000000010 | train_acc: 1.00
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [======================================&gt;-] 98.30% | train_error: -0.0000000010 | train_acc: 1.00
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [======================================&gt;-] 98.40% | train_error: -0.0000000010 | train_acc: 1.00
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [======================================&gt;-] 98.50% | train_error: -0.0000000010 | train_acc: 1.00
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [======================================&gt;-] 98.60% | train_error: -0.0000000010 | train_acc: 1.00
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [======================================&gt;-] 98.70% | train_error: -0.0000000010 | train_acc: 1.00
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [======================================&gt;-] 98.80% | train_error: -0.0000000010 | train_acc: 1.00
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [======================================&gt;-] 98.90% | train_error: -0.0000000010 | train_acc: 1.00
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [======================================&gt;-] 99.00% | train_error: -0.0000000010 | train_acc: 1.00
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [======================================&gt;-] 99.10% | train_error: -0.0000000010 | train_acc: 1.00
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [======================================&gt;-] 99.20% | train_error: -0.0000000010 | train_acc: 1.00
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [======================================&gt;-] 99.30% | train_error: -0.0000000010 | train_acc: 1.00
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [======================================&gt;-] 99.40% | train_error: -0.0000000010 | train_acc: 1.00
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [======================================&gt;-] 99.50% | train_error: -0.0000000010 | train_acc: 1.00
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [======================================&gt;-] 99.60% | train_error: -0.0000000010 | train_acc: 1.00
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [======================================&gt;-] 99.70% | train_error: -0.0000000010 | train_acc: 1.00
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [======================================&gt;-] 99.80% | train_error: -0.0000000010 | train_acc: 1.00
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [======================================&gt;-] 99.90% | train_error: -0.0000000010 | train_acc: 1.00
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=======================================&gt;] 100.0% | train_error: -0.0000000010 | train_acc: 1.00
</pre></div>
</div>
</div>
</div>
<p>Not bad, but the results depend strongly on the learning reate. Try different learning rates.</p>
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