Morten Hjorth-Jensen
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Applied Data Analysis and Machine Learning
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About the course
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Teachers and Grading
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Review of Statistics with Resampling Techniques and Linear Algebra
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1. Elements of Probability Theory and Statistical Data Analysis
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<a class="reference internal" href="linalg.html">
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2. Linear Algebra, Handling of Arrays and more Python Features
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<p aria-level="2" class="caption" role="heading">
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From Regression to Support Vector Machines
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3. Linear Regression
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4. Ridge and Lasso Regression
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5. Resampling Methods
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6. Logistic Regression
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<li class="toctree-l1">
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<a class="reference internal" href="chapteroptimization.html">
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7. Optimization, the central part of any Machine Learning algortithm
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<a class="reference internal" href="chapter5.html">
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8. Support Vector Machines, overarching aims
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<p aria-level="2" class="caption" role="heading">
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Decision Trees, Ensemble Methods and Boosting
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9. Decision trees, overarching aims
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<a class="reference internal" href="chapter7.html">
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10. Ensemble Methods: From a Single Tree to Many Trees and Extreme Boosting, Meet the Jungle of Methods
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</a>
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Dimensionality Reduction
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11. Basic ideas of the Principal Component Analysis (PCA)
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12. Clustering and Unsupervised Learning
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Deep Learning Methods
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13. Neural networks
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14. Building a Feed Forward Neural Network
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15. Solving Differential Equations with Deep Learning
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16. Convolutional Neural Networks
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17. Recurrent neural networks: Overarching view
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Weekly material, notes and exercises
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Exercises week 34
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Week 34: Introduction to the course, Logistics and Practicalities
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Exercises week 35
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Week 35: From Ordinary Linear Regression to Ridge and Lasso Regression
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Exercises week 36
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Week 36: Statistical interpretation of Linear Regression and Resampling techniques
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Exercises week 37
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Week 37: Statistical interpretations and Resampling Methods
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Exercises week 38
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Week 38: Logistic Regression and Optimization
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Exercises week 39
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Week 39: Optimization and Gradient Methods
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Week 40: Gradient descent methods (continued) and start Neural networks
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Exercises week 41
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Week 41 Neural networks and constructing a neural network code
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Exercises week 42
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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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Project 2 on Machine Learning, deadline November 13 (Midnight)
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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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The AND and XOR Gates
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Representing the Data Sets
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Setting up the Neural Network
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||
The Code using Scikit-Learn
|
||
</a>
|
||
</li>
|
||
<li class="toc-h2 nav-item toc-entry">
|
||
<a class="reference internal nav-link" href="#building-a-neural-network-code">
|
||
Building a neural network code
|
||
</a>
|
||
<ul class="nav section-nav flex-column">
|
||
<li class="toc-h3 nav-item toc-entry">
|
||
<a class="reference internal nav-link" href="#learning-rate-methods">
|
||
Learning rate methods
|
||
</a>
|
||
</li>
|
||
<li class="toc-h3 nav-item toc-entry">
|
||
<a class="reference internal nav-link" href="#usage-of-the-above-learning-rate-schedulers">
|
||
Usage of the above learning rate schedulers
|
||
</a>
|
||
</li>
|
||
<li class="toc-h3 nav-item toc-entry">
|
||
<a class="reference internal nav-link" href="#cost-functions">
|
||
Cost functions
|
||
</a>
|
||
</li>
|
||
<li class="toc-h3 nav-item toc-entry">
|
||
<a class="reference internal nav-link" href="#activation-functions">
|
||
Activation functions
|
||
</a>
|
||
</li>
|
||
<li class="toc-h3 nav-item toc-entry">
|
||
<a class="reference internal nav-link" href="#the-neural-network">
|
||
The Neural Network
|
||
</a>
|
||
</li>
|
||
<li class="toc-h3 nav-item toc-entry">
|
||
<a class="reference internal nav-link" href="#multiclass-classification">
|
||
Multiclass classification
|
||
</a>
|
||
</li>
|
||
</ul>
|
||
</li>
|
||
</ul>
|
||
</li>
|
||
</ul>
|
||
|
||
</nav>
|
||
</div>
|
||
</div>
|
||
</div>
|
||
<div id="main-content" class="row">
|
||
<div class="col-12 col-md-9 pl-md-3 pr-md-0">
|
||
<!-- Table of contents that is only displayed when printing the page -->
|
||
<div id="jb-print-docs-body" class="onlyprint">
|
||
<h1>Exercises weeks 43 and 44</h1>
|
||
<!-- Table of contents -->
|
||
<div id="print-main-content">
|
||
<div id="jb-print-toc">
|
||
|
||
<div>
|
||
<h2> Contents </h2>
|
||
</div>
|
||
<nav aria-label="Page">
|
||
<ul class="visible nav section-nav flex-column">
|
||
<li class="toc-h1 nav-item toc-entry">
|
||
<a class="reference internal nav-link" href="#">
|
||
Exercises weeks 43 and 44
|
||
</a>
|
||
</li>
|
||
<li class="toc-h1 nav-item toc-entry">
|
||
<a class="reference internal nav-link" href="#overarching-aims-of-the-exercises-weeks-43-and-44">
|
||
Overarching aims of the exercises weeks 43 and 44
|
||
</a>
|
||
<ul class="visible nav section-nav flex-column">
|
||
<li class="toc-h2 nav-item toc-entry">
|
||
<a class="reference internal nav-link" href="#the-and-and-xor-gates">
|
||
The AND and XOR Gates
|
||
</a>
|
||
</li>
|
||
<li class="toc-h2 nav-item toc-entry">
|
||
<a class="reference internal nav-link" href="#representing-the-data-sets">
|
||
Representing the Data Sets
|
||
</a>
|
||
</li>
|
||
<li class="toc-h2 nav-item toc-entry">
|
||
<a class="reference internal nav-link" href="#setting-up-the-neural-network">
|
||
Setting up the Neural Network
|
||
</a>
|
||
</li>
|
||
<li class="toc-h2 nav-item toc-entry">
|
||
<a class="reference internal nav-link" href="#the-code-using-scikit-learn">
|
||
The Code using Scikit-Learn
|
||
</a>
|
||
</li>
|
||
<li class="toc-h2 nav-item toc-entry">
|
||
<a class="reference internal nav-link" href="#building-a-neural-network-code">
|
||
Building a neural network code
|
||
</a>
|
||
<ul class="nav section-nav flex-column">
|
||
<li class="toc-h3 nav-item toc-entry">
|
||
<a class="reference internal nav-link" href="#learning-rate-methods">
|
||
Learning rate methods
|
||
</a>
|
||
</li>
|
||
<li class="toc-h3 nav-item toc-entry">
|
||
<a class="reference internal nav-link" href="#usage-of-the-above-learning-rate-schedulers">
|
||
Usage of the above learning rate schedulers
|
||
</a>
|
||
</li>
|
||
<li class="toc-h3 nav-item toc-entry">
|
||
<a class="reference internal nav-link" href="#cost-functions">
|
||
Cost functions
|
||
</a>
|
||
</li>
|
||
<li class="toc-h3 nav-item toc-entry">
|
||
<a class="reference internal nav-link" href="#activation-functions">
|
||
Activation functions
|
||
</a>
|
||
</li>
|
||
<li class="toc-h3 nav-item toc-entry">
|
||
<a class="reference internal nav-link" href="#the-neural-network">
|
||
The Neural Network
|
||
</a>
|
||
</li>
|
||
<li class="toc-h3 nav-item toc-entry">
|
||
<a class="reference internal nav-link" href="#multiclass-classification">
|
||
Multiclass classification
|
||
</a>
|
||
</li>
|
||
</ul>
|
||
</li>
|
||
</ul>
|
||
</li>
|
||
</ul>
|
||
|
||
</nav>
|
||
</div>
|
||
</div>
|
||
</div>
|
||
|
||
<div>
|
||
|
||
<!-- HTML file automatically generated from DocOnce source (https://github.com/doconce/doconce/)
|
||
doconce format html exercisesweek43.do.txt -->
|
||
<!-- 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">
|
||
\[\begin{split}
|
||
\boldsymbol{X}=\begin{bmatrix} 0 & 0 \\
|
||
0 & 1 \\
|
||
1 & 0 \\
|
||
1 & 1 \end{bmatrix},
|
||
\end{split}\]</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-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">"""</span>
|
||
<span class="sd">Simple code that tests XOR, OR and AND gates with linear regression</span>
|
||
<span class="sd">"""</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"># we obtain a prediction by taking the class with the highest likelihood</span>
|
||
<span class="k">def</span> <span class="nf">predict</span><span class="p">(</span><span class="n">X</span><span class="p">):</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="k">return</span> <span class="n">np</span><span class="o">.</span><span class="n">argmax</span><span class="p">(</span><span class="n">probabilities</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="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">2</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>
|
||
|
||
|
||
<span class="n">predictions</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="nb">print</span><span class="p">(</span><span class="n">predictions</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>[[0.80625657 0.36420967]
|
||
[0.90297441 0.30170017]
|
||
[0.89823921 0.28566769]
|
||
[0.93420126 0.25920793]]
|
||
[0 0 0 0]
|
||
</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_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">2</span>
|
||
<span class="n">n_features</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">'logistic'</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">"Learning rate = "</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">"Lambda = "</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">"Accuracy score on data set: "</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">"viridis"</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">"Test Accuracy"</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">"$\eta$"</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">"$\lambda$"</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
|
||
|
||
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
|
||
|
||
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'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'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'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'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'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'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'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'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'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't converged yet.
|
||
warnings.warn(
|
||
</pre></div>
|
||
</div>
|
||
<img alt="_images/exercisesweek43_11_2.png" src="_images/exercisesweek43_11_2.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>
|
||
<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">"""</span>
|
||
<span class="sd"> Abstract class for Schedulers</span>
|
||
<span class="sd"> """</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">"Before scheduler:</span><span class="se">\n</span><span class="si">{</span><span class="n">weights</span><span class="si">=}</span><span class="s2">"</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">"</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">"</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">"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">"</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">></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">></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">></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">"RELU"</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">></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">"LRELU"</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">></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">"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">"</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">"</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">"</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">"</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">"</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">"error"</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">"""</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"> """</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">"""</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"> """</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">"</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">"</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">"</span><span class="se">\r</span><span class="s2">"</span> <span class="o">+</span> <span class="s2">" "</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">""</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">"train_errors"</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">"val_errors"</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">"train_accs"</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">"val_accs"</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">"""</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"> """</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">></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">"""</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"> """</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">"""</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"> """</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"><</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">"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"</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">"""</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"> """</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">"softmax"</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">"""</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"> """</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">"""</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"> """</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">"CostLogReg"</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">"CostCrossEntropy"</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">"""</span>
|
||
<span class="sd"> Description:</span>
|
||
<span class="sd"> ------------</span>
|
||
<span class="sd"> Displays progress of training</span>
|
||
<span class="sd"> """</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">">"</span> <span class="k">if</span> <span class="n">num_equals</span> <span class="o">></span> <span class="mi">0</span> <span class="k">else</span> <span class="s2">""</span>
|
||
<span class="n">bar</span> <span class="o">=</span> <span class="s2">"["</span> <span class="o">+</span> <span class="s2">"="</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">"-"</span> <span class="o">*</span> <span class="n">num_not</span> <span class="o">+</span> <span class="s2">"]"</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">" </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">% "</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">"| </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"> "</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">"</span><span class="se">\r</span><span class="s2">"</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">"""</span>
|
||
<span class="sd"> Description:</span>
|
||
<span class="sd"> ------------</span>
|
||
<span class="sd"> Formats decimal numbers for progress bar</span>
|
||
<span class="sd"> """</span>
|
||
<span class="k">if</span> <span class="n">value</span> <span class="o">></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"><</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">>=</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">"</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">"</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">></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'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> [>---------------------------------------] 3.000% | train_error: 3.64
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [>---------------------------------------] 4.000% | train_error: 3.62
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=>--------------------------------------] 5.000% | train_error: 3.60
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=>--------------------------------------] 6.000% | train_error: 3.58
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=>--------------------------------------] 7.000% | train_error: 3.57
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==>-------------------------------------] 8.000% | train_error: 3.55
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==>-------------------------------------] 9.000% | train_error: 3.53
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===>------------------------------------] 10.00% | train_error: 3.52
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===>------------------------------------] 11.00% | train_error: 3.50
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===>------------------------------------] 12.00% | train_error: 3.48
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [====>-----------------------------------] 13.00% | train_error: 3.47
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [====>-----------------------------------] 14.00% | train_error: 3.45
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=====>----------------------------------] 15.00% | train_error: 3.43
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=====>----------------------------------] 16.00% | train_error: 3.42
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=====>----------------------------------] 17.00% | train_error: 3.40
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [======>---------------------------------] 18.00% | train_error: 3.38
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [======>---------------------------------] 19.00% | train_error: 3.37
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=======>--------------------------------] 20.00% | train_error: 3.35
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=======>--------------------------------] 21.00% | train_error: 3.34
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=======>--------------------------------] 22.00% | train_error: 3.32
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [========>-------------------------------] 23.00% | train_error: 3.31
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [========>-------------------------------] 24.00% | train_error: 3.29
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=========>------------------------------] 25.00% | train_error: 3.27
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=========>------------------------------] 26.00% | train_error: 3.26
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=========>------------------------------] 27.00% | train_error: 3.24
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==========>-----------------------------] 28.00% | train_error: 3.23
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==========>-----------------------------] 29.00% | train_error: 3.21
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===========>----------------------------] 30.00% | train_error: 3.20
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===========>----------------------------] 31.00% | train_error: 3.18
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===========>----------------------------] 32.00% | train_error: 3.17
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [============>---------------------------] 33.00% | train_error: 3.15
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [============>---------------------------] 34.00% | train_error: 3.14
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=============>--------------------------] 35.00% | train_error: 3.12
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=============>--------------------------] 36.00% | train_error: 3.11
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=============>--------------------------] 37.00% | train_error: 3.09
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==============>-------------------------] 38.00% | train_error: 3.08
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==============>-------------------------] 39.00% | train_error: 3.06
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===============>------------------------] 40.00% | train_error: 3.05
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===============>------------------------] 41.00% | train_error: 3.03
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===============>------------------------] 42.00% | train_error: 3.02
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [================>-----------------------] 43.00% | train_error: 3.00
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [================>-----------------------] 44.00% | train_error: 2.99
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=================>----------------------] 45.00% | train_error: 2.98
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=================>----------------------] 46.00% | train_error: 2.96
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=================>----------------------] 47.00% | train_error: 2.95
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==================>---------------------] 48.00% | train_error: 2.93
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==================>---------------------] 49.00% | train_error: 2.92
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===================>--------------------] 50.00% | train_error: 2.91
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===================>--------------------] 51.00% | train_error: 2.89
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===================>--------------------] 52.00% | train_error: 2.88
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [====================>-------------------] 53.00% | train_error: 2.86
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [====================>-------------------] 54.00% | train_error: 2.85
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=====================>------------------] 55.00% | train_error: 2.84
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=====================>------------------] 56.00% | train_error: 2.82
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=====================>------------------] 57.00% | train_error: 2.81
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [======================>-----------------] 58.00% | train_error: 2.80
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [======================>-----------------] 59.00% | train_error: 2.78
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=======================>----------------] 60.00% | train_error: 2.77
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=======================>----------------] 61.00% | train_error: 2.76
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=======================>----------------] 62.00% | train_error: 2.74
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [========================>---------------] 63.00% | train_error: 2.73
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [========================>---------------] 64.00% | train_error: 2.72
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=========================>--------------] 65.00% | train_error: 2.70
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=========================>--------------] 66.00% | train_error: 2.69
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=========================>--------------] 67.00% | train_error: 2.68
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==========================>-------------] 68.00% | train_error: 2.67
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==========================>-------------] 69.00% | train_error: 2.65
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===========================>------------] 70.00% | train_error: 2.64
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===========================>------------] 71.00% | train_error: 2.63
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===========================>------------] 72.00% | train_error: 2.62
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [============================>-----------] 73.00% | train_error: 2.60
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [============================>-----------] 74.00% | train_error: 2.59
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=============================>----------] 75.00% | train_error: 2.58
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=============================>----------] 76.00% | train_error: 2.57
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=============================>----------] 77.00% | train_error: 2.55
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==============================>---------] 78.00% | train_error: 2.54
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==============================>---------] 79.00% | train_error: 2.53
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===============================>--------] 80.00% | train_error: 2.52
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===============================>--------] 81.00% | train_error: 2.51
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===============================>--------] 82.00% | train_error: 2.49
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [================================>-------] 83.00% | train_error: 2.48
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [================================>-------] 84.00% | train_error: 2.47
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=================================>------] 85.00% | train_error: 2.46
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=================================>------] 86.00% | train_error: 2.45
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=================================>------] 87.00% | train_error: 2.44
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==================================>-----] 88.00% | train_error: 2.42
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==================================>-----] 89.00% | train_error: 2.41
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===================================>----] 90.00% | train_error: 2.40
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===================================>----] 91.00% | train_error: 2.39
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===================================>----] 92.00% | train_error: 2.38
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [====================================>---] 93.00% | train_error: 2.37
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [====================================>---] 94.00% | train_error: 2.36
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=====================================>--] 95.00% | train_error: 2.34
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=====================================>--] 96.00% | train_error: 2.33
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=====================================>--] 97.00% | train_error: 2.32
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [======================================>-] 98.00% | train_error: 2.31
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [======================================>-] 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> [=======================================>] 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'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>
|
||
</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=0.0001
|
||
|
||
[----------------------------------------] 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> [>---------------------------------------] 2.500% | train_error: 3.27
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [>---------------------------------------] 2.600% | train_error: 3.26
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [>---------------------------------------] 2.700% | train_error: 3.24
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [>---------------------------------------] 2.800% | train_error: 3.23
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [>---------------------------------------] 2.900% | train_error: 3.21
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [>---------------------------------------] 3.000% | train_error: 3.20
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [>---------------------------------------] 3.100% | train_error: 3.18
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [>---------------------------------------] 3.200% | train_error: 3.17
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [>---------------------------------------] 3.300% | train_error: 3.15
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [>---------------------------------------] 3.400% | train_error: 3.14
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [>---------------------------------------] 3.500% | train_error: 3.12
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [>---------------------------------------] 3.600% | train_error: 3.11
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [>---------------------------------------] 3.700% | train_error: 3.09
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [>---------------------------------------] 3.800% | train_error: 3.08
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [>---------------------------------------] 3.900% | train_error: 3.06
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [>---------------------------------------] 4.000% | train_error: 3.05
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [>---------------------------------------] 4.100% | train_error: 3.03
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [>---------------------------------------] 4.200% | train_error: 3.02
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [>---------------------------------------] 4.300% | train_error: 3.00
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [>---------------------------------------] 4.400% | train_error: 2.99
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [>---------------------------------------] 4.500% | train_error: 2.98
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [>---------------------------------------] 4.600% | train_error: 2.96
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [>---------------------------------------] 4.700% | train_error: 2.95
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [>---------------------------------------] 4.800% | train_error: 2.93
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [>---------------------------------------] 4.900% | train_error: 2.92
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=>--------------------------------------] 5.000% | train_error: 2.91
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=>--------------------------------------] 5.100% | train_error: 2.89
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=>--------------------------------------] 5.200% | train_error: 2.88
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=>--------------------------------------] 5.300% | train_error: 2.86
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=>--------------------------------------] 5.400% | train_error: 2.85
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=>--------------------------------------] 5.500% | train_error: 2.84
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=>--------------------------------------] 5.600% | train_error: 2.82
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=>--------------------------------------] 5.700% | train_error: 2.81
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=>--------------------------------------] 5.800% | train_error: 2.80
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=>--------------------------------------] 5.900% | train_error: 2.78
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=>--------------------------------------] 6.000% | train_error: 2.77
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=>--------------------------------------] 6.100% | train_error: 2.76
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=>--------------------------------------] 6.200% | train_error: 2.74
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=>--------------------------------------] 6.300% | train_error: 2.73
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=>--------------------------------------] 6.400% | train_error: 2.72
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=>--------------------------------------] 6.500% | train_error: 2.70
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=>--------------------------------------] 6.600% | train_error: 2.69
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=>--------------------------------------] 6.700% | train_error: 2.68
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=>--------------------------------------] 6.800% | train_error: 2.67
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=>--------------------------------------] 6.900% | train_error: 2.65
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=>--------------------------------------] 7.000% | train_error: 2.64
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=>--------------------------------------] 7.100% | train_error: 2.63
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=>--------------------------------------] 7.200% | train_error: 2.62
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=>--------------------------------------] 7.300% | train_error: 2.60
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=>--------------------------------------] 7.400% | train_error: 2.59
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==>-------------------------------------] 7.500% | train_error: 2.58
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==>-------------------------------------] 7.600% | train_error: 2.57
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==>-------------------------------------] 7.700% | train_error: 2.55
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==>-------------------------------------] 7.800% | train_error: 2.54
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==>-------------------------------------] 7.900% | train_error: 2.53
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==>-------------------------------------] 8.000% | train_error: 2.52
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==>-------------------------------------] 8.100% | train_error: 2.51
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==>-------------------------------------] 8.200% | train_error: 2.49
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==>-------------------------------------] 8.300% | train_error: 2.48
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==>-------------------------------------] 8.400% | train_error: 2.47
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==>-------------------------------------] 8.500% | train_error: 2.46
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==>-------------------------------------] 8.600% | train_error: 2.45
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==>-------------------------------------] 8.700% | train_error: 2.44
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==>-------------------------------------] 8.800% | train_error: 2.42
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==>-------------------------------------] 8.900% | train_error: 2.41
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==>-------------------------------------] 9.000% | train_error: 2.40
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==>-------------------------------------] 9.100% | train_error: 2.39
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==>-------------------------------------] 9.200% | train_error: 2.38
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==>-------------------------------------] 9.300% | train_error: 2.37
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==>-------------------------------------] 9.400% | train_error: 2.36
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==>-------------------------------------] 9.500% | train_error: 2.34
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==>-------------------------------------] 9.600% | train_error: 2.33
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==>-------------------------------------] 9.700% | train_error: 2.32
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==>-------------------------------------] 9.800% | train_error: 2.31
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==>-------------------------------------] 9.900% | train_error: 2.30
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===>------------------------------------] 10.00% | train_error: 2.29
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===>------------------------------------] 10.10% | train_error: 2.28
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===>------------------------------------] 10.20% | train_error: 2.27
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===>------------------------------------] 10.30% | train_error: 2.26
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===>------------------------------------] 10.40% | train_error: 2.25
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===>------------------------------------] 10.50% | train_error: 2.23
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===>------------------------------------] 10.60% | train_error: 2.22
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===>------------------------------------] 10.70% | train_error: 2.21
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===>------------------------------------] 10.80% | train_error: 2.20
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===>------------------------------------] 10.90% | train_error: 2.19
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===>------------------------------------] 11.00% | train_error: 2.18
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===>------------------------------------] 11.10% | train_error: 2.17
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===>------------------------------------] 11.20% | train_error: 2.16
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===>------------------------------------] 11.30% | train_error: 2.15
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===>------------------------------------] 11.40% | train_error: 2.14
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===>------------------------------------] 11.50% | train_error: 2.13
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===>------------------------------------] 11.60% | train_error: 2.12
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===>------------------------------------] 11.70% | train_error: 2.11
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===>------------------------------------] 11.80% | train_error: 2.10
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===>------------------------------------] 11.90% | train_error: 2.09
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===>------------------------------------] 12.00% | train_error: 2.08
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===>------------------------------------] 12.10% | train_error: 2.07
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===>------------------------------------] 12.20% | train_error: 2.06
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===>------------------------------------] 12.30% | train_error: 2.05
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===>------------------------------------] 12.40% | train_error: 2.04
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [====>-----------------------------------] 12.50% | train_error: 2.03
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [====>-----------------------------------] 12.60% | train_error: 2.02
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [====>-----------------------------------] 12.70% | train_error: 2.01
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [====>-----------------------------------] 12.80% | train_error: 2.00
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [====>-----------------------------------] 12.90% | train_error: 1.99
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [====>-----------------------------------] 13.00% | train_error: 1.98
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [====>-----------------------------------] 13.10% | train_error: 1.97
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [====>-----------------------------------] 13.20% | train_error: 1.96
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [====>-----------------------------------] 13.30% | train_error: 1.96
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [====>-----------------------------------] 13.40% | train_error: 1.95
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [====>-----------------------------------] 13.50% | train_error: 1.94
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [====>-----------------------------------] 13.60% | train_error: 1.93
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [====>-----------------------------------] 13.70% | train_error: 1.92
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [====>-----------------------------------] 13.80% | train_error: 1.91
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [====>-----------------------------------] 13.90% | train_error: 1.90
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [====>-----------------------------------] 14.00% | train_error: 1.89
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [====>-----------------------------------] 14.10% | train_error: 1.88
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [====>-----------------------------------] 14.20% | train_error: 1.87
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [====>-----------------------------------] 14.30% | train_error: 1.86
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [====>-----------------------------------] 14.40% | train_error: 1.86
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [====>-----------------------------------] 14.50% | train_error: 1.85
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [====>-----------------------------------] 14.60% | train_error: 1.84
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [====>-----------------------------------] 14.70% | train_error: 1.83
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [====>-----------------------------------] 14.80% | train_error: 1.82
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [====>-----------------------------------] 14.90% | train_error: 1.81
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=====>----------------------------------] 15.00% | train_error: 1.80
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=====>----------------------------------] 15.10% | train_error: 1.79
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=====>----------------------------------] 15.20% | train_error: 1.79
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=====>----------------------------------] 15.30% | train_error: 1.78
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=====>----------------------------------] 15.40% | train_error: 1.77
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=====>----------------------------------] 15.50% | train_error: 1.76
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=====>----------------------------------] 15.60% | train_error: 1.75
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=====>----------------------------------] 15.70% | train_error: 1.74
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=====>----------------------------------] 15.80% | train_error: 1.74
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=====>----------------------------------] 15.90% | train_error: 1.73
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=====>----------------------------------] 16.00% | train_error: 1.72
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=====>----------------------------------] 16.10% | train_error: 1.71
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=====>----------------------------------] 16.20% | train_error: 1.70
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=====>----------------------------------] 16.30% | train_error: 1.69
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=====>----------------------------------] 16.40% | train_error: 1.69
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=====>----------------------------------] 16.50% | train_error: 1.68
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=====>----------------------------------] 16.60% | train_error: 1.67
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=====>----------------------------------] 16.70% | train_error: 1.66
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=====>----------------------------------] 16.80% | train_error: 1.65
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=====>----------------------------------] 16.90% | train_error: 1.65
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=====>----------------------------------] 17.00% | train_error: 1.64
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=====>----------------------------------] 17.10% | train_error: 1.63
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=====>----------------------------------] 17.20% | train_error: 1.62
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=====>----------------------------------] 17.30% | train_error: 1.62
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=====>----------------------------------] 17.40% | train_error: 1.61
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [======>---------------------------------] 17.50% | train_error: 1.60
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [======>---------------------------------] 17.60% | train_error: 1.59
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [======>---------------------------------] 17.70% | train_error: 1.59
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [======>---------------------------------] 17.80% | train_error: 1.58
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [======>---------------------------------] 17.90% | train_error: 1.57
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [======>---------------------------------] 18.00% | train_error: 1.56
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [======>---------------------------------] 18.10% | train_error: 1.56
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [======>---------------------------------] 18.20% | train_error: 1.55
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [======>---------------------------------] 18.30% | train_error: 1.54
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [======>---------------------------------] 18.40% | train_error: 1.53
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [======>---------------------------------] 18.50% | train_error: 1.53
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [======>---------------------------------] 18.60% | train_error: 1.52
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [======>---------------------------------] 18.70% | train_error: 1.51
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [======>---------------------------------] 18.80% | train_error: 1.50
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [======>---------------------------------] 18.90% | train_error: 1.50
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [======>---------------------------------] 19.00% | train_error: 1.49
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [======>---------------------------------] 19.10% | train_error: 1.48
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [======>---------------------------------] 19.20% | train_error: 1.48
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [======>---------------------------------] 19.30% | train_error: 1.47
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [======>---------------------------------] 19.40% | train_error: 1.46
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [======>---------------------------------] 19.50% | train_error: 1.46
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [======>---------------------------------] 19.60% | train_error: 1.45
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [======>---------------------------------] 19.70% | train_error: 1.44
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [======>---------------------------------] 19.80% | train_error: 1.43
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [======>---------------------------------] 19.90% | train_error: 1.43
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=======>--------------------------------] 20.00% | train_error: 1.42
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=======>--------------------------------] 20.10% | train_error: 1.41
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=======>--------------------------------] 20.20% | train_error: 1.41
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=======>--------------------------------] 20.30% | train_error: 1.40
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=======>--------------------------------] 20.40% | train_error: 1.39
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=======>--------------------------------] 20.50% | train_error: 1.39
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=======>--------------------------------] 20.60% | train_error: 1.38
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=======>--------------------------------] 20.70% | train_error: 1.37
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=======>--------------------------------] 20.80% | train_error: 1.37
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=======>--------------------------------] 20.90% | train_error: 1.36
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=======>--------------------------------] 21.00% | train_error: 1.35
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=======>--------------------------------] 21.10% | train_error: 1.35
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=======>--------------------------------] 21.20% | train_error: 1.34
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=======>--------------------------------] 21.30% | train_error: 1.34
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=======>--------------------------------] 21.40% | train_error: 1.33
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=======>--------------------------------] 21.50% | train_error: 1.32
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=======>--------------------------------] 21.60% | train_error: 1.32
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=======>--------------------------------] 21.70% | train_error: 1.31
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=======>--------------------------------] 21.80% | train_error: 1.30
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=======>--------------------------------] 21.90% | train_error: 1.30
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=======>--------------------------------] 22.00% | train_error: 1.29
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=======>--------------------------------] 22.10% | train_error: 1.29
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=======>--------------------------------] 22.20% | train_error: 1.28
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=======>--------------------------------] 22.30% | train_error: 1.27
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=======>--------------------------------] 22.40% | train_error: 1.27
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [========>-------------------------------] 22.50% | train_error: 1.26
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [========>-------------------------------] 22.60% | train_error: 1.26
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [========>-------------------------------] 22.70% | train_error: 1.25
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [========>-------------------------------] 22.80% | train_error: 1.24
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [========>-------------------------------] 22.90% | train_error: 1.24
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [========>-------------------------------] 23.00% | train_error: 1.23
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [========>-------------------------------] 23.10% | train_error: 1.23
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [========>-------------------------------] 23.20% | train_error: 1.22
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [========>-------------------------------] 23.30% | train_error: 1.21
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [========>-------------------------------] 23.40% | train_error: 1.21
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [========>-------------------------------] 23.50% | train_error: 1.20
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [========>-------------------------------] 23.60% | train_error: 1.20
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [========>-------------------------------] 23.70% | train_error: 1.19
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [========>-------------------------------] 23.80% | train_error: 1.19
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [========>-------------------------------] 23.90% | train_error: 1.18
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [========>-------------------------------] 24.00% | train_error: 1.17
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [========>-------------------------------] 24.10% | train_error: 1.17
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [========>-------------------------------] 24.20% | train_error: 1.16
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [========>-------------------------------] 24.30% | train_error: 1.16
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [========>-------------------------------] 24.40% | train_error: 1.15
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [========>-------------------------------] 24.50% | train_error: 1.15
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [========>-------------------------------] 24.60% | train_error: 1.14
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [========>-------------------------------] 24.70% | train_error: 1.14
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [========>-------------------------------] 24.80% | train_error: 1.13
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [========>-------------------------------] 24.90% | train_error: 1.13
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=========>------------------------------] 25.00% | train_error: 1.12
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=========>------------------------------] 25.10% | train_error: 1.11
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=========>------------------------------] 25.20% | train_error: 1.11
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=========>------------------------------] 25.30% | train_error: 1.10
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=========>------------------------------] 25.40% | train_error: 1.10
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=========>------------------------------] 25.50% | train_error: 1.09
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=========>------------------------------] 25.60% | train_error: 1.09
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=========>------------------------------] 25.70% | train_error: 1.08
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=========>------------------------------] 25.80% | train_error: 1.08
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=========>------------------------------] 25.90% | train_error: 1.07
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=========>------------------------------] 26.00% | train_error: 1.07
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=========>------------------------------] 26.10% | train_error: 1.06
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=========>------------------------------] 26.20% | train_error: 1.06
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=========>------------------------------] 26.30% | train_error: 1.05
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=========>------------------------------] 26.40% | train_error: 1.05
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=========>------------------------------] 26.50% | train_error: 1.04
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=========>------------------------------] 26.60% | train_error: 1.04
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=========>------------------------------] 26.70% | train_error: 1.03
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=========>------------------------------] 26.80% | train_error: 1.03
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=========>------------------------------] 26.90% | train_error: 1.02
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=========>------------------------------] 27.00% | train_error: 1.02
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=========>------------------------------] 27.10% | train_error: 1.01
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=========>------------------------------] 27.20% | train_error: 1.01
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=========>------------------------------] 27.30% | train_error: 1.00
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=========>------------------------------] 27.40% | train_error: 0.999
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==========>-----------------------------] 27.50% | train_error: 0.994
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==========>-----------------------------] 27.60% | train_error: 0.990
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==========>-----------------------------] 27.70% | train_error: 0.985
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==========>-----------------------------] 27.80% | train_error: 0.980
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==========>-----------------------------] 27.90% | train_error: 0.976
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==========>-----------------------------] 28.00% | train_error: 0.971
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==========>-----------------------------] 28.10% | train_error: 0.966
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==========>-----------------------------] 28.20% | train_error: 0.962
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==========>-----------------------------] 28.30% | train_error: 0.957
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==========>-----------------------------] 28.40% | train_error: 0.953
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==========>-----------------------------] 28.50% | train_error: 0.948
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==========>-----------------------------] 28.60% | train_error: 0.944
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==========>-----------------------------] 28.70% | train_error: 0.939
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==========>-----------------------------] 28.80% | train_error: 0.935
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==========>-----------------------------] 28.90% | train_error: 0.930
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==========>-----------------------------] 29.00% | train_error: 0.926
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==========>-----------------------------] 29.10% | train_error: 0.922
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==========>-----------------------------] 29.20% | train_error: 0.917
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==========>-----------------------------] 29.30% | train_error: 0.913
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==========>-----------------------------] 29.40% | train_error: 0.909
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==========>-----------------------------] 29.50% | train_error: 0.904
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==========>-----------------------------] 29.60% | train_error: 0.900
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==========>-----------------------------] 29.70% | train_error: 0.896
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==========>-----------------------------] 29.80% | train_error: 0.891
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==========>-----------------------------] 29.90% | train_error: 0.887
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===========>----------------------------] 30.00% | train_error: 0.883
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===========>----------------------------] 30.10% | train_error: 0.879
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===========>----------------------------] 30.20% | train_error: 0.875
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===========>----------------------------] 30.30% | train_error: 0.870
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===========>----------------------------] 30.40% | train_error: 0.866
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===========>----------------------------] 30.50% | train_error: 0.862
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===========>----------------------------] 30.60% | train_error: 0.858
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===========>----------------------------] 30.70% | train_error: 0.854
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===========>----------------------------] 30.80% | train_error: 0.850
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===========>----------------------------] 30.90% | train_error: 0.846
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===========>----------------------------] 31.00% | train_error: 0.842
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===========>----------------------------] 31.10% | train_error: 0.838
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===========>----------------------------] 31.20% | train_error: 0.834
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===========>----------------------------] 31.30% | train_error: 0.830
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===========>----------------------------] 31.40% | train_error: 0.826
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===========>----------------------------] 31.50% | train_error: 0.822
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===========>----------------------------] 31.60% | train_error: 0.818
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===========>----------------------------] 31.70% | train_error: 0.814
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===========>----------------------------] 31.80% | train_error: 0.811
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===========>----------------------------] 31.90% | train_error: 0.807
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===========>----------------------------] 32.00% | train_error: 0.803
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===========>----------------------------] 32.10% | train_error: 0.799
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===========>----------------------------] 32.20% | train_error: 0.795
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===========>----------------------------] 32.30% | train_error: 0.792
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===========>----------------------------] 32.40% | train_error: 0.788
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [============>---------------------------] 32.50% | train_error: 0.784
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [============>---------------------------] 32.60% | train_error: 0.780
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [============>---------------------------] 32.70% | train_error: 0.777
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [============>---------------------------] 32.80% | train_error: 0.773
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [============>---------------------------] 32.90% | train_error: 0.769
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [============>---------------------------] 33.00% | train_error: 0.766
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [============>---------------------------] 33.10% | train_error: 0.762
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [============>---------------------------] 33.20% | train_error: 0.759
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [============>---------------------------] 33.30% | train_error: 0.755
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [============>---------------------------] 33.40% | train_error: 0.751
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [============>---------------------------] 33.50% | train_error: 0.748
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [============>---------------------------] 33.60% | train_error: 0.744
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [============>---------------------------] 33.70% | train_error: 0.741
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [============>---------------------------] 33.80% | train_error: 0.737
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [============>---------------------------] 33.90% | train_error: 0.734
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [============>---------------------------] 34.00% | train_error: 0.730
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [============>---------------------------] 34.10% | train_error: 0.727
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [============>---------------------------] 34.20% | train_error: 0.723
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [============>---------------------------] 34.30% | train_error: 0.720
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [============>---------------------------] 34.40% | train_error: 0.717
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [============>---------------------------] 34.50% | train_error: 0.713
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [============>---------------------------] 34.60% | train_error: 0.710
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [============>---------------------------] 34.70% | train_error: 0.706
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [============>---------------------------] 34.80% | train_error: 0.703
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [============>---------------------------] 34.90% | train_error: 0.700
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=============>--------------------------] 35.00% | train_error: 0.696
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=============>--------------------------] 35.10% | train_error: 0.693
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=============>--------------------------] 35.20% | train_error: 0.690
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=============>--------------------------] 35.30% | train_error: 0.687
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=============>--------------------------] 35.40% | train_error: 0.683
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=============>--------------------------] 35.50% | train_error: 0.680
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=============>--------------------------] 35.60% | train_error: 0.677
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=============>--------------------------] 35.70% | train_error: 0.674
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=============>--------------------------] 35.80% | train_error: 0.670
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=============>--------------------------] 35.90% | train_error: 0.667
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=============>--------------------------] 36.00% | train_error: 0.664
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=============>--------------------------] 36.10% | train_error: 0.661
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=============>--------------------------] 36.20% | train_error: 0.658
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=============>--------------------------] 36.30% | train_error: 0.655
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=============>--------------------------] 36.40% | train_error: 0.652
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=============>--------------------------] 36.50% | train_error: 0.649
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=============>--------------------------] 36.60% | train_error: 0.646
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=============>--------------------------] 36.70% | train_error: 0.642
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=============>--------------------------] 36.80% | train_error: 0.639
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=============>--------------------------] 36.90% | train_error: 0.636
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=============>--------------------------] 37.00% | train_error: 0.633
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=============>--------------------------] 37.10% | train_error: 0.630
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=============>--------------------------] 37.20% | train_error: 0.627
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=============>--------------------------] 37.30% | train_error: 0.624
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=============>--------------------------] 37.40% | train_error: 0.622
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==============>-------------------------] 37.50% | train_error: 0.619
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==============>-------------------------] 37.60% | train_error: 0.616
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==============>-------------------------] 37.70% | train_error: 0.613
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==============>-------------------------] 37.80% | train_error: 0.610
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==============>-------------------------] 37.90% | train_error: 0.607
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==============>-------------------------] 38.00% | train_error: 0.604
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==============>-------------------------] 38.10% | train_error: 0.601
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==============>-------------------------] 38.20% | train_error: 0.598
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==============>-------------------------] 38.30% | train_error: 0.596
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==============>-------------------------] 38.40% | train_error: 0.593
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==============>-------------------------] 38.50% | train_error: 0.590
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==============>-------------------------] 38.60% | train_error: 0.587
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==============>-------------------------] 38.70% | train_error: 0.584
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==============>-------------------------] 38.80% | train_error: 0.582
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==============>-------------------------] 38.90% | train_error: 0.579
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==============>-------------------------] 39.00% | train_error: 0.576
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==============>-------------------------] 39.10% | train_error: 0.573
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==============>-------------------------] 39.20% | train_error: 0.571
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==============>-------------------------] 39.30% | train_error: 0.568
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==============>-------------------------] 39.40% | train_error: 0.565
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==============>-------------------------] 39.50% | train_error: 0.563
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==============>-------------------------] 39.60% | train_error: 0.560
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==============>-------------------------] 39.70% | train_error: 0.557
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==============>-------------------------] 39.80% | train_error: 0.555
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==============>-------------------------] 39.90% | train_error: 0.552
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===============>------------------------] 40.00% | train_error: 0.549
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===============>------------------------] 40.10% | train_error: 0.547
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===============>------------------------] 40.20% | train_error: 0.544
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===============>------------------------] 40.30% | train_error: 0.542
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===============>------------------------] 40.40% | train_error: 0.539
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===============>------------------------] 40.50% | train_error: 0.537
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===============>------------------------] 40.60% | train_error: 0.534
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===============>------------------------] 40.70% | train_error: 0.532
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===============>------------------------] 40.80% | train_error: 0.529
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===============>------------------------] 40.90% | train_error: 0.527
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===============>------------------------] 41.00% | train_error: 0.524
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===============>------------------------] 41.10% | train_error: 0.522
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===============>------------------------] 41.20% | train_error: 0.519
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===============>------------------------] 41.30% | train_error: 0.517
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===============>------------------------] 41.40% | train_error: 0.514
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===============>------------------------] 41.50% | train_error: 0.512
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===============>------------------------] 41.60% | train_error: 0.509
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===============>------------------------] 41.70% | train_error: 0.507
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===============>------------------------] 41.80% | train_error: 0.505
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===============>------------------------] 41.90% | train_error: 0.502
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===============>------------------------] 42.00% | train_error: 0.500
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===============>------------------------] 42.10% | train_error: 0.498
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===============>------------------------] 42.20% | train_error: 0.495
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===============>------------------------] 42.30% | train_error: 0.493
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===============>------------------------] 42.40% | train_error: 0.491
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [================>-----------------------] 42.50% | train_error: 0.488
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [================>-----------------------] 42.60% | train_error: 0.486
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [================>-----------------------] 42.70% | train_error: 0.484
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [================>-----------------------] 42.80% | train_error: 0.481
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [================>-----------------------] 42.90% | train_error: 0.479
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [================>-----------------------] 43.00% | train_error: 0.477
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [================>-----------------------] 43.10% | train_error: 0.475
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [================>-----------------------] 43.20% | train_error: 0.472
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [================>-----------------------] 43.30% | train_error: 0.470
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [================>-----------------------] 43.40% | train_error: 0.468
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [================>-----------------------] 43.50% | train_error: 0.466
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [================>-----------------------] 43.60% | train_error: 0.463
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [================>-----------------------] 43.70% | train_error: 0.461
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [================>-----------------------] 43.80% | train_error: 0.459
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [================>-----------------------] 43.90% | train_error: 0.457
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [================>-----------------------] 44.00% | train_error: 0.455
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [================>-----------------------] 44.10% | train_error: 0.453
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [================>-----------------------] 44.20% | train_error: 0.451
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [================>-----------------------] 44.30% | train_error: 0.448
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [================>-----------------------] 44.40% | train_error: 0.446
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [================>-----------------------] 44.50% | train_error: 0.444
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [================>-----------------------] 44.60% | train_error: 0.442
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [================>-----------------------] 44.70% | train_error: 0.440
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [================>-----------------------] 44.80% | train_error: 0.438
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [================>-----------------------] 44.90% | train_error: 0.436
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=================>----------------------] 45.00% | train_error: 0.434
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=================>----------------------] 45.10% | train_error: 0.432
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=================>----------------------] 45.20% | train_error: 0.430
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=================>----------------------] 45.30% | train_error: 0.428
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=================>----------------------] 45.40% | train_error: 0.426
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=================>----------------------] 45.50% | train_error: 0.424
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=================>----------------------] 45.60% | train_error: 0.422
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=================>----------------------] 45.70% | train_error: 0.420
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=================>----------------------] 45.80% | train_error: 0.418
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=================>----------------------] 45.90% | train_error: 0.416
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=================>----------------------] 46.00% | train_error: 0.414
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=================>----------------------] 46.10% | train_error: 0.412
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=================>----------------------] 46.20% | train_error: 0.410
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=================>----------------------] 46.30% | train_error: 0.408
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=================>----------------------] 46.40% | train_error: 0.406
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=================>----------------------] 46.50% | train_error: 0.404
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=================>----------------------] 46.60% | train_error: 0.402
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=================>----------------------] 46.70% | train_error: 0.400
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=================>----------------------] 46.80% | train_error: 0.398
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=================>----------------------] 46.90% | train_error: 0.397
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=================>----------------------] 47.00% | train_error: 0.395
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=================>----------------------] 47.10% | train_error: 0.393
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=================>----------------------] 47.20% | train_error: 0.391
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=================>----------------------] 47.30% | train_error: 0.389
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=================>----------------------] 47.40% | train_error: 0.387
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==================>---------------------] 47.50% | train_error: 0.386
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==================>---------------------] 47.60% | train_error: 0.384
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==================>---------------------] 47.70% | train_error: 0.382
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==================>---------------------] 47.80% | train_error: 0.380
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==================>---------------------] 47.90% | train_error: 0.378
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==================>---------------------] 48.00% | train_error: 0.377
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==================>---------------------] 48.10% | train_error: 0.375
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==================>---------------------] 48.20% | train_error: 0.373
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==================>---------------------] 48.30% | train_error: 0.371
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==================>---------------------] 48.40% | train_error: 0.370
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==================>---------------------] 48.50% | train_error: 0.368
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==================>---------------------] 48.60% | train_error: 0.366
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==================>---------------------] 48.70% | train_error: 0.364
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==================>---------------------] 48.80% | train_error: 0.363
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==================>---------------------] 48.90% | train_error: 0.361
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==================>---------------------] 49.00% | train_error: 0.359
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==================>---------------------] 49.10% | train_error: 0.358
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==================>---------------------] 49.20% | train_error: 0.356
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==================>---------------------] 49.30% | train_error: 0.354
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==================>---------------------] 49.40% | train_error: 0.353
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==================>---------------------] 49.50% | train_error: 0.351
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==================>---------------------] 49.60% | train_error: 0.349
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==================>---------------------] 49.70% | train_error: 0.348
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==================>---------------------] 49.80% | train_error: 0.346
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==================>---------------------] 49.90% | train_error: 0.344
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===================>--------------------] 50.00% | train_error: 0.343
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===================>--------------------] 50.10% | train_error: 0.341
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===================>--------------------] 50.20% | train_error: 0.339
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===================>--------------------] 50.30% | train_error: 0.338
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===================>--------------------] 50.40% | train_error: 0.336
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===================>--------------------] 50.50% | train_error: 0.335
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===================>--------------------] 50.60% | train_error: 0.333
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===================>--------------------] 50.70% | train_error: 0.332
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===================>--------------------] 50.80% | train_error: 0.330
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===================>--------------------] 50.90% | train_error: 0.328
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===================>--------------------] 51.00% | train_error: 0.327
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===================>--------------------] 51.10% | train_error: 0.325
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===================>--------------------] 51.20% | train_error: 0.324
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===================>--------------------] 51.30% | train_error: 0.322
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===================>--------------------] 51.40% | train_error: 0.321
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===================>--------------------] 51.50% | train_error: 0.319
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===================>--------------------] 51.60% | train_error: 0.318
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===================>--------------------] 51.70% | train_error: 0.316
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===================>--------------------] 51.80% | train_error: 0.315
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===================>--------------------] 51.90% | train_error: 0.313
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===================>--------------------] 52.00% | train_error: 0.312
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===================>--------------------] 52.10% | train_error: 0.310
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===================>--------------------] 52.20% | train_error: 0.309
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===================>--------------------] 52.30% | train_error: 0.308
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===================>--------------------] 52.40% | train_error: 0.306
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [====================>-------------------] 52.50% | train_error: 0.305
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [====================>-------------------] 52.60% | train_error: 0.303
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [====================>-------------------] 52.70% | train_error: 0.302
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [====================>-------------------] 52.80% | train_error: 0.300
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [====================>-------------------] 52.90% | train_error: 0.299
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [====================>-------------------] 53.00% | train_error: 0.298
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [====================>-------------------] 53.10% | train_error: 0.296
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [====================>-------------------] 53.20% | train_error: 0.295
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [====================>-------------------] 53.30% | train_error: 0.293
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [====================>-------------------] 53.40% | train_error: 0.292
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [====================>-------------------] 53.50% | train_error: 0.291
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [====================>-------------------] 53.60% | train_error: 0.289
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [====================>-------------------] 53.70% | train_error: 0.288
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [====================>-------------------] 53.80% | train_error: 0.287
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [====================>-------------------] 53.90% | train_error: 0.285
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [====================>-------------------] 54.00% | train_error: 0.284
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [====================>-------------------] 54.10% | train_error: 0.283
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [====================>-------------------] 54.20% | train_error: 0.281
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [====================>-------------------] 54.30% | train_error: 0.280
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [====================>-------------------] 54.40% | train_error: 0.279
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [====================>-------------------] 54.50% | train_error: 0.277
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [====================>-------------------] 54.60% | train_error: 0.276
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [====================>-------------------] 54.70% | train_error: 0.275
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [====================>-------------------] 54.80% | train_error: 0.273
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [====================>-------------------] 54.90% | train_error: 0.272
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=====================>------------------] 55.00% | train_error: 0.271
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=====================>------------------] 55.10% | train_error: 0.270
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=====================>------------------] 55.20% | train_error: 0.268
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=====================>------------------] 55.30% | train_error: 0.267
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=====================>------------------] 55.40% | train_error: 0.266
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=====================>------------------] 55.50% | train_error: 0.265
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=====================>------------------] 55.60% | train_error: 0.263
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=====================>------------------] 55.70% | train_error: 0.262
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=====================>------------------] 55.80% | train_error: 0.261
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=====================>------------------] 55.90% | train_error: 0.260
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=====================>------------------] 56.00% | train_error: 0.259
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=====================>------------------] 56.10% | train_error: 0.257
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=====================>------------------] 56.20% | train_error: 0.256
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=====================>------------------] 56.30% | train_error: 0.255
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=====================>------------------] 56.40% | train_error: 0.254
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=====================>------------------] 56.50% | train_error: 0.253
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=====================>------------------] 56.60% | train_error: 0.251
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=====================>------------------] 56.70% | train_error: 0.250
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=====================>------------------] 56.80% | train_error: 0.249
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=====================>------------------] 56.90% | train_error: 0.248
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=====================>------------------] 57.00% | train_error: 0.247
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=====================>------------------] 57.10% | train_error: 0.246
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=====================>------------------] 57.20% | train_error: 0.244
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=====================>------------------] 57.30% | train_error: 0.243
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=====================>------------------] 57.40% | train_error: 0.242
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [======================>-----------------] 57.50% | train_error: 0.241
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [======================>-----------------] 57.60% | train_error: 0.240
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [======================>-----------------] 57.70% | train_error: 0.239
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [======================>-----------------] 57.80% | train_error: 0.238
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [======================>-----------------] 57.90% | train_error: 0.237
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [======================>-----------------] 58.00% | train_error: 0.235
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [======================>-----------------] 58.10% | train_error: 0.234
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [======================>-----------------] 58.20% | train_error: 0.233
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [======================>-----------------] 58.30% | train_error: 0.232
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [======================>-----------------] 58.40% | train_error: 0.231
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [======================>-----------------] 58.50% | train_error: 0.230
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [======================>-----------------] 58.60% | train_error: 0.229
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [======================>-----------------] 58.70% | train_error: 0.228
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [======================>-----------------] 58.80% | train_error: 0.227
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [======================>-----------------] 58.90% | train_error: 0.226
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [======================>-----------------] 59.00% | train_error: 0.225
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [======================>-----------------] 59.10% | train_error: 0.224
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [======================>-----------------] 59.20% | train_error: 0.223
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [======================>-----------------] 59.30% | train_error: 0.222
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [======================>-----------------] 59.40% | train_error: 0.221
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [======================>-----------------] 59.50% | train_error: 0.219
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [======================>-----------------] 59.60% | train_error: 0.218
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [======================>-----------------] 59.70% | train_error: 0.217
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [======================>-----------------] 59.80% | train_error: 0.216
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [======================>-----------------] 59.90% | train_error: 0.215
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=======================>----------------] 60.00% | train_error: 0.214
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=======================>----------------] 60.10% | train_error: 0.213
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=======================>----------------] 60.20% | train_error: 0.212
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=======================>----------------] 60.30% | train_error: 0.211
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=======================>----------------] 60.40% | train_error: 0.210
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=======================>----------------] 60.50% | train_error: 0.209
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=======================>----------------] 60.60% | train_error: 0.209
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=======================>----------------] 60.70% | train_error: 0.208
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=======================>----------------] 60.80% | train_error: 0.207
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=======================>----------------] 60.90% | train_error: 0.206
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=======================>----------------] 61.00% | train_error: 0.205
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=======================>----------------] 61.10% | train_error: 0.204
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=======================>----------------] 61.20% | train_error: 0.203
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=======================>----------------] 61.30% | train_error: 0.202
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=======================>----------------] 61.40% | train_error: 0.201
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=======================>----------------] 61.50% | train_error: 0.200
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=======================>----------------] 61.60% | train_error: 0.199
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=======================>----------------] 61.70% | train_error: 0.198
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=======================>----------------] 61.80% | train_error: 0.197
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=======================>----------------] 61.90% | train_error: 0.196
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=======================>----------------] 62.00% | train_error: 0.195
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=======================>----------------] 62.10% | train_error: 0.194
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=======================>----------------] 62.20% | train_error: 0.194
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=======================>----------------] 62.30% | train_error: 0.193
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=======================>----------------] 62.40% | train_error: 0.192
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [========================>---------------] 62.50% | train_error: 0.191
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [========================>---------------] 62.60% | train_error: 0.190
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [========================>---------------] 62.70% | train_error: 0.189
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [========================>---------------] 62.80% | train_error: 0.188
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [========================>---------------] 62.90% | train_error: 0.187
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [========================>---------------] 63.00% | train_error: 0.186
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [========================>---------------] 63.10% | train_error: 0.186
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [========================>---------------] 63.20% | train_error: 0.185
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [========================>---------------] 63.30% | train_error: 0.184
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [========================>---------------] 63.40% | train_error: 0.183
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [========================>---------------] 63.50% | train_error: 0.182
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [========================>---------------] 63.60% | train_error: 0.181
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [========================>---------------] 63.70% | train_error: 0.180
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [========================>---------------] 63.80% | train_error: 0.180
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [========================>---------------] 63.90% | train_error: 0.179
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [========================>---------------] 64.00% | train_error: 0.178
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [========================>---------------] 64.10% | train_error: 0.177
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [========================>---------------] 64.20% | train_error: 0.176
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [========================>---------------] 64.30% | train_error: 0.176
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [========================>---------------] 64.40% | train_error: 0.175
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [========================>---------------] 64.50% | train_error: 0.174
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [========================>---------------] 64.60% | train_error: 0.173
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [========================>---------------] 64.70% | train_error: 0.172
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [========================>---------------] 64.80% | train_error: 0.171
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [========================>---------------] 64.90% | train_error: 0.171
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=========================>--------------] 65.00% | train_error: 0.170
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=========================>--------------] 65.10% | train_error: 0.169
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=========================>--------------] 65.20% | train_error: 0.168
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=========================>--------------] 65.30% | train_error: 0.168
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=========================>--------------] 65.40% | train_error: 0.167
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=========================>--------------] 65.50% | train_error: 0.166
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=========================>--------------] 65.60% | train_error: 0.165
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=========================>--------------] 65.70% | train_error: 0.164
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=========================>--------------] 65.80% | train_error: 0.164
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=========================>--------------] 65.90% | train_error: 0.163
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=========================>--------------] 66.00% | train_error: 0.162
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=========================>--------------] 66.10% | train_error: 0.161
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=========================>--------------] 66.20% | train_error: 0.161
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=========================>--------------] 66.30% | train_error: 0.160
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=========================>--------------] 66.40% | train_error: 0.159
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=========================>--------------] 66.50% | train_error: 0.158
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=========================>--------------] 66.60% | train_error: 0.158
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=========================>--------------] 66.70% | train_error: 0.157
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=========================>--------------] 66.80% | train_error: 0.156
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=========================>--------------] 66.90% | train_error: 0.156
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=========================>--------------] 67.00% | train_error: 0.155
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=========================>--------------] 67.10% | train_error: 0.154
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=========================>--------------] 67.20% | train_error: 0.153
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=========================>--------------] 67.30% | train_error: 0.153
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=========================>--------------] 67.40% | train_error: 0.152
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==========================>-------------] 67.50% | train_error: 0.151
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==========================>-------------] 67.60% | train_error: 0.151
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==========================>-------------] 67.70% | train_error: 0.150
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==========================>-------------] 67.80% | train_error: 0.149
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==========================>-------------] 67.90% | train_error: 0.149
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==========================>-------------] 68.00% | train_error: 0.148
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==========================>-------------] 68.10% | train_error: 0.147
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==========================>-------------] 68.20% | train_error: 0.146
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==========================>-------------] 68.30% | train_error: 0.146
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==========================>-------------] 68.40% | train_error: 0.145
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==========================>-------------] 68.50% | train_error: 0.144
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==========================>-------------] 68.60% | train_error: 0.144
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==========================>-------------] 68.70% | train_error: 0.143
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==========================>-------------] 68.80% | train_error: 0.142
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==========================>-------------] 68.90% | train_error: 0.142
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==========================>-------------] 69.00% | train_error: 0.141
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==========================>-------------] 69.10% | train_error: 0.141
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==========================>-------------] 69.20% | train_error: 0.140
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==========================>-------------] 69.30% | train_error: 0.139
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==========================>-------------] 69.40% | train_error: 0.139
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==========================>-------------] 69.50% | train_error: 0.138
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==========================>-------------] 69.60% | train_error: 0.137
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==========================>-------------] 69.70% | train_error: 0.137
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==========================>-------------] 69.80% | train_error: 0.136
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==========================>-------------] 69.90% | train_error: 0.135
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===========================>------------] 70.00% | train_error: 0.135
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===========================>------------] 70.10% | train_error: 0.134
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===========================>------------] 70.20% | train_error: 0.134
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===========================>------------] 70.30% | train_error: 0.133
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===========================>------------] 70.40% | train_error: 0.132
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===========================>------------] 70.50% | train_error: 0.132
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===========================>------------] 70.60% | train_error: 0.131
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===========================>------------] 70.70% | train_error: 0.131
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===========================>------------] 70.80% | train_error: 0.130
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===========================>------------] 70.90% | train_error: 0.129
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===========================>------------] 71.00% | train_error: 0.129
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===========================>------------] 71.10% | train_error: 0.128
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===========================>------------] 71.20% | train_error: 0.128
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===========================>------------] 71.30% | train_error: 0.127
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===========================>------------] 71.40% | train_error: 0.126
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===========================>------------] 71.50% | train_error: 0.126
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===========================>------------] 71.60% | train_error: 0.125
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===========================>------------] 71.70% | train_error: 0.125
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===========================>------------] 71.80% | train_error: 0.124
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===========================>------------] 71.90% | train_error: 0.124
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===========================>------------] 72.00% | train_error: 0.123
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===========================>------------] 72.10% | train_error: 0.122
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===========================>------------] 72.20% | train_error: 0.122
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===========================>------------] 72.30% | train_error: 0.121
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===========================>------------] 72.40% | train_error: 0.121
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [============================>-----------] 72.50% | train_error: 0.120
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [============================>-----------] 72.60% | train_error: 0.120
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [============================>-----------] 72.70% | train_error: 0.119
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [============================>-----------] 72.80% | train_error: 0.119
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [============================>-----------] 72.90% | train_error: 0.118
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [============================>-----------] 73.00% | train_error: 0.117
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [============================>-----------] 73.10% | train_error: 0.117
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [============================>-----------] 73.20% | train_error: 0.116
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [============================>-----------] 73.30% | train_error: 0.116
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [============================>-----------] 73.40% | train_error: 0.115
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [============================>-----------] 73.50% | train_error: 0.115
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [============================>-----------] 73.60% | train_error: 0.114
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [============================>-----------] 73.70% | train_error: 0.114
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [============================>-----------] 73.80% | train_error: 0.113
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [============================>-----------] 73.90% | train_error: 0.113
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [============================>-----------] 74.00% | train_error: 0.112
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [============================>-----------] 74.10% | train_error: 0.112
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [============================>-----------] 74.20% | train_error: 0.111
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [============================>-----------] 74.30% | train_error: 0.111
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [============================>-----------] 74.40% | train_error: 0.110
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [============================>-----------] 74.50% | train_error: 0.110
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [============================>-----------] 74.60% | train_error: 0.109
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [============================>-----------] 74.70% | train_error: 0.109
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [============================>-----------] 74.80% | train_error: 0.108
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [============================>-----------] 74.90% | train_error: 0.108
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=============================>----------] 75.00% | train_error: 0.107
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=============================>----------] 75.10% | train_error: 0.107
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=============================>----------] 75.20% | train_error: 0.106
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=============================>----------] 75.30% | train_error: 0.106
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=============================>----------] 75.40% | train_error: 0.105
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=============================>----------] 75.50% | train_error: 0.105
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=============================>----------] 75.60% | train_error: 0.104
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=============================>----------] 75.70% | train_error: 0.104
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=============================>----------] 75.80% | train_error: 0.103
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=============================>----------] 75.90% | train_error: 0.103
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=============================>----------] 76.00% | train_error: 0.102
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=============================>----------] 76.10% | train_error: 0.102
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=============================>----------] 76.20% | train_error: 0.101
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=============================>----------] 76.30% | train_error: 0.101
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=============================>----------] 76.40% | train_error: 0.101
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=============================>----------] 76.50% | train_error: 0.100
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=============================>----------] 76.60% | train_error: 0.0996
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=============================>----------] 76.70% | train_error: 0.0992
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=============================>----------] 76.80% | train_error: 0.0987
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=============================>----------] 76.90% | train_error: 0.0983
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=============================>----------] 77.00% | train_error: 0.0978
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=============================>----------] 77.10% | train_error: 0.0974
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=============================>----------] 77.20% | train_error: 0.0969
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=============================>----------] 77.30% | train_error: 0.0965
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=============================>----------] 77.40% | train_error: 0.0961
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==============================>---------] 77.50% | train_error: 0.0956
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==============================>---------] 77.60% | train_error: 0.0952
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==============================>---------] 77.70% | train_error: 0.0948
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==============================>---------] 77.80% | train_error: 0.0943
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==============================>---------] 77.90% | train_error: 0.0939
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==============================>---------] 78.00% | train_error: 0.0935
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==============================>---------] 78.10% | train_error: 0.0930
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==============================>---------] 78.20% | train_error: 0.0926
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==============================>---------] 78.30% | train_error: 0.0922
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==============================>---------] 78.40% | train_error: 0.0918
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==============================>---------] 78.50% | train_error: 0.0914
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==============================>---------] 78.60% | train_error: 0.0910
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==============================>---------] 78.70% | train_error: 0.0905
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==============================>---------] 78.80% | train_error: 0.0901
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==============================>---------] 78.90% | train_error: 0.0897
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==============================>---------] 79.00% | train_error: 0.0893
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==============================>---------] 79.10% | train_error: 0.0889
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==============================>---------] 79.20% | train_error: 0.0885
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==============================>---------] 79.30% | train_error: 0.0881
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==============================>---------] 79.40% | train_error: 0.0877
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==============================>---------] 79.50% | train_error: 0.0873
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==============================>---------] 79.60% | train_error: 0.0869
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==============================>---------] 79.70% | train_error: 0.0865
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==============================>---------] 79.80% | train_error: 0.0861
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==============================>---------] 79.90% | train_error: 0.0858
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===============================>--------] 80.00% | train_error: 0.0854
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===============================>--------] 80.10% | train_error: 0.0850
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===============================>--------] 80.20% | train_error: 0.0846
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===============================>--------] 80.30% | train_error: 0.0842
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===============================>--------] 80.40% | train_error: 0.0838
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===============================>--------] 80.50% | train_error: 0.0835
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===============================>--------] 80.60% | train_error: 0.0831
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===============================>--------] 80.70% | train_error: 0.0827
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===============================>--------] 80.80% | train_error: 0.0823
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===============================>--------] 80.90% | train_error: 0.0820
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===============================>--------] 81.00% | train_error: 0.0816
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===============================>--------] 81.10% | train_error: 0.0812
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===============================>--------] 81.20% | train_error: 0.0809
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===============================>--------] 81.30% | train_error: 0.0805
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===============================>--------] 81.40% | train_error: 0.0801
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===============================>--------] 81.50% | train_error: 0.0798
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===============================>--------] 81.60% | train_error: 0.0794
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===============================>--------] 81.70% | train_error: 0.0791
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===============================>--------] 81.80% | train_error: 0.0787
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===============================>--------] 81.90% | train_error: 0.0784
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===============================>--------] 82.00% | train_error: 0.0780
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===============================>--------] 82.10% | train_error: 0.0776
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===============================>--------] 82.20% | train_error: 0.0773
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===============================>--------] 82.30% | train_error: 0.0770
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===============================>--------] 82.40% | train_error: 0.0766
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [================================>-------] 82.50% | train_error: 0.0763
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [================================>-------] 82.60% | train_error: 0.0759
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [================================>-------] 82.70% | train_error: 0.0756
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [================================>-------] 82.80% | train_error: 0.0752
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [================================>-------] 82.90% | train_error: 0.0749
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [================================>-------] 83.00% | train_error: 0.0746
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [================================>-------] 83.10% | train_error: 0.0742
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [================================>-------] 83.20% | train_error: 0.0739
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [================================>-------] 83.30% | train_error: 0.0736
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [================================>-------] 83.40% | train_error: 0.0732
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [================================>-------] 83.50% | train_error: 0.0729
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [================================>-------] 83.60% | train_error: 0.0726
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [================================>-------] 83.70% | train_error: 0.0723
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [================================>-------] 83.80% | train_error: 0.0719
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [================================>-------] 83.90% | train_error: 0.0716
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [================================>-------] 84.00% | train_error: 0.0713
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [================================>-------] 84.10% | train_error: 0.0710
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [================================>-------] 84.20% | train_error: 0.0707
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [================================>-------] 84.30% | train_error: 0.0703
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [================================>-------] 84.40% | train_error: 0.0700
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [================================>-------] 84.50% | train_error: 0.0697
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [================================>-------] 84.60% | train_error: 0.0694
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [================================>-------] 84.70% | train_error: 0.0691
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [================================>-------] 84.80% | train_error: 0.0688
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [================================>-------] 84.90% | train_error: 0.0685
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=================================>------] 85.00% | train_error: 0.0682
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=================================>------] 85.10% | train_error: 0.0679
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=================================>------] 85.20% | train_error: 0.0676
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=================================>------] 85.30% | train_error: 0.0673
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=================================>------] 85.40% | train_error: 0.0670
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=================================>------] 85.50% | train_error: 0.0667
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=================================>------] 85.60% | train_error: 0.0664
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=================================>------] 85.70% | train_error: 0.0661
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=================================>------] 85.80% | train_error: 0.0658
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=================================>------] 85.90% | train_error: 0.0655
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=================================>------] 86.00% | train_error: 0.0652
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=================================>------] 86.10% | train_error: 0.0649
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=================================>------] 86.20% | train_error: 0.0646
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=================================>------] 86.30% | train_error: 0.0643
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=================================>------] 86.40% | train_error: 0.0641
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=================================>------] 86.50% | train_error: 0.0638
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=================================>------] 86.60% | train_error: 0.0635
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=================================>------] 86.70% | train_error: 0.0632
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=================================>------] 86.80% | train_error: 0.0629
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=================================>------] 86.90% | train_error: 0.0626
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=================================>------] 87.00% | train_error: 0.0624
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=================================>------] 87.10% | train_error: 0.0621
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=================================>------] 87.20% | train_error: 0.0618
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=================================>------] 87.30% | train_error: 0.0615
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=================================>------] 87.40% | train_error: 0.0613
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==================================>-----] 87.50% | train_error: 0.0610
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==================================>-----] 87.60% | train_error: 0.0607
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==================================>-----] 87.70% | train_error: 0.0605
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==================================>-----] 87.80% | train_error: 0.0602
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==================================>-----] 87.90% | train_error: 0.0599
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==================================>-----] 88.00% | train_error: 0.0597
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==================================>-----] 88.10% | train_error: 0.0594
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==================================>-----] 88.20% | train_error: 0.0591
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==================================>-----] 88.30% | train_error: 0.0589
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==================================>-----] 88.40% | train_error: 0.0586
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==================================>-----] 88.50% | train_error: 0.0584
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==================================>-----] 88.60% | train_error: 0.0581
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==================================>-----] 88.70% | train_error: 0.0578
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==================================>-----] 88.80% | train_error: 0.0576
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==================================>-----] 88.90% | train_error: 0.0573
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==================================>-----] 89.00% | train_error: 0.0571
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==================================>-----] 89.10% | train_error: 0.0568
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==================================>-----] 89.20% | train_error: 0.0566
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==================================>-----] 89.30% | train_error: 0.0563
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==================================>-----] 89.40% | train_error: 0.0561
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==================================>-----] 89.50% | train_error: 0.0558
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==================================>-----] 89.60% | train_error: 0.0556
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==================================>-----] 89.70% | train_error: 0.0553
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==================================>-----] 89.80% | train_error: 0.0551
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [==================================>-----] 89.90% | train_error: 0.0549
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===================================>----] 90.00% | train_error: 0.0546
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===================================>----] 90.10% | train_error: 0.0544
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===================================>----] 90.20% | train_error: 0.0541
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===================================>----] 90.30% | train_error: 0.0539
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===================================>----] 90.40% | train_error: 0.0537
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===================================>----] 90.50% | train_error: 0.0534
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===================================>----] 90.60% | train_error: 0.0532
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===================================>----] 90.70% | train_error: 0.0530
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===================================>----] 90.80% | train_error: 0.0527
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===================================>----] 90.90% | train_error: 0.0525
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===================================>----] 91.00% | train_error: 0.0523
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===================================>----] 91.10% | train_error: 0.0520
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===================================>----] 91.20% | train_error: 0.0518
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===================================>----] 91.30% | train_error: 0.0516
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===================================>----] 91.40% | train_error: 0.0514
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===================================>----] 91.50% | train_error: 0.0511
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===================================>----] 91.60% | train_error: 0.0509
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===================================>----] 91.70% | train_error: 0.0507
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===================================>----] 91.80% | train_error: 0.0505
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===================================>----] 91.90% | train_error: 0.0503
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===================================>----] 92.00% | train_error: 0.0500
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===================================>----] 92.10% | train_error: 0.0498
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===================================>----] 92.20% | train_error: 0.0496
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===================================>----] 92.30% | train_error: 0.0494
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [===================================>----] 92.40% | train_error: 0.0492
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [====================================>---] 92.50% | train_error: 0.0490
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [====================================>---] 92.60% | train_error: 0.0487
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [====================================>---] 92.70% | train_error: 0.0485
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [====================================>---] 92.80% | train_error: 0.0483
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [====================================>---] 92.90% | train_error: 0.0481
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [====================================>---] 93.00% | train_error: 0.0479
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [====================================>---] 93.10% | train_error: 0.0477
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [====================================>---] 93.20% | train_error: 0.0475
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [====================================>---] 93.30% | train_error: 0.0473
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [====================================>---] 93.40% | train_error: 0.0471
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [====================================>---] 93.50% | train_error: 0.0469
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [====================================>---] 93.60% | train_error: 0.0467
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [====================================>---] 93.70% | train_error: 0.0465
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [====================================>---] 93.80% | train_error: 0.0463
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [====================================>---] 93.90% | train_error: 0.0461
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [====================================>---] 94.00% | train_error: 0.0459
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [====================================>---] 94.10% | train_error: 0.0457
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [====================================>---] 94.20% | train_error: 0.0455
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [====================================>---] 94.30% | train_error: 0.0453
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [====================================>---] 94.40% | train_error: 0.0451
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [====================================>---] 94.50% | train_error: 0.0449
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [====================================>---] 94.60% | train_error: 0.0447
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [====================================>---] 94.70% | train_error: 0.0445
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [====================================>---] 94.80% | train_error: 0.0443
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [====================================>---] 94.90% | train_error: 0.0441
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=====================================>--] 95.00% | train_error: 0.0439
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=====================================>--] 95.10% | train_error: 0.0437
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=====================================>--] 95.20% | train_error: 0.0435
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=====================================>--] 95.30% | train_error: 0.0434
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=====================================>--] 95.40% | train_error: 0.0432
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=====================================>--] 95.50% | train_error: 0.0430
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=====================================>--] 95.60% | train_error: 0.0428
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=====================================>--] 95.70% | train_error: 0.0426
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=====================================>--] 95.80% | train_error: 0.0424
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=====================================>--] 95.90% | train_error: 0.0423
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=====================================>--] 96.00% | train_error: 0.0421
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=====================================>--] 96.10% | train_error: 0.0419
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=====================================>--] 96.20% | train_error: 0.0417
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=====================================>--] 96.30% | train_error: 0.0415
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=====================================>--] 96.40% | train_error: 0.0414
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=====================================>--] 96.50% | train_error: 0.0412
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=====================================>--] 96.60% | train_error: 0.0410
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=====================================>--] 96.70% | train_error: 0.0408
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=====================================>--] 96.80% | train_error: 0.0407
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=====================================>--] 96.90% | train_error: 0.0405
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=====================================>--] 97.00% | train_error: 0.0403
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=====================================>--] 97.10% | train_error: 0.0401
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=====================================>--] 97.20% | train_error: 0.0400
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=====================================>--] 97.30% | train_error: 0.0398
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=====================================>--] 97.40% | train_error: 0.0396
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [======================================>-] 97.50% | train_error: 0.0395
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [======================================>-] 97.60% | train_error: 0.0393
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [======================================>-] 97.70% | train_error: 0.0391
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [======================================>-] 97.80% | train_error: 0.0389
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [======================================>-] 97.90% | train_error: 0.0388
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [======================================>-] 98.00% | train_error: 0.0386
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [======================================>-] 98.10% | train_error: 0.0385
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [======================================>-] 98.20% | train_error: 0.0383
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [======================================>-] 98.30% | train_error: 0.0381
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [======================================>-] 98.40% | train_error: 0.0380
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [======================================>-] 98.50% | train_error: 0.0378
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [======================================>-] 98.60% | train_error: 0.0376
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [======================================>-] 98.70% | train_error: 0.0375
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [======================================>-] 98.80% | train_error: 0.0373
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [======================================>-] 98.90% | train_error: 0.0372
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [======================================>-] 99.00% | train_error: 0.0370
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [======================================>-] 99.10% | train_error: 0.0369
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [======================================>-] 99.20% | train_error: 0.0367
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [======================================>-] 99.30% | train_error: 0.0365
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [======================================>-] 99.40% | train_error: 0.0364
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [======================================>-] 99.50% | train_error: 0.0362
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [======================================>-] 99.60% | train_error: 0.0361
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [======================================>-] 99.70% | train_error: 0.0359
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [======================================>-] 99.80% | train_error: 0.0358
|
||
</pre></div>
|
||
</div>
|
||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [======================================>-] 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> [=======================================>] 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'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>
|
||
</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> [>---------------------------------------] 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> [>---------------------------------------] 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> [>---------------------------------------] 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> [>---------------------------------------] 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> [>---------------------------------------] 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> [>---------------------------------------] 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> [>---------------------------------------] 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> [>---------------------------------------] 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> [>---------------------------------------] 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> [>---------------------------------------] 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> [>---------------------------------------] 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> [>---------------------------------------] 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> [>---------------------------------------] 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> [>---------------------------------------] 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> [>---------------------------------------] 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> [>---------------------------------------] 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> [>---------------------------------------] 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> [>---------------------------------------] 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> [>---------------------------------------] 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> [>---------------------------------------] 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> [>---------------------------------------] 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> [>---------------------------------------] 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> [>---------------------------------------] 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> [>---------------------------------------] 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> [>---------------------------------------] 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> [=>--------------------------------------] 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> [=>--------------------------------------] 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> [=>--------------------------------------] 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> [=>--------------------------------------] 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> [=>--------------------------------------] 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> [=>--------------------------------------] 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> [=>--------------------------------------] 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> [=>--------------------------------------] 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> [=>--------------------------------------] 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> [=>--------------------------------------] 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> [=>--------------------------------------] 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> [=>--------------------------------------] 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> [=>--------------------------------------] 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> [=>--------------------------------------] 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> [=>--------------------------------------] 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> [=>--------------------------------------] 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> [=>--------------------------------------] 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> [=>--------------------------------------] 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> [=>--------------------------------------] 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> [=>--------------------------------------] 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> [=>--------------------------------------] 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> [=>--------------------------------------] 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> [=>--------------------------------------] 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> [=>--------------------------------------] 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> [=>--------------------------------------] 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> [==>-------------------------------------] 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> [==>-------------------------------------] 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> [==>-------------------------------------] 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> [==>-------------------------------------] 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> [==>-------------------------------------] 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> [==>-------------------------------------] 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> [==>-------------------------------------] 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> [==>-------------------------------------] 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> [==>-------------------------------------] 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> [==>-------------------------------------] 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> [==>-------------------------------------] 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> [==>-------------------------------------] 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> [==>-------------------------------------] 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> [==>-------------------------------------] 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> [==>-------------------------------------] 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> [==>-------------------------------------] 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> [==>-------------------------------------] 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> [==>-------------------------------------] 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> [==>-------------------------------------] 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> [==>-------------------------------------] 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> [==>-------------------------------------] 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> [==>-------------------------------------] 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> [==>-------------------------------------] 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> [==>-------------------------------------] 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> [==>-------------------------------------] 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> [===>------------------------------------] 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> [===>------------------------------------] 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> [===>------------------------------------] 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> [===>------------------------------------] 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> [===>------------------------------------] 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> [===>------------------------------------] 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> [===>------------------------------------] 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> [===>------------------------------------] 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> [===>------------------------------------] 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> [===>------------------------------------] 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> [===>------------------------------------] 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> [===>------------------------------------] 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> [===>------------------------------------] 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> [===>------------------------------------] 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> [===>------------------------------------] 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> [===>------------------------------------] 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> [===>------------------------------------] 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> [===>------------------------------------] 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> [===>------------------------------------] 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> [===>------------------------------------] 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> [===>------------------------------------] 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> [===>------------------------------------] 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> [===>------------------------------------] 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> [===>------------------------------------] 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> [===>------------------------------------] 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> [====>-----------------------------------] 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> [====>-----------------------------------] 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> [====>-----------------------------------] 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> [====>-----------------------------------] 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> [====>-----------------------------------] 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> [====>-----------------------------------] 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> [====>-----------------------------------] 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> [====>-----------------------------------] 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> [====>-----------------------------------] 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> [====>-----------------------------------] 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> [====>-----------------------------------] 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> [====>-----------------------------------] 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> [====>-----------------------------------] 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> [====>-----------------------------------] 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> [====>-----------------------------------] 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> [====>-----------------------------------] 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> [====>-----------------------------------] 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> [====>-----------------------------------] 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> [====>-----------------------------------] 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> [====>-----------------------------------] 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> [====>-----------------------------------] 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> [====>-----------------------------------] 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> [====>-----------------------------------] 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> [====>-----------------------------------] 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> [====>-----------------------------------] 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> [=====>----------------------------------] 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> [=====>----------------------------------] 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> [=====>----------------------------------] 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> [=====>----------------------------------] 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> [=====>----------------------------------] 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> [=====>----------------------------------] 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> [=====>----------------------------------] 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> [=====>----------------------------------] 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> [=====>----------------------------------] 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> [=====>----------------------------------] 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> [=====>----------------------------------] 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> [=====>----------------------------------] 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> [=====>----------------------------------] 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> [=====>----------------------------------] 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> [=====>----------------------------------] 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> [=====>----------------------------------] 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> [=====>----------------------------------] 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> [=====>----------------------------------] 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> [=====>----------------------------------] 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> [=====>----------------------------------] 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> [=====>----------------------------------] 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> [=====>----------------------------------] 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> [=====>----------------------------------] 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> [=====>----------------------------------] 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> [=====>----------------------------------] 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> [======>---------------------------------] 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> [======>---------------------------------] 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> [======>---------------------------------] 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> [======>---------------------------------] 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> [======>---------------------------------] 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> [======>---------------------------------] 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> [======>---------------------------------] 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> [======>---------------------------------] 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> [======>---------------------------------] 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> [======>---------------------------------] 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> [======>---------------------------------] 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> [======>---------------------------------] 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> [======>---------------------------------] 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> [======>---------------------------------] 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> [======>---------------------------------] 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> [======>---------------------------------] 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> [======>---------------------------------] 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> [======>---------------------------------] 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> [======>---------------------------------] 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> [======>---------------------------------] 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> [======>---------------------------------] 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> [======>---------------------------------] 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> [======>---------------------------------] 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> [======>---------------------------------] 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> [======>---------------------------------] 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> [=======>--------------------------------] 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> [=======>--------------------------------] 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> [=======>--------------------------------] 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> [=======>--------------------------------] 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> [=======>--------------------------------] 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> [=======>--------------------------------] 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> [=======>--------------------------------] 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> [=======>--------------------------------] 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> [=======>--------------------------------] 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> [=======>--------------------------------] 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> [=======>--------------------------------] 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> [=======>--------------------------------] 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> [=======>--------------------------------] 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> [=======>--------------------------------] 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> [=======>--------------------------------] 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> [=======>--------------------------------] 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> [=======>--------------------------------] 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> [=======>--------------------------------] 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> [=======>--------------------------------] 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> [=======>--------------------------------] 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> [=======>--------------------------------] 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> [=======>--------------------------------] 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> [=======>--------------------------------] 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> [=======>--------------------------------] 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> [=======>--------------------------------] 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> [========>-------------------------------] 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> [========>-------------------------------] 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> [========>-------------------------------] 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> [========>-------------------------------] 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> [========>-------------------------------] 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> [========>-------------------------------] 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> [========>-------------------------------] 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> [========>-------------------------------] 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> [========>-------------------------------] 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> [========>-------------------------------] 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> [========>-------------------------------] 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> [========>-------------------------------] 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> [========>-------------------------------] 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> [========>-------------------------------] 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> [========>-------------------------------] 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> [========>-------------------------------] 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> [========>-------------------------------] 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> [========>-------------------------------] 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> [========>-------------------------------] 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> [========>-------------------------------] 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> [========>-------------------------------] 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> [========>-------------------------------] 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> [========>-------------------------------] 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> [========>-------------------------------] 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> [========>-------------------------------] 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> [=========>------------------------------] 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> [=========>------------------------------] 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> [=========>------------------------------] 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> [=========>------------------------------] 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> [=========>------------------------------] 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> [=========>------------------------------] 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> [=========>------------------------------] 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> [=========>------------------------------] 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> [=========>------------------------------] 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> [=========>------------------------------] 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> [=========>------------------------------] 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> [=========>------------------------------] 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> [=========>------------------------------] 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> [=========>------------------------------] 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> [=========>------------------------------] 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> [=========>------------------------------] 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> [=========>------------------------------] 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> [=========>------------------------------] 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> [=========>------------------------------] 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> [=========>------------------------------] 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> [=========>------------------------------] 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> [=========>------------------------------] 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> [=========>------------------------------] 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> [=========>------------------------------] 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> [=========>------------------------------] 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> [==========>-----------------------------] 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> [==========>-----------------------------] 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> [==========>-----------------------------] 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> [==========>-----------------------------] 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> [==========>-----------------------------] 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> [==========>-----------------------------] 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> [==========>-----------------------------] 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> [==========>-----------------------------] 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> [==========>-----------------------------] 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> [==========>-----------------------------] 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> [==========>-----------------------------] 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> [==========>-----------------------------] 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> [==========>-----------------------------] 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> [==========>-----------------------------] 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> [==========>-----------------------------] 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> [==========>-----------------------------] 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> [==========>-----------------------------] 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> [==========>-----------------------------] 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> [==========>-----------------------------] 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> [==========>-----------------------------] 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> [==========>-----------------------------] 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> [==========>-----------------------------] 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> [==========>-----------------------------] 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> [==========>-----------------------------] 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> [==========>-----------------------------] 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> [===========>----------------------------] 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> [===========>----------------------------] 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> [===========>----------------------------] 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> [===========>----------------------------] 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> [===========>----------------------------] 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> [===========>----------------------------] 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> [===========>----------------------------] 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> [===========>----------------------------] 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> [===========>----------------------------] 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> [===========>----------------------------] 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> [===========>----------------------------] 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> [===========>----------------------------] 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> [===========>----------------------------] 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> [===========>----------------------------] 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> [===========>----------------------------] 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> [===========>----------------------------] 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> [===========>----------------------------] 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> [===========>----------------------------] 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> [===========>----------------------------] 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> [===========>----------------------------] 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> [===========>----------------------------] 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> [===========>----------------------------] 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> [===========>----------------------------] 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> [===========>----------------------------] 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> [===========>----------------------------] 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> [============>---------------------------] 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> [============>---------------------------] 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> [============>---------------------------] 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> [============>---------------------------] 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> [============>---------------------------] 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> [============>---------------------------] 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> [============>---------------------------] 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> [============>---------------------------] 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> [============>---------------------------] 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> [============>---------------------------] 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> [============>---------------------------] 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> [============>---------------------------] 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> [============>---------------------------] 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> [============>---------------------------] 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> [============>---------------------------] 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> [============>---------------------------] 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> [============>---------------------------] 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> [============>---------------------------] 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> [============>---------------------------] 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> [============>---------------------------] 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> [============>---------------------------] 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> [============>---------------------------] 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> [============>---------------------------] 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> [============>---------------------------] 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> [============>---------------------------] 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> [=============>--------------------------] 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> [=============>--------------------------] 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> [=============>--------------------------] 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> [=============>--------------------------] 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> [=============>--------------------------] 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> [=============>--------------------------] 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> [=============>--------------------------] 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> [=============>--------------------------] 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> [=============>--------------------------] 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> [=============>--------------------------] 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> [=============>--------------------------] 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> [=============>--------------------------] 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> [=============>--------------------------] 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> [=============>--------------------------] 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> [=============>--------------------------] 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> [=============>--------------------------] 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> [=============>--------------------------] 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> [=============>--------------------------] 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> [=============>--------------------------] 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> [=============>--------------------------] 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> [=============>--------------------------] 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> [=============>--------------------------] 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> [=============>--------------------------] 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> [=============>--------------------------] 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> [=============>--------------------------] 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> [==============>-------------------------] 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> [==============>-------------------------] 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> [==============>-------------------------] 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> [==============>-------------------------] 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> [==============>-------------------------] 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> [==============>-------------------------] 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> [==============>-------------------------] 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> [==============>-------------------------] 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> [==============>-------------------------] 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> [==============>-------------------------] 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> [==============>-------------------------] 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> [==============>-------------------------] 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> [==============>-------------------------] 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> [==============>-------------------------] 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> [==============>-------------------------] 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> [==============>-------------------------] 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> [==============>-------------------------] 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> [==============>-------------------------] 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> [==============>-------------------------] 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> [==============>-------------------------] 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> [==============>-------------------------] 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> [==============>-------------------------] 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> [==============>-------------------------] 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> [==============>-------------------------] 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> [==============>-------------------------] 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> [===============>------------------------] 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> [===============>------------------------] 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> [===============>------------------------] 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> [===============>------------------------] 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> [===============>------------------------] 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> [===============>------------------------] 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> [===============>------------------------] 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> [===============>------------------------] 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> [===============>------------------------] 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> [===============>------------------------] 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> [===============>------------------------] 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> [===============>------------------------] 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> [===============>------------------------] 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> [===============>------------------------] 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> [===============>------------------------] 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> [===============>------------------------] 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> [===============>------------------------] 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> [===============>------------------------] 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> [===============>------------------------] 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> [===============>------------------------] 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> [===============>------------------------] 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> [===============>------------------------] 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> [===============>------------------------] 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> [===============>------------------------] 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> [===============>------------------------] 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> [================>-----------------------] 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> [================>-----------------------] 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> [================>-----------------------] 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> [================>-----------------------] 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> [================>-----------------------] 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> [================>-----------------------] 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> [================>-----------------------] 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> [================>-----------------------] 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> [================>-----------------------] 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> [================>-----------------------] 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> [================>-----------------------] 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> [================>-----------------------] 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> [================>-----------------------] 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> [================>-----------------------] 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> [================>-----------------------] 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> [================>-----------------------] 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> [================>-----------------------] 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> [================>-----------------------] 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> [================>-----------------------] 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> [================>-----------------------] 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> [================>-----------------------] 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> [================>-----------------------] 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> [================>-----------------------] 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> [================>-----------------------] 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> [================>-----------------------] 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> [=================>----------------------] 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> [=================>----------------------] 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> [=================>----------------------] 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> [=================>----------------------] 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> [=================>----------------------] 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> [=================>----------------------] 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> [=================>----------------------] 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> [=================>----------------------] 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> [=================>----------------------] 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> [=================>----------------------] 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> [=================>----------------------] 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> [=================>----------------------] 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> [=================>----------------------] 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> [=================>----------------------] 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> [=================>----------------------] 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> [=================>----------------------] 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> [=================>----------------------] 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> [=================>----------------------] 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> [=================>----------------------] 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> [=================>----------------------] 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> [=================>----------------------] 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> [=================>----------------------] 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> [=================>----------------------] 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> [=================>----------------------] 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> [=================>----------------------] 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> [==================>---------------------] 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> [==================>---------------------] 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> [==================>---------------------] 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> [==================>---------------------] 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> [==================>---------------------] 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> [==================>---------------------] 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> [==================>---------------------] 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> [==================>---------------------] 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> [==================>---------------------] 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> [==================>---------------------] 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> [==================>---------------------] 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> [==================>---------------------] 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> [==================>---------------------] 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> [==================>---------------------] 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> [==================>---------------------] 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> [==================>---------------------] 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> [==================>---------------------] 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> [==================>---------------------] 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> [==================>---------------------] 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> [==================>---------------------] 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> [==================>---------------------] 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> [==================>---------------------] 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> [==================>---------------------] 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> [==================>---------------------] 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> [==================>---------------------] 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> [===================>--------------------] 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> [===================>--------------------] 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> [===================>--------------------] 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> [===================>--------------------] 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> [===================>--------------------] 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> [===================>--------------------] 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> [===================>--------------------] 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> [===================>--------------------] 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> [===================>--------------------] 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> [===================>--------------------] 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> [===================>--------------------] 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> [===================>--------------------] 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> [===================>--------------------] 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> [===================>--------------------] 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> [===================>--------------------] 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> [===================>--------------------] 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> [===================>--------------------] 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> [===================>--------------------] 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> [===================>--------------------] 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> [===================>--------------------] 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> [===================>--------------------] 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> [===================>--------------------] 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> [===================>--------------------] 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> [===================>--------------------] 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> [===================>--------------------] 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> [====================>-------------------] 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> [====================>-------------------] 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> [====================>-------------------] 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> [====================>-------------------] 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> [====================>-------------------] 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> [====================>-------------------] 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> [====================>-------------------] 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> [====================>-------------------] 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> [====================>-------------------] 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> [====================>-------------------] 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> [====================>-------------------] 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> [====================>-------------------] 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> [====================>-------------------] 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> [====================>-------------------] 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> [====================>-------------------] 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> [====================>-------------------] 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> [====================>-------------------] 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> [====================>-------------------] 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> [====================>-------------------] 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> [====================>-------------------] 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> [====================>-------------------] 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> [====================>-------------------] 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> [====================>-------------------] 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> [====================>-------------------] 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> [====================>-------------------] 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> [=====================>------------------] 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> [=====================>------------------] 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> [=====================>------------------] 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> [=====================>------------------] 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> [=====================>------------------] 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> [=====================>------------------] 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> [=====================>------------------] 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> [=====================>------------------] 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> [=====================>------------------] 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> [=====================>------------------] 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> [=====================>------------------] 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> [=====================>------------------] 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> [=====================>------------------] 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> [=====================>------------------] 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> [=====================>------------------] 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> [=====================>------------------] 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> [=====================>------------------] 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> [=====================>------------------] 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> [=====================>------------------] 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> [=====================>------------------] 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> [=====================>------------------] 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> [=====================>------------------] 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> [=====================>------------------] 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> [=====================>------------------] 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> [=====================>------------------] 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> [======================>-----------------] 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> [======================>-----------------] 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> [======================>-----------------] 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> [======================>-----------------] 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> [======================>-----------------] 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> [======================>-----------------] 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> [======================>-----------------] 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> [======================>-----------------] 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> [======================>-----------------] 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> [======================>-----------------] 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> [======================>-----------------] 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> [======================>-----------------] 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> [======================>-----------------] 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> [======================>-----------------] 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> [======================>-----------------] 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> [======================>-----------------] 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> [======================>-----------------] 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> [======================>-----------------] 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> [======================>-----------------] 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> [======================>-----------------] 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> [======================>-----------------] 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> [======================>-----------------] 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> [======================>-----------------] 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> [======================>-----------------] 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> [======================>-----------------] 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> [=======================>----------------] 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> [=======================>----------------] 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> [=======================>----------------] 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> [=======================>----------------] 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> [=======================>----------------] 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> [=======================>----------------] 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> [=======================>----------------] 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> [=======================>----------------] 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> [=======================>----------------] 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> [=======================>----------------] 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> [=======================>----------------] 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> [=======================>----------------] 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> [=======================>----------------] 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> [=======================>----------------] 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> [=======================>----------------] 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> [=======================>----------------] 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> [=======================>----------------] 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> [=======================>----------------] 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> [=======================>----------------] 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> [=======================>----------------] 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> [=======================>----------------] 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> [=======================>----------------] 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> [=======================>----------------] 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> [=======================>----------------] 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> [=======================>----------------] 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> [========================>---------------] 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> [========================>---------------] 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> [========================>---------------] 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> [========================>---------------] 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> [========================>---------------] 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> [========================>---------------] 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> [========================>---------------] 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> [========================>---------------] 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> [========================>---------------] 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> [========================>---------------] 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> [========================>---------------] 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> [========================>---------------] 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> [========================>---------------] 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> [========================>---------------] 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> [========================>---------------] 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> [========================>---------------] 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> [========================>---------------] 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> [========================>---------------] 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> [========================>---------------] 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> [========================>---------------] 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> [========================>---------------] 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> [========================>---------------] 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> [========================>---------------] 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> [========================>---------------] 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> [========================>---------------] 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> [=========================>--------------] 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> [=========================>--------------] 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> [=========================>--------------] 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> [=========================>--------------] 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> [=========================>--------------] 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> [=========================>--------------] 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> [=========================>--------------] 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> [=========================>--------------] 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> [=========================>--------------] 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> [=========================>--------------] 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> [=========================>--------------] 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> [=========================>--------------] 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> [=========================>--------------] 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> [=========================>--------------] 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> [=========================>--------------] 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> [=========================>--------------] 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> [=========================>--------------] 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> [=========================>--------------] 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> [=========================>--------------] 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> [=========================>--------------] 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> [=========================>--------------] 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> [=========================>--------------] 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> [=========================>--------------] 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> [=========================>--------------] 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> [=========================>--------------] 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> [==========================>-------------] 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> [==========================>-------------] 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> [==========================>-------------] 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> [==========================>-------------] 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> [==========================>-------------] 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> [==========================>-------------] 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> [==========================>-------------] 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> [==========================>-------------] 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> [==========================>-------------] 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> [==========================>-------------] 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> [==========================>-------------] 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> [==========================>-------------] 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> [==========================>-------------] 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> [==========================>-------------] 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> [==========================>-------------] 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> [==========================>-------------] 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> [==========================>-------------] 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> [==========================>-------------] 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> [==========================>-------------] 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> [==========================>-------------] 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> [==========================>-------------] 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> [==========================>-------------] 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> [==========================>-------------] 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> [==========================>-------------] 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> [==========================>-------------] 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> [===========================>------------] 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> [===========================>------------] 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> [===========================>------------] 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> [===========================>------------] 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> [===========================>------------] 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> [===========================>------------] 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> [===========================>------------] 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> [===========================>------------] 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> [===========================>------------] 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> [===========================>------------] 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> [===========================>------------] 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> [===========================>------------] 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> [===========================>------------] 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> [===========================>------------] 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> [===========================>------------] 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> [===========================>------------] 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> [===========================>------------] 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> [===========================>------------] 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> [===========================>------------] 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> [===========================>------------] 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> [===========================>------------] 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> [===========================>------------] 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> [===========================>------------] 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> [===========================>------------] 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> [===========================>------------] 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> [============================>-----------] 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> [============================>-----------] 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> [============================>-----------] 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> [============================>-----------] 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> [============================>-----------] 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> [============================>-----------] 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> [============================>-----------] 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> [============================>-----------] 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> [============================>-----------] 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> [============================>-----------] 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> [============================>-----------] 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> [============================>-----------] 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> [============================>-----------] 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> [============================>-----------] 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> [============================>-----------] 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> [============================>-----------] 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> [============================>-----------] 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> [============================>-----------] 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> [============================>-----------] 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> [============================>-----------] 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> [============================>-----------] 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> [============================>-----------] 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> [============================>-----------] 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> [============================>-----------] 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> [============================>-----------] 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> [=============================>----------] 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> [=============================>----------] 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> [=============================>----------] 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> [=============================>----------] 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> [=============================>----------] 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> [=============================>----------] 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> [=============================>----------] 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> [=============================>----------] 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> [=============================>----------] 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> [=============================>----------] 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> [=============================>----------] 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> [=============================>----------] 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> [=============================>----------] 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> [=============================>----------] 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> [=============================>----------] 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> [=============================>----------] 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> [=============================>----------] 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> [=============================>----------] 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> [=============================>----------] 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> [=============================>----------] 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> [=============================>----------] 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> [=============================>----------] 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> [=============================>----------] 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> [=============================>----------] 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> [=============================>----------] 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> [==============================>---------] 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> [==============================>---------] 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> [==============================>---------] 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> [==============================>---------] 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> [==============================>---------] 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> [==============================>---------] 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> [==============================>---------] 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> [==============================>---------] 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> [==============================>---------] 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> [==============================>---------] 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> [==============================>---------] 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> [==============================>---------] 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> [==============================>---------] 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> [==============================>---------] 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> [==============================>---------] 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> [==============================>---------] 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> [==============================>---------] 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> [==============================>---------] 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> [==============================>---------] 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> [==============================>---------] 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> [==============================>---------] 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> [==============================>---------] 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> [==============================>---------] 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> [==============================>---------] 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> [==============================>---------] 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> [===============================>--------] 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> [===============================>--------] 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> [===============================>--------] 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> [===============================>--------] 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> [===============================>--------] 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> [===============================>--------] 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> [===============================>--------] 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> [===============================>--------] 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> [===============================>--------] 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> [===============================>--------] 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> [===============================>--------] 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> [===============================>--------] 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> [===============================>--------] 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> [===============================>--------] 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> [===============================>--------] 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> [===============================>--------] 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> [===============================>--------] 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> [===============================>--------] 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> [===============================>--------] 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> [===============================>--------] 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> [===============================>--------] 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> [===============================>--------] 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> [===============================>--------] 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> [===============================>--------] 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> [===============================>--------] 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> [================================>-------] 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> [================================>-------] 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> [================================>-------] 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> [================================>-------] 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> [================================>-------] 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> [================================>-------] 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> [================================>-------] 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> [================================>-------] 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> [================================>-------] 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> [================================>-------] 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> [================================>-------] 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> [================================>-------] 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> [================================>-------] 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> [================================>-------] 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> [================================>-------] 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> [================================>-------] 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> [================================>-------] 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> [================================>-------] 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> [================================>-------] 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> [================================>-------] 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> [================================>-------] 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> [================================>-------] 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> [================================>-------] 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> [================================>-------] 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> [================================>-------] 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> [=================================>------] 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> [=================================>------] 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> [=================================>------] 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> [=================================>------] 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> [=================================>------] 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> [=================================>------] 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> [=================================>------] 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> [=================================>------] 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> [=================================>------] 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> [=================================>------] 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> [=================================>------] 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> [=================================>------] 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> [=================================>------] 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> [=================================>------] 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> [=================================>------] 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> [=================================>------] 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> [=================================>------] 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> [=================================>------] 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> [=================================>------] 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> [=================================>------] 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> [=================================>------] 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> [=================================>------] 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> [=================================>------] 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> [=================================>------] 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> [=================================>------] 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> [==================================>-----] 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> [==================================>-----] 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> [==================================>-----] 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> [==================================>-----] 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> [==================================>-----] 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> [==================================>-----] 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> [==================================>-----] 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> [==================================>-----] 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> [==================================>-----] 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> [==================================>-----] 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> [==================================>-----] 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> [==================================>-----] 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> [==================================>-----] 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> [==================================>-----] 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> [==================================>-----] 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> [==================================>-----] 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> [==================================>-----] 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> [==================================>-----] 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> [==================================>-----] 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> [==================================>-----] 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> [==================================>-----] 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> [==================================>-----] 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> [==================================>-----] 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> [==================================>-----] 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> [==================================>-----] 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> [===================================>----] 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> [===================================>----] 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> [===================================>----] 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> [===================================>----] 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> [===================================>----] 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> [===================================>----] 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> [===================================>----] 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> [===================================>----] 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> [===================================>----] 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> [===================================>----] 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> [===================================>----] 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> [===================================>----] 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> [===================================>----] 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> [===================================>----] 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> [===================================>----] 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> [===================================>----] 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> [===================================>----] 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> [===================================>----] 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> [===================================>----] 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> [===================================>----] 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> [===================================>----] 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> [===================================>----] 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> [===================================>----] 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> [===================================>----] 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> [===================================>----] 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> [====================================>---] 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> [====================================>---] 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> [====================================>---] 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> [====================================>---] 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> [====================================>---] 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> [====================================>---] 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> [====================================>---] 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> [====================================>---] 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> [====================================>---] 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> [====================================>---] 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> [====================================>---] 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> [====================================>---] 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> [====================================>---] 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> [====================================>---] 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> [====================================>---] 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> [====================================>---] 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> [====================================>---] 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> [====================================>---] 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> [====================================>---] 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> [====================================>---] 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> [====================================>---] 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> [====================================>---] 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> [====================================>---] 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> [====================================>---] 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> [====================================>---] 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> [=====================================>--] 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> [=====================================>--] 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> [=====================================>--] 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> [=====================================>--] 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> [=====================================>--] 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> [=====================================>--] 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> [=====================================>--] 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> [=====================================>--] 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> [=====================================>--] 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> [=====================================>--] 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> [=====================================>--] 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> [=====================================>--] 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> [=====================================>--] 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> [=====================================>--] 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> [=====================================>--] 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> [=====================================>--] 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> [=====================================>--] 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> [=====================================>--] 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> [=====================================>--] 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> [=====================================>--] 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> [=====================================>--] 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> [=====================================>--] 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> [=====================================>--] 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> [=====================================>--] 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> [=====================================>--] 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> [======================================>-] 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> [======================================>-] 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> [======================================>-] 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> [======================================>-] 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> [======================================>-] 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> [======================================>-] 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> [======================================>-] 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> [======================================>-] 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> [======================================>-] 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> [======================================>-] 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> [======================================>-] 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> [======================================>-] 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> [======================================>-] 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> [======================================>-] 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> [======================================>-] 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> [======================================>-] 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> [======================================>-] 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> [======================================>-] 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> [======================================>-] 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> [======================================>-] 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> [======================================>-] 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> [======================================>-] 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> [======================================>-] 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> [======================================>-] 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> [======================================>-] 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> [=======================================>] 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>
|
||
<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">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'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> [>---------------------------------------] 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> [>---------------------------------------] 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> [>---------------------------------------] 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> [>---------------------------------------] 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> [>---------------------------------------] 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> [>---------------------------------------] 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> [>---------------------------------------] 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> [>---------------------------------------] 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> [>---------------------------------------] 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> [>---------------------------------------] 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> [>---------------------------------------] 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> [>---------------------------------------] 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> [>---------------------------------------] 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> [>---------------------------------------] 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> [>---------------------------------------] 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> [>---------------------------------------] 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> [>---------------------------------------] 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> [>---------------------------------------] 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> [>---------------------------------------] 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> [>---------------------------------------] 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> [>---------------------------------------] 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> [>---------------------------------------] 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> [>---------------------------------------] 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> [>---------------------------------------] 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> [>---------------------------------------] 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> [=>--------------------------------------] 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> [=>--------------------------------------] 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> [=>--------------------------------------] 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> [=>--------------------------------------] 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> [=>--------------------------------------] 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> [=>--------------------------------------] 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> [=>--------------------------------------] 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> [=>--------------------------------------] 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> [=>--------------------------------------] 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> [=>--------------------------------------] 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> [=>--------------------------------------] 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> [=>--------------------------------------] 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> [=>--------------------------------------] 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> [=>--------------------------------------] 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> [=>--------------------------------------] 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> [=>--------------------------------------] 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> [=>--------------------------------------] 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> [=>--------------------------------------] 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> [=>--------------------------------------] 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> [=>--------------------------------------] 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> [=>--------------------------------------] 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> [=>--------------------------------------] 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> [=>--------------------------------------] 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> [=>--------------------------------------] 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> [=>--------------------------------------] 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> [==>-------------------------------------] 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> [==>-------------------------------------] 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> [==>-------------------------------------] 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> [==>-------------------------------------] 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> [==>-------------------------------------] 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> [==>-------------------------------------] 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> [==>-------------------------------------] 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> [==>-------------------------------------] 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> [==>-------------------------------------] 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> [==>-------------------------------------] 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> [==>-------------------------------------] 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> [==>-------------------------------------] 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> [==>-------------------------------------] 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> [==>-------------------------------------] 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> [==>-------------------------------------] 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> [==>-------------------------------------] 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> [==>-------------------------------------] 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> [==>-------------------------------------] 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> [==>-------------------------------------] 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> [==>-------------------------------------] 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> [==>-------------------------------------] 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> [==>-------------------------------------] 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> [==>-------------------------------------] 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> [==>-------------------------------------] 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> [==>-------------------------------------] 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> [===>------------------------------------] 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> [===>------------------------------------] 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> [===>------------------------------------] 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> [===>------------------------------------] 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> [===>------------------------------------] 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> [===>------------------------------------] 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> [===>------------------------------------] 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> [===>------------------------------------] 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> [===>------------------------------------] 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> [===>------------------------------------] 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> [===>------------------------------------] 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> [===>------------------------------------] 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> [===>------------------------------------] 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> [===>------------------------------------] 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> [===>------------------------------------] 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> [===>------------------------------------] 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> [===>------------------------------------] 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> [===>------------------------------------] 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> [===>------------------------------------] 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> [===>------------------------------------] 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> [===>------------------------------------] 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> [===>------------------------------------] 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> [===>------------------------------------] 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> [===>------------------------------------] 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> [===>------------------------------------] 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> [====>-----------------------------------] 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> [====>-----------------------------------] 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> [====>-----------------------------------] 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> [====>-----------------------------------] 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> [====>-----------------------------------] 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> [====>-----------------------------------] 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> [====>-----------------------------------] 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> [====>-----------------------------------] 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> [====>-----------------------------------] 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> [====>-----------------------------------] 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> [====>-----------------------------------] 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> [====>-----------------------------------] 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> [====>-----------------------------------] 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> [====>-----------------------------------] 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> [====>-----------------------------------] 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> [====>-----------------------------------] 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> [====>-----------------------------------] 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> [====>-----------------------------------] 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> [====>-----------------------------------] 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> [====>-----------------------------------] 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> [====>-----------------------------------] 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> [====>-----------------------------------] 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> [====>-----------------------------------] 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> [====>-----------------------------------] 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> [====>-----------------------------------] 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> [=====>----------------------------------] 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> [=====>----------------------------------] 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> [=====>----------------------------------] 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> [=====>----------------------------------] 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> [=====>----------------------------------] 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> [=====>----------------------------------] 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> [=====>----------------------------------] 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> [=====>----------------------------------] 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> [=====>----------------------------------] 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> [=====>----------------------------------] 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> [=====>----------------------------------] 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> [=====>----------------------------------] 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> [=====>----------------------------------] 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> [=====>----------------------------------] 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> [=====>----------------------------------] 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> [=====>----------------------------------] 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> [=====>----------------------------------] 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> [=====>----------------------------------] 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> [=====>----------------------------------] 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> [=====>----------------------------------] 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> [=====>----------------------------------] 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> [=====>----------------------------------] 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> [=====>----------------------------------] 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> [=====>----------------------------------] 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> [=====>----------------------------------] 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> [======>---------------------------------] 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> [======>---------------------------------] 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> [======>---------------------------------] 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> [======>---------------------------------] 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> [======>---------------------------------] 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> [======>---------------------------------] 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> [======>---------------------------------] 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> [======>---------------------------------] 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> [======>---------------------------------] 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> [======>---------------------------------] 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> [======>---------------------------------] 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> [======>---------------------------------] 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> [======>---------------------------------] 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> [======>---------------------------------] 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> [======>---------------------------------] 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> [======>---------------------------------] 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> [======>---------------------------------] 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> [======>---------------------------------] 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> [======>---------------------------------] 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> [======>---------------------------------] 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> [======>---------------------------------] 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> [======>---------------------------------] 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> [======>---------------------------------] 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> [======>---------------------------------] 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> [======>---------------------------------] 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> [=======>--------------------------------] 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> [=======>--------------------------------] 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> [=======>--------------------------------] 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> [=======>--------------------------------] 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> [=======>--------------------------------] 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> [=======>--------------------------------] 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> [=======>--------------------------------] 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> [=======>--------------------------------] 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> [=======>--------------------------------] 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> [=======>--------------------------------] 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> [=======>--------------------------------] 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> [=======>--------------------------------] 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> [=======>--------------------------------] 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> [=======>--------------------------------] 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> [=======>--------------------------------] 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> [=======>--------------------------------] 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> [=======>--------------------------------] 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> [=======>--------------------------------] 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> [=======>--------------------------------] 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> [=======>--------------------------------] 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> [=======>--------------------------------] 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> [=======>--------------------------------] 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> [=======>--------------------------------] 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> [=======>--------------------------------] 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> [=======>--------------------------------] 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> [========>-------------------------------] 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> [========>-------------------------------] 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> [========>-------------------------------] 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> [========>-------------------------------] 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> [========>-------------------------------] 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> [========>-------------------------------] 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> [========>-------------------------------] 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> [========>-------------------------------] 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> [========>-------------------------------] 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> [========>-------------------------------] 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> [========>-------------------------------] 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> [========>-------------------------------] 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> [========>-------------------------------] 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> [========>-------------------------------] 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> [========>-------------------------------] 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> [========>-------------------------------] 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> [========>-------------------------------] 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> [========>-------------------------------] 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> [========>-------------------------------] 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> [========>-------------------------------] 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> [========>-------------------------------] 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> [========>-------------------------------] 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> [========>-------------------------------] 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> [========>-------------------------------] 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> [========>-------------------------------] 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> [=========>------------------------------] 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> [=========>------------------------------] 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> [=========>------------------------------] 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> [=========>------------------------------] 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> [=========>------------------------------] 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> [=========>------------------------------] 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> [=========>------------------------------] 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> [=========>------------------------------] 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> [=========>------------------------------] 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> [=========>------------------------------] 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> [=========>------------------------------] 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> [=========>------------------------------] 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> [=========>------------------------------] 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> [=========>------------------------------] 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> [=========>------------------------------] 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> [=========>------------------------------] 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> [=========>------------------------------] 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> [=========>------------------------------] 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> [=========>------------------------------] 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> [=========>------------------------------] 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> [=========>------------------------------] 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> [=========>------------------------------] 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> [=========>------------------------------] 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> [=========>------------------------------] 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> [=========>------------------------------] 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> [==========>-----------------------------] 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> [==========>-----------------------------] 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> [==========>-----------------------------] 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> [==========>-----------------------------] 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> [==========>-----------------------------] 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> [==========>-----------------------------] 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> [==========>-----------------------------] 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> [==========>-----------------------------] 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> [==========>-----------------------------] 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> [==========>-----------------------------] 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> [==========>-----------------------------] 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> [==========>-----------------------------] 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> [==========>-----------------------------] 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> [==========>-----------------------------] 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> [==========>-----------------------------] 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> [==========>-----------------------------] 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> [==========>-----------------------------] 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> [==========>-----------------------------] 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> [==========>-----------------------------] 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> [==========>-----------------------------] 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> [==========>-----------------------------] 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> [==========>-----------------------------] 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> [==========>-----------------------------] 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> [==========>-----------------------------] 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> [==========>-----------------------------] 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> [===========>----------------------------] 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> [===========>----------------------------] 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> [===========>----------------------------] 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> [===========>----------------------------] 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> [===========>----------------------------] 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> [===========>----------------------------] 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> [===========>----------------------------] 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> [===========>----------------------------] 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> [===========>----------------------------] 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> [===========>----------------------------] 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> [===========>----------------------------] 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> [===========>----------------------------] 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> [===========>----------------------------] 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> [===========>----------------------------] 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> [===========>----------------------------] 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> [===========>----------------------------] 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> [===========>----------------------------] 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> [===========>----------------------------] 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> [===========>----------------------------] 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> [===========>----------------------------] 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> [===========>----------------------------] 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> [===========>----------------------------] 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> [===========>----------------------------] 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> [===========>----------------------------] 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> [===========>----------------------------] 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> [============>---------------------------] 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> [============>---------------------------] 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> [============>---------------------------] 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> [============>---------------------------] 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> [============>---------------------------] 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> [============>---------------------------] 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> [============>---------------------------] 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> [============>---------------------------] 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> [============>---------------------------] 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> [============>---------------------------] 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> [============>---------------------------] 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> [============>---------------------------] 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> [============>---------------------------] 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> [============>---------------------------] 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> [============>---------------------------] 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> [============>---------------------------] 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> [============>---------------------------] 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> [============>---------------------------] 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> [============>---------------------------] 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> [============>---------------------------] 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> [============>---------------------------] 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> [============>---------------------------] 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> [============>---------------------------] 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> [============>---------------------------] 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> [============>---------------------------] 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> [=============>--------------------------] 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> [=============>--------------------------] 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> [=============>--------------------------] 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> [=============>--------------------------] 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> [=============>--------------------------] 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> [=============>--------------------------] 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> [=============>--------------------------] 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> [=============>--------------------------] 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> [=============>--------------------------] 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> [=============>--------------------------] 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> [=============>--------------------------] 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> [=============>--------------------------] 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> [=============>--------------------------] 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> [=============>--------------------------] 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> [=============>--------------------------] 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> [=============>--------------------------] 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> [=============>--------------------------] 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> [=============>--------------------------] 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> [=============>--------------------------] 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> [=============>--------------------------] 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> [=============>--------------------------] 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> [=============>--------------------------] 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> [=============>--------------------------] 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> [=============>--------------------------] 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> [=============>--------------------------] 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> [==============>-------------------------] 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> [==============>-------------------------] 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> [==============>-------------------------] 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> [==============>-------------------------] 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> [==============>-------------------------] 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> [==============>-------------------------] 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> [==============>-------------------------] 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> [==============>-------------------------] 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> [==============>-------------------------] 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> [==============>-------------------------] 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> [==============>-------------------------] 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> [==============>-------------------------] 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> [==============>-------------------------] 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> [==============>-------------------------] 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> [==============>-------------------------] 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> [==============>-------------------------] 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> [==============>-------------------------] 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> [==============>-------------------------] 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> [==============>-------------------------] 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> [==============>-------------------------] 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> [==============>-------------------------] 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> [==============>-------------------------] 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> [==============>-------------------------] 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> [==============>-------------------------] 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> [==============>-------------------------] 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> [===============>------------------------] 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> [===============>------------------------] 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> [===============>------------------------] 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> [===============>------------------------] 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> [===============>------------------------] 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> [===============>------------------------] 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> [===============>------------------------] 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> [===============>------------------------] 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> [===============>------------------------] 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> [===============>------------------------] 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> [===============>------------------------] 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> [===============>------------------------] 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> [===============>------------------------] 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> [===============>------------------------] 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> [===============>------------------------] 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> [===============>------------------------] 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> [===============>------------------------] 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> [===============>------------------------] 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> [===============>------------------------] 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> [===============>------------------------] 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> [===============>------------------------] 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> [===============>------------------------] 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> [===============>------------------------] 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> [===============>------------------------] 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> [===============>------------------------] 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> [================>-----------------------] 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> [================>-----------------------] 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> [================>-----------------------] 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> [================>-----------------------] 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> [================>-----------------------] 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> [================>-----------------------] 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> [================>-----------------------] 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> [================>-----------------------] 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> [================>-----------------------] 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> [================>-----------------------] 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> [================>-----------------------] 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> [================>-----------------------] 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> [================>-----------------------] 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> [================>-----------------------] 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> [================>-----------------------] 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> [================>-----------------------] 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> [================>-----------------------] 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> [================>-----------------------] 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> [================>-----------------------] 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> [================>-----------------------] 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> [================>-----------------------] 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> [================>-----------------------] 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> [================>-----------------------] 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> [================>-----------------------] 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> [================>-----------------------] 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> [=================>----------------------] 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> [=================>----------------------] 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> [=================>----------------------] 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> [=================>----------------------] 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> [=================>----------------------] 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> [=================>----------------------] 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> [=================>----------------------] 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> [=================>----------------------] 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> [=================>----------------------] 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> [=================>----------------------] 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> [=================>----------------------] 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> [=================>----------------------] 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> [=================>----------------------] 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> [=================>----------------------] 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> [=================>----------------------] 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> [=================>----------------------] 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> [=================>----------------------] 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> [=================>----------------------] 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> [=================>----------------------] 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> [=================>----------------------] 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> [=================>----------------------] 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> [=================>----------------------] 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> [=================>----------------------] 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> [=================>----------------------] 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> [=================>----------------------] 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> [==================>---------------------] 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> [==================>---------------------] 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> [==================>---------------------] 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> [==================>---------------------] 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> [==================>---------------------] 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> [==================>---------------------] 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> [==================>---------------------] 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> [==================>---------------------] 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> [==================>---------------------] 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> [==================>---------------------] 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> [==================>---------------------] 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> [==================>---------------------] 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> [==================>---------------------] 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> [==================>---------------------] 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> [==================>---------------------] 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> [==================>---------------------] 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> [==================>---------------------] 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> [==================>---------------------] 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> [==================>---------------------] 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> [==================>---------------------] 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> [==================>---------------------] 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> [==================>---------------------] 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> [==================>---------------------] 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> [==================>---------------------] 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> [==================>---------------------] 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> [===================>--------------------] 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> [===================>--------------------] 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> [===================>--------------------] 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> [===================>--------------------] 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> [===================>--------------------] 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> [===================>--------------------] 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> [===================>--------------------] 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> [===================>--------------------] 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> [===================>--------------------] 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> [===================>--------------------] 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> [===================>--------------------] 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> [===================>--------------------] 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> [===================>--------------------] 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> [===================>--------------------] 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> [===================>--------------------] 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> [===================>--------------------] 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> [===================>--------------------] 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> [===================>--------------------] 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> [===================>--------------------] 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> [===================>--------------------] 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> [===================>--------------------] 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> [===================>--------------------] 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> [===================>--------------------] 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> [===================>--------------------] 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> [===================>--------------------] 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> [====================>-------------------] 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> [====================>-------------------] 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> [====================>-------------------] 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> [====================>-------------------] 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> [====================>-------------------] 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> [====================>-------------------] 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> [====================>-------------------] 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> [====================>-------------------] 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> [====================>-------------------] 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> [====================>-------------------] 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> [====================>-------------------] 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> [====================>-------------------] 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> [====================>-------------------] 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> [====================>-------------------] 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> [====================>-------------------] 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> [====================>-------------------] 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> [====================>-------------------] 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> [====================>-------------------] 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> [====================>-------------------] 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> [====================>-------------------] 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> [====================>-------------------] 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> [====================>-------------------] 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> [====================>-------------------] 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> [====================>-------------------] 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> [====================>-------------------] 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> [=====================>------------------] 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> [=====================>------------------] 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> [=====================>------------------] 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> [=====================>------------------] 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> [=====================>------------------] 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> [=====================>------------------] 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> [=====================>------------------] 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> [=====================>------------------] 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> [=====================>------------------] 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> [=====================>------------------] 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> [=====================>------------------] 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> [=====================>------------------] 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> [=====================>------------------] 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> [=====================>------------------] 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> [=====================>------------------] 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> [=====================>------------------] 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> [=====================>------------------] 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> [=====================>------------------] 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> [=====================>------------------] 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> [=====================>------------------] 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> [=====================>------------------] 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> [=====================>------------------] 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> [=====================>------------------] 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> [=====================>------------------] 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> [=====================>------------------] 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> [======================>-----------------] 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> [======================>-----------------] 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> [======================>-----------------] 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> [======================>-----------------] 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> [======================>-----------------] 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> [======================>-----------------] 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> [======================>-----------------] 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> [======================>-----------------] 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> [======================>-----------------] 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> [======================>-----------------] 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> [======================>-----------------] 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> [======================>-----------------] 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> [======================>-----------------] 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> [======================>-----------------] 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> [======================>-----------------] 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> [======================>-----------------] 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> [======================>-----------------] 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> [======================>-----------------] 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> [======================>-----------------] 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> [======================>-----------------] 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> [======================>-----------------] 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> [======================>-----------------] 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> [======================>-----------------] 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> [======================>-----------------] 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> [======================>-----------------] 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> [=======================>----------------] 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> [=======================>----------------] 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> [=======================>----------------] 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> [=======================>----------------] 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> [=======================>----------------] 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> [=======================>----------------] 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> [=======================>----------------] 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> [=======================>----------------] 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> [=======================>----------------] 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> [=======================>----------------] 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> [=======================>----------------] 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> [=======================>----------------] 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> [=======================>----------------] 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> [=======================>----------------] 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> [=======================>----------------] 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> [=======================>----------------] 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> [=======================>----------------] 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> [=======================>----------------] 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> [=======================>----------------] 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> [=======================>----------------] 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> [=======================>----------------] 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> [=======================>----------------] 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> [=======================>----------------] 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> [=======================>----------------] 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> [=======================>----------------] 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> [========================>---------------] 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> [========================>---------------] 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> [========================>---------------] 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> [========================>---------------] 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> [========================>---------------] 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> [========================>---------------] 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> [========================>---------------] 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> [========================>---------------] 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> [========================>---------------] 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> [========================>---------------] 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> [========================>---------------] 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> [========================>---------------] 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> [========================>---------------] 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> [========================>---------------] 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> [========================>---------------] 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> [========================>---------------] 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> [========================>---------------] 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> [========================>---------------] 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> [========================>---------------] 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> [========================>---------------] 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> [========================>---------------] 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> [========================>---------------] 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> [========================>---------------] 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> [========================>---------------] 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> [========================>---------------] 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> [=========================>--------------] 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> [=========================>--------------] 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> [=========================>--------------] 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> [=========================>--------------] 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> [=========================>--------------] 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> [=========================>--------------] 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> [=========================>--------------] 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> [=========================>--------------] 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> [=========================>--------------] 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> [=========================>--------------] 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> [=========================>--------------] 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> [=========================>--------------] 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> [=========================>--------------] 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> [=========================>--------------] 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> [=========================>--------------] 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> [=========================>--------------] 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> [=========================>--------------] 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> [=========================>--------------] 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> [=========================>--------------] 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> [=========================>--------------] 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> [=========================>--------------] 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> [=========================>--------------] 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> [=========================>--------------] 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> [=========================>--------------] 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> [=========================>--------------] 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> [==========================>-------------] 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> [==========================>-------------] 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> [==========================>-------------] 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> [==========================>-------------] 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> [==========================>-------------] 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> [==========================>-------------] 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> [==========================>-------------] 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> [==========================>-------------] 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> [==========================>-------------] 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> [==========================>-------------] 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> [==========================>-------------] 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> [==========================>-------------] 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> [==========================>-------------] 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> [==========================>-------------] 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> [==========================>-------------] 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> [==========================>-------------] 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> [==========================>-------------] 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> [==========================>-------------] 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> [==========================>-------------] 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> [==========================>-------------] 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> [==========================>-------------] 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> [==========================>-------------] 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> [==========================>-------------] 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> [==========================>-------------] 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> [==========================>-------------] 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> [===========================>------------] 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> [===========================>------------] 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> [===========================>------------] 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> [===========================>------------] 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> [===========================>------------] 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> [===========================>------------] 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> [===========================>------------] 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> [===========================>------------] 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> [===========================>------------] 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> [===========================>------------] 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> [===========================>------------] 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> [===========================>------------] 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> [===========================>------------] 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> [===========================>------------] 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> [===========================>------------] 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> [===========================>------------] 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> [===========================>------------] 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> [===========================>------------] 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> [===========================>------------] 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> [===========================>------------] 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> [===========================>------------] 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> [===========================>------------] 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> [===========================>------------] 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> [===========================>------------] 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> [===========================>------------] 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> [============================>-----------] 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> [============================>-----------] 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> [============================>-----------] 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> [============================>-----------] 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> [============================>-----------] 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> [============================>-----------] 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> [============================>-----------] 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> [============================>-----------] 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> [============================>-----------] 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> [============================>-----------] 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> [============================>-----------] 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> [============================>-----------] 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> [============================>-----------] 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> [============================>-----------] 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> [============================>-----------] 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> [============================>-----------] 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> [============================>-----------] 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> [============================>-----------] 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> [============================>-----------] 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> [============================>-----------] 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> [============================>-----------] 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> [============================>-----------] 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> [============================>-----------] 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> [============================>-----------] 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> [============================>-----------] 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> [=============================>----------] 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> [=============================>----------] 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> [=============================>----------] 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> [=============================>----------] 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> [=============================>----------] 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> [=============================>----------] 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> [=============================>----------] 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> [=============================>----------] 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> [=============================>----------] 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> [=============================>----------] 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> [=============================>----------] 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> [=============================>----------] 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> [=============================>----------] 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> [=============================>----------] 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> [=============================>----------] 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> [=============================>----------] 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> [=============================>----------] 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> [=============================>----------] 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> [=============================>----------] 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> [=============================>----------] 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> [=============================>----------] 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> [=============================>----------] 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> [=============================>----------] 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> [=============================>----------] 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> [=============================>----------] 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> [==============================>---------] 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> [==============================>---------] 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> [==============================>---------] 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> [==============================>---------] 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> [==============================>---------] 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> [==============================>---------] 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> [==============================>---------] 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> [==============================>---------] 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> [==============================>---------] 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> [==============================>---------] 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> [==============================>---------] 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> [==============================>---------] 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> [==============================>---------] 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> [==============================>---------] 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> [==============================>---------] 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> [==============================>---------] 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> [==============================>---------] 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> [==============================>---------] 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> [==============================>---------] 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> [==============================>---------] 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> [==============================>---------] 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> [==============================>---------] 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> [==============================>---------] 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> [==============================>---------] 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> [==============================>---------] 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> [===============================>--------] 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> [===============================>--------] 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> [===============================>--------] 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> [===============================>--------] 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> [===============================>--------] 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> [===============================>--------] 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> [===============================>--------] 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> [===============================>--------] 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> [===============================>--------] 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> [===============================>--------] 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> [===============================>--------] 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> [===============================>--------] 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> [===============================>--------] 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> [===============================>--------] 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> [===============================>--------] 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> [===============================>--------] 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> [===============================>--------] 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> [===============================>--------] 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> [===============================>--------] 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> [===============================>--------] 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> [===============================>--------] 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> [===============================>--------] 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> [===============================>--------] 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> [===============================>--------] 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> [===============================>--------] 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> [================================>-------] 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> [================================>-------] 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> [================================>-------] 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> [================================>-------] 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> [================================>-------] 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> [================================>-------] 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> [================================>-------] 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> [================================>-------] 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> [================================>-------] 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> [================================>-------] 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> [================================>-------] 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> [================================>-------] 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> [================================>-------] 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> [================================>-------] 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> [================================>-------] 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> [================================>-------] 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> [================================>-------] 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> [================================>-------] 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> [================================>-------] 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> [================================>-------] 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> [================================>-------] 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> [================================>-------] 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> [================================>-------] 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> [================================>-------] 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> [================================>-------] 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> [=================================>------] 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> [=================================>------] 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> [=================================>------] 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> [=================================>------] 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> [=================================>------] 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> [=================================>------] 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> [=================================>------] 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> [=================================>------] 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> [=================================>------] 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> [=================================>------] 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> [=================================>------] 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> [=================================>------] 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> [=================================>------] 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> [=================================>------] 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> [=================================>------] 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> [=================================>------] 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> [=================================>------] 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> [=================================>------] 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> [=================================>------] 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> [=================================>------] 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> [=================================>------] 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> [=================================>------] 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> [=================================>------] 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> [=================================>------] 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> [=================================>------] 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> [==================================>-----] 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> [==================================>-----] 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> [==================================>-----] 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> [==================================>-----] 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> [==================================>-----] 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> [==================================>-----] 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> [==================================>-----] 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> [==================================>-----] 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> [==================================>-----] 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> [==================================>-----] 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> [==================================>-----] 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> [==================================>-----] 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> [==================================>-----] 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> [==================================>-----] 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> [==================================>-----] 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> [==================================>-----] 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> [==================================>-----] 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> [==================================>-----] 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> [==================================>-----] 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> [==================================>-----] 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> [==================================>-----] 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> [==================================>-----] 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> [==================================>-----] 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> [==================================>-----] 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> [==================================>-----] 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> [===================================>----] 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> [===================================>----] 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> [===================================>----] 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> [===================================>----] 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> [===================================>----] 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> [===================================>----] 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> [===================================>----] 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> [===================================>----] 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> [===================================>----] 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> [===================================>----] 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> [===================================>----] 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> [===================================>----] 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> [===================================>----] 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> [===================================>----] 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> [===================================>----] 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> [===================================>----] 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> [===================================>----] 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> [===================================>----] 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> [===================================>----] 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> [===================================>----] 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> [===================================>----] 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> [===================================>----] 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> [===================================>----] 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> [===================================>----] 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> [===================================>----] 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> [====================================>---] 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> [====================================>---] 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> [====================================>---] 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> [====================================>---] 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> [====================================>---] 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> [====================================>---] 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> [====================================>---] 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> [====================================>---] 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> [====================================>---] 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> [====================================>---] 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> [====================================>---] 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> [====================================>---] 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> [====================================>---] 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> [====================================>---] 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> [====================================>---] 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> [====================================>---] 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> [====================================>---] 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> [====================================>---] 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> [====================================>---] 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> [====================================>---] 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> [====================================>---] 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> [====================================>---] 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> [====================================>---] 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> [====================================>---] 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> [====================================>---] 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> [=====================================>--] 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> [=====================================>--] 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> [=====================================>--] 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> [=====================================>--] 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> [=====================================>--] 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> [=====================================>--] 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> [=====================================>--] 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> [=====================================>--] 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> [=====================================>--] 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> [=====================================>--] 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> [=====================================>--] 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> [=====================================>--] 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> [=====================================>--] 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> [=====================================>--] 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> [=====================================>--] 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> [=====================================>--] 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> [=====================================>--] 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> [=====================================>--] 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> [=====================================>--] 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> [=====================================>--] 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> [=====================================>--] 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> [=====================================>--] 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> [=====================================>--] 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> [=====================================>--] 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> [=====================================>--] 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> [======================================>-] 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> [======================================>-] 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> [======================================>-] 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> [======================================>-] 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> [======================================>-] 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> [======================================>-] 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> [======================================>-] 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> [======================================>-] 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> [======================================>-] 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> [======================================>-] 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> [======================================>-] 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> [======================================>-] 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> [======================================>-] 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> [======================================>-] 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> [======================================>-] 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> [======================================>-] 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> [======================================>-] 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> [======================================>-] 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> [======================================>-] 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> [======================================>-] 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> [======================================>-] 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> [======================================>-] 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> [======================================>-] 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> [======================================>-] 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> [======================================>-] 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> [=======================================>] 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'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> [>---------------------------------------] 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> [>---------------------------------------] 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> [>---------------------------------------] 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> [>---------------------------------------] 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> [>---------------------------------------] 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> [>---------------------------------------] 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> [>---------------------------------------] 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> [>---------------------------------------] 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> [>---------------------------------------] 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> [>---------------------------------------] 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> [>---------------------------------------] 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> [>---------------------------------------] 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> [>---------------------------------------] 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> [>---------------------------------------] 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> [>---------------------------------------] 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> [>---------------------------------------] 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> [>---------------------------------------] 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> [>---------------------------------------] 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> [>---------------------------------------] 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> [>---------------------------------------] 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> [>---------------------------------------] 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> [>---------------------------------------] 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> [>---------------------------------------] 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> [>---------------------------------------] 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> [>---------------------------------------] 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> [=>--------------------------------------] 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> [=>--------------------------------------] 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> [=>--------------------------------------] 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> [=>--------------------------------------] 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> [=>--------------------------------------] 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> [=>--------------------------------------] 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> [=>--------------------------------------] 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> [=>--------------------------------------] 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> [=>--------------------------------------] 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> [=>--------------------------------------] 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> [=>--------------------------------------] 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> [=>--------------------------------------] 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> [=>--------------------------------------] 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> [=>--------------------------------------] 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> [=>--------------------------------------] 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> [=>--------------------------------------] 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> [=>--------------------------------------] 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> [=>--------------------------------------] 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> [=>--------------------------------------] 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> [=>--------------------------------------] 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> [=>--------------------------------------] 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> [=>--------------------------------------] 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> [=>--------------------------------------] 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> [=>--------------------------------------] 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> [=>--------------------------------------] 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> [==>-------------------------------------] 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> [==>-------------------------------------] 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> [==>-------------------------------------] 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> [==>-------------------------------------] 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> [==>-------------------------------------] 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> [==>-------------------------------------] 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> [==>-------------------------------------] 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> [==>-------------------------------------] 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> [==>-------------------------------------] 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> [==>-------------------------------------] 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> [==>-------------------------------------] 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> [==>-------------------------------------] 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> [==>-------------------------------------] 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> [==>-------------------------------------] 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> [==>-------------------------------------] 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> [==>-------------------------------------] 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> [==>-------------------------------------] 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> [==>-------------------------------------] 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> [==>-------------------------------------] 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> [==>-------------------------------------] 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> [==>-------------------------------------] 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> [==>-------------------------------------] 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> [==>-------------------------------------] 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> [==>-------------------------------------] 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> [==>-------------------------------------] 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> [===>------------------------------------] 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> [===>------------------------------------] 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> [===>------------------------------------] 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> [===>------------------------------------] 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> [===>------------------------------------] 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> [===>------------------------------------] 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> [===>------------------------------------] 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> [===>------------------------------------] 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> [===>------------------------------------] 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> [===>------------------------------------] 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> [===>------------------------------------] 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> [===>------------------------------------] 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> [===>------------------------------------] 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> [===>------------------------------------] 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> [===>------------------------------------] 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> [===>------------------------------------] 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> [===>------------------------------------] 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> [===>------------------------------------] 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> [===>------------------------------------] 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> [===>------------------------------------] 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> [===>------------------------------------] 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> [===>------------------------------------] 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> [===>------------------------------------] 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> [===>------------------------------------] 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> [===>------------------------------------] 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> [====>-----------------------------------] 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> [====>-----------------------------------] 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> [====>-----------------------------------] 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> [====>-----------------------------------] 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> [====>-----------------------------------] 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> [====>-----------------------------------] 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> [====>-----------------------------------] 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> [====>-----------------------------------] 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> [====>-----------------------------------] 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> [====>-----------------------------------] 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> [====>-----------------------------------] 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> [====>-----------------------------------] 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> [====>-----------------------------------] 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> [====>-----------------------------------] 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> [====>-----------------------------------] 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> [====>-----------------------------------] 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> [====>-----------------------------------] 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> [====>-----------------------------------] 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> [====>-----------------------------------] 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> [====>-----------------------------------] 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> [====>-----------------------------------] 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> [====>-----------------------------------] 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> [====>-----------------------------------] 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> [====>-----------------------------------] 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> [====>-----------------------------------] 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> [=====>----------------------------------] 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> [=====>----------------------------------] 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> [=====>----------------------------------] 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> [=====>----------------------------------] 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> [=====>----------------------------------] 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> [=====>----------------------------------] 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> [=====>----------------------------------] 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> [=====>----------------------------------] 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> [=====>----------------------------------] 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> [=====>----------------------------------] 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> [=====>----------------------------------] 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> [=====>----------------------------------] 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> [=====>----------------------------------] 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> [=====>----------------------------------] 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> [=====>----------------------------------] 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> [=====>----------------------------------] 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> [=====>----------------------------------] 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> [=====>----------------------------------] 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> [=====>----------------------------------] 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> [=====>----------------------------------] 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> [=====>----------------------------------] 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> [=====>----------------------------------] 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> [=====>----------------------------------] 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> [=====>----------------------------------] 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> [=====>----------------------------------] 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> [======>---------------------------------] 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> [======>---------------------------------] 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> [======>---------------------------------] 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> [======>---------------------------------] 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> [======>---------------------------------] 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> [======>---------------------------------] 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> [======>---------------------------------] 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> [======>---------------------------------] 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> [======>---------------------------------] 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> [======>---------------------------------] 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> [======>---------------------------------] 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> [======>---------------------------------] 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> [======>---------------------------------] 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> [======>---------------------------------] 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> [======>---------------------------------] 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> [======>---------------------------------] 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> [======>---------------------------------] 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> [======>---------------------------------] 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> [======>---------------------------------] 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> [======>---------------------------------] 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> [======>---------------------------------] 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> [======>---------------------------------] 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> [======>---------------------------------] 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> [======>---------------------------------] 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> [======>---------------------------------] 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> [=======>--------------------------------] 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> [=======>--------------------------------] 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> [=======>--------------------------------] 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> [=======>--------------------------------] 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> [=======>--------------------------------] 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> [=======>--------------------------------] 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> [=======>--------------------------------] 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> [=======>--------------------------------] 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> [=======>--------------------------------] 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> [=======>--------------------------------] 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> [=======>--------------------------------] 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> [=======>--------------------------------] 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> [=======>--------------------------------] 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> [=======>--------------------------------] 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> [=======>--------------------------------] 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> [=======>--------------------------------] 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> [=======>--------------------------------] 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> [=======>--------------------------------] 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> [=======>--------------------------------] 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> [=======>--------------------------------] 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> [=======>--------------------------------] 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> [=======>--------------------------------] 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> [=======>--------------------------------] 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> [=======>--------------------------------] 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> [=======>--------------------------------] 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> [========>-------------------------------] 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> [========>-------------------------------] 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> [========>-------------------------------] 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> [========>-------------------------------] 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> [========>-------------------------------] 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> [========>-------------------------------] 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> [========>-------------------------------] 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> [========>-------------------------------] 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> [========>-------------------------------] 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> [========>-------------------------------] 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> [========>-------------------------------] 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> [========>-------------------------------] 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> [========>-------------------------------] 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> [========>-------------------------------] 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> [========>-------------------------------] 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> [========>-------------------------------] 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> [========>-------------------------------] 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> [========>-------------------------------] 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> [========>-------------------------------] 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> [========>-------------------------------] 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> [========>-------------------------------] 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> [========>-------------------------------] 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> [========>-------------------------------] 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> [========>-------------------------------] 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> [========>-------------------------------] 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> [=========>------------------------------] 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> [=========>------------------------------] 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> [=========>------------------------------] 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> [=========>------------------------------] 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> [=========>------------------------------] 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> [=========>------------------------------] 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> [=========>------------------------------] 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> [=========>------------------------------] 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> [=========>------------------------------] 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> [=========>------------------------------] 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> [=========>------------------------------] 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> [=========>------------------------------] 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> [=========>------------------------------] 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> [=========>------------------------------] 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> [=========>------------------------------] 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> [=========>------------------------------] 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> [=========>------------------------------] 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> [=========>------------------------------] 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> [=========>------------------------------] 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> [=========>------------------------------] 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> [=========>------------------------------] 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> [=========>------------------------------] 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> [=========>------------------------------] 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> [=========>------------------------------] 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> [=========>------------------------------] 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> [==========>-----------------------------] 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> [==========>-----------------------------] 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> [==========>-----------------------------] 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> [==========>-----------------------------] 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> [==========>-----------------------------] 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> [==========>-----------------------------] 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> [==========>-----------------------------] 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> [==========>-----------------------------] 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> [==========>-----------------------------] 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> [==========>-----------------------------] 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> [==========>-----------------------------] 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> [==========>-----------------------------] 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> [==========>-----------------------------] 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> [==========>-----------------------------] 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> [==========>-----------------------------] 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> [==========>-----------------------------] 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> [==========>-----------------------------] 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> [==========>-----------------------------] 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> [==========>-----------------------------] 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> [==========>-----------------------------] 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> [==========>-----------------------------] 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> [==========>-----------------------------] 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> [==========>-----------------------------] 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> [==========>-----------------------------] 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> [==========>-----------------------------] 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> [===========>----------------------------] 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> [===========>----------------------------] 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> [===========>----------------------------] 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> [===========>----------------------------] 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> [===========>----------------------------] 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> [===========>----------------------------] 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> [===========>----------------------------] 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> [===========>----------------------------] 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> [===========>----------------------------] 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> [===========>----------------------------] 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> [===========>----------------------------] 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> [===========>----------------------------] 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> [===========>----------------------------] 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> [===========>----------------------------] 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> [===========>----------------------------] 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> [===========>----------------------------] 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> [===========>----------------------------] 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> [===========>----------------------------] 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> [===========>----------------------------] 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> [===========>----------------------------] 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> [===========>----------------------------] 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> [===========>----------------------------] 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> [===========>----------------------------] 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> [===========>----------------------------] 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> [===========>----------------------------] 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> [============>---------------------------] 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> [============>---------------------------] 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> [============>---------------------------] 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> [============>---------------------------] 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> [============>---------------------------] 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> [============>---------------------------] 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> [============>---------------------------] 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> [============>---------------------------] 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> [============>---------------------------] 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> [============>---------------------------] 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> [============>---------------------------] 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> [============>---------------------------] 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> [============>---------------------------] 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> [============>---------------------------] 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> [============>---------------------------] 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> [============>---------------------------] 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> [============>---------------------------] 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> [============>---------------------------] 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> [============>---------------------------] 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> [============>---------------------------] 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> [============>---------------------------] 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> [============>---------------------------] 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> [============>---------------------------] 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> [============>---------------------------] 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> [============>---------------------------] 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> [=============>--------------------------] 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> [=============>--------------------------] 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> [=============>--------------------------] 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> [=============>--------------------------] 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> [=============>--------------------------] 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> [=============>--------------------------] 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> [=============>--------------------------] 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> [=============>--------------------------] 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> [=============>--------------------------] 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> [=============>--------------------------] 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> [=============>--------------------------] 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> [=============>--------------------------] 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> [=============>--------------------------] 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> [=============>--------------------------] 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> [=============>--------------------------] 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> [=============>--------------------------] 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> [=============>--------------------------] 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> [=============>--------------------------] 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> [=============>--------------------------] 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> [=============>--------------------------] 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> [=============>--------------------------] 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> [=============>--------------------------] 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> [=============>--------------------------] 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> [=============>--------------------------] 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> [=============>--------------------------] 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> [==============>-------------------------] 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> [==============>-------------------------] 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> [==============>-------------------------] 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> [==============>-------------------------] 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> [==============>-------------------------] 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> [==============>-------------------------] 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> [==============>-------------------------] 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> [==============>-------------------------] 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> [==============>-------------------------] 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> [==============>-------------------------] 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> [==============>-------------------------] 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> [==============>-------------------------] 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> [==============>-------------------------] 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> [==============>-------------------------] 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> [==============>-------------------------] 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> [==============>-------------------------] 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> [==============>-------------------------] 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> [==============>-------------------------] 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> [==============>-------------------------] 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> [==============>-------------------------] 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> [==============>-------------------------] 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> [==============>-------------------------] 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> [==============>-------------------------] 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> [==============>-------------------------] 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> [==============>-------------------------] 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> [===============>------------------------] 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> [===============>------------------------] 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> [===============>------------------------] 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> [===============>------------------------] 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> [===============>------------------------] 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> [===============>------------------------] 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> [===============>------------------------] 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> [===============>------------------------] 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> [===============>------------------------] 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> [===============>------------------------] 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> [===============>------------------------] 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> [===============>------------------------] 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> [===============>------------------------] 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> [===============>------------------------] 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> [===============>------------------------] 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> [===============>------------------------] 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> [===============>------------------------] 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> [===============>------------------------] 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> [===============>------------------------] 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> [===============>------------------------] 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> [===============>------------------------] 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> [===============>------------------------] 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> [===============>------------------------] 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> [===============>------------------------] 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> [===============>------------------------] 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> [================>-----------------------] 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> [================>-----------------------] 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> [================>-----------------------] 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> [================>-----------------------] 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> [================>-----------------------] 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> [================>-----------------------] 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> [================>-----------------------] 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> [================>-----------------------] 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> [================>-----------------------] 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> [================>-----------------------] 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> [================>-----------------------] 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> [================>-----------------------] 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> [================>-----------------------] 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> [================>-----------------------] 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> [================>-----------------------] 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> [================>-----------------------] 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> [================>-----------------------] 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> [================>-----------------------] 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> [================>-----------------------] 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> [================>-----------------------] 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> [================>-----------------------] 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> [================>-----------------------] 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> [================>-----------------------] 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> [================>-----------------------] 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> [================>-----------------------] 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> [=================>----------------------] 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> [=================>----------------------] 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> [=================>----------------------] 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> [=================>----------------------] 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> [=================>----------------------] 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> [=================>----------------------] 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> [=================>----------------------] 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> [=================>----------------------] 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> [=================>----------------------] 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> [=================>----------------------] 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> [=================>----------------------] 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> [=================>----------------------] 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> [=================>----------------------] 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> [=================>----------------------] 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> [=================>----------------------] 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> [=================>----------------------] 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> [=================>----------------------] 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> [=================>----------------------] 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> [=================>----------------------] 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> [=================>----------------------] 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> [=================>----------------------] 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> [=================>----------------------] 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> [=================>----------------------] 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> [=================>----------------------] 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> [=================>----------------------] 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> [==================>---------------------] 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> [==================>---------------------] 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> [==================>---------------------] 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> [==================>---------------------] 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> [==================>---------------------] 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> [==================>---------------------] 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> [==================>---------------------] 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> [==================>---------------------] 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> [==================>---------------------] 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> [==================>---------------------] 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> [==================>---------------------] 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> [==================>---------------------] 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> [==================>---------------------] 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> [==================>---------------------] 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> [==================>---------------------] 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> [==================>---------------------] 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> [==================>---------------------] 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> [==================>---------------------] 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> [==================>---------------------] 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> [==================>---------------------] 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> [==================>---------------------] 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> [==================>---------------------] 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> [==================>---------------------] 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> [==================>---------------------] 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> [==================>---------------------] 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> [===================>--------------------] 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> [===================>--------------------] 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> [===================>--------------------] 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> [===================>--------------------] 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> [===================>--------------------] 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> [===================>--------------------] 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> [===================>--------------------] 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> [===================>--------------------] 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> [===================>--------------------] 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> [===================>--------------------] 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> [===================>--------------------] 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> [===================>--------------------] 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> [===================>--------------------] 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> [===================>--------------------] 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> [===================>--------------------] 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> [===================>--------------------] 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> [===================>--------------------] 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> [===================>--------------------] 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> [===================>--------------------] 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> [===================>--------------------] 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> [===================>--------------------] 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> [===================>--------------------] 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> [===================>--------------------] 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> [===================>--------------------] 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> [===================>--------------------] 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> [====================>-------------------] 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> [====================>-------------------] 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> [====================>-------------------] 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> [====================>-------------------] 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> [====================>-------------------] 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> [====================>-------------------] 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> [====================>-------------------] 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> [====================>-------------------] 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> [====================>-------------------] 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> [====================>-------------------] 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> [====================>-------------------] 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> [====================>-------------------] 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> [====================>-------------------] 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> [====================>-------------------] 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> [====================>-------------------] 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> [====================>-------------------] 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> [====================>-------------------] 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> [====================>-------------------] 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> [====================>-------------------] 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> [====================>-------------------] 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> [====================>-------------------] 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> [====================>-------------------] 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> [====================>-------------------] 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> [====================>-------------------] 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> [====================>-------------------] 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> [=====================>------------------] 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> [=====================>------------------] 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> [=====================>------------------] 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> [=====================>------------------] 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> [=====================>------------------] 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> [=====================>------------------] 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> [=====================>------------------] 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> [=====================>------------------] 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> [=====================>------------------] 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> [=====================>------------------] 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> [=====================>------------------] 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> [=====================>------------------] 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> [=====================>------------------] 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> [=====================>------------------] 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> [=====================>------------------] 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> [=====================>------------------] 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> [=====================>------------------] 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> [=====================>------------------] 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> [=====================>------------------] 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> [=====================>------------------] 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> [=====================>------------------] 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> [=====================>------------------] 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> [=====================>------------------] 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> [=====================>------------------] 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> [=====================>------------------] 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> [======================>-----------------] 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> [======================>-----------------] 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> [======================>-----------------] 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> [======================>-----------------] 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> [======================>-----------------] 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> [======================>-----------------] 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> [======================>-----------------] 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> [======================>-----------------] 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> [======================>-----------------] 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> [======================>-----------------] 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> [======================>-----------------] 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> [======================>-----------------] 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> [======================>-----------------] 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> [======================>-----------------] 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> [======================>-----------------] 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> [======================>-----------------] 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> [======================>-----------------] 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> [======================>-----------------] 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> [======================>-----------------] 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> [======================>-----------------] 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> [======================>-----------------] 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> [======================>-----------------] 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> [======================>-----------------] 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> [======================>-----------------] 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> [======================>-----------------] 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> [=======================>----------------] 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> [=======================>----------------] 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> [=======================>----------------] 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> [=======================>----------------] 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> [=======================>----------------] 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> [=======================>----------------] 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> [=======================>----------------] 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> [=======================>----------------] 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> [=======================>----------------] 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> [=======================>----------------] 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> [=======================>----------------] 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> [=======================>----------------] 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> [=======================>----------------] 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> [=======================>----------------] 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> [=======================>----------------] 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> [=======================>----------------] 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> [=======================>----------------] 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> [=======================>----------------] 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> [=======================>----------------] 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> [=======================>----------------] 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> [=======================>----------------] 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> [=======================>----------------] 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> [=======================>----------------] 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> [=======================>----------------] 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> [=======================>----------------] 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> [========================>---------------] 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> [========================>---------------] 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> [========================>---------------] 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> [========================>---------------] 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> [========================>---------------] 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> [========================>---------------] 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> [========================>---------------] 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> [========================>---------------] 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> [========================>---------------] 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> [========================>---------------] 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> [========================>---------------] 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> [========================>---------------] 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> [========================>---------------] 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> [========================>---------------] 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> [========================>---------------] 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> [========================>---------------] 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> [========================>---------------] 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> [========================>---------------] 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> [========================>---------------] 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> [========================>---------------] 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> [========================>---------------] 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> [========================>---------------] 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> [========================>---------------] 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> [========================>---------------] 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> [========================>---------------] 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> [=========================>--------------] 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> [=========================>--------------] 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> [=========================>--------------] 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> [=========================>--------------] 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> [=========================>--------------] 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> [=========================>--------------] 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> [=========================>--------------] 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> [=========================>--------------] 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> [=========================>--------------] 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> [=========================>--------------] 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> [=========================>--------------] 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> [=========================>--------------] 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> [=========================>--------------] 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> [=========================>--------------] 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> [=========================>--------------] 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> [=========================>--------------] 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> [=========================>--------------] 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> [=========================>--------------] 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> [=========================>--------------] 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> [=========================>--------------] 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> [=========================>--------------] 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> [=========================>--------------] 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> [=========================>--------------] 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> [=========================>--------------] 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> [=========================>--------------] 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> [==========================>-------------] 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> [==========================>-------------] 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> [==========================>-------------] 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> [==========================>-------------] 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> [==========================>-------------] 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> [==========================>-------------] 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> [==========================>-------------] 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> [==========================>-------------] 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> [==========================>-------------] 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> [==========================>-------------] 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> [==========================>-------------] 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> [==========================>-------------] 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> [==========================>-------------] 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> [==========================>-------------] 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> [==========================>-------------] 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> [==========================>-------------] 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> [==========================>-------------] 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> [==========================>-------------] 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> [==========================>-------------] 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> [==========================>-------------] 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> [==========================>-------------] 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> [==========================>-------------] 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> [==========================>-------------] 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> [==========================>-------------] 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> [==========================>-------------] 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> [===========================>------------] 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> [===========================>------------] 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> [===========================>------------] 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> [===========================>------------] 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> [===========================>------------] 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> [===========================>------------] 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> [===========================>------------] 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> [===========================>------------] 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> [===========================>------------] 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> [===========================>------------] 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> [===========================>------------] 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> [===========================>------------] 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> [===========================>------------] 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> [===========================>------------] 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> [===========================>------------] 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> [===========================>------------] 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> [===========================>------------] 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> [===========================>------------] 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> [===========================>------------] 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> [===========================>------------] 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> [===========================>------------] 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> [===========================>------------] 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> [===========================>------------] 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> [===========================>------------] 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> [===========================>------------] 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> [============================>-----------] 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> [============================>-----------] 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> [============================>-----------] 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> [============================>-----------] 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> [============================>-----------] 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> [============================>-----------] 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> [============================>-----------] 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> [============================>-----------] 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> [============================>-----------] 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> [============================>-----------] 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> [============================>-----------] 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> [============================>-----------] 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> [============================>-----------] 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> [============================>-----------] 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> [============================>-----------] 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> [============================>-----------] 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> [============================>-----------] 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> [============================>-----------] 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> [============================>-----------] 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> [============================>-----------] 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> [============================>-----------] 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> [============================>-----------] 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> [============================>-----------] 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> [============================>-----------] 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> [============================>-----------] 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> [=============================>----------] 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> [=============================>----------] 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> [=============================>----------] 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> [=============================>----------] 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> [=============================>----------] 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> [=============================>----------] 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> [=============================>----------] 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> [=============================>----------] 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> [=============================>----------] 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> [=============================>----------] 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> [=============================>----------] 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> [=============================>----------] 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> [=============================>----------] 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> [=============================>----------] 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> [=============================>----------] 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> [=============================>----------] 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> [=============================>----------] 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> [=============================>----------] 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> [=============================>----------] 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> [=============================>----------] 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> [=============================>----------] 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> [=============================>----------] 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> [=============================>----------] 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> [=============================>----------] 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> [=============================>----------] 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> [==============================>---------] 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> [==============================>---------] 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> [==============================>---------] 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> [==============================>---------] 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> [==============================>---------] 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> [==============================>---------] 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> [==============================>---------] 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> [==============================>---------] 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> [==============================>---------] 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> [==============================>---------] 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> [==============================>---------] 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> [==============================>---------] 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> [==============================>---------] 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> [==============================>---------] 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> [==============================>---------] 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> [==============================>---------] 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> [==============================>---------] 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> [==============================>---------] 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> [==============================>---------] 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> [==============================>---------] 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> [==============================>---------] 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> [==============================>---------] 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> [==============================>---------] 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> [==============================>---------] 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> [==============================>---------] 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> [===============================>--------] 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> [===============================>--------] 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> [===============================>--------] 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> [===============================>--------] 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> [===============================>--------] 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> [===============================>--------] 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> [===============================>--------] 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> [===============================>--------] 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> [===============================>--------] 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> [===============================>--------] 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> [===============================>--------] 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> [===============================>--------] 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> [===============================>--------] 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> [===============================>--------] 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> [===============================>--------] 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> [===============================>--------] 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> [===============================>--------] 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> [===============================>--------] 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> [===============================>--------] 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> [===============================>--------] 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> [===============================>--------] 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> [===============================>--------] 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> [===============================>--------] 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> [===============================>--------] 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> [===============================>--------] 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> [================================>-------] 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> [================================>-------] 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> [================================>-------] 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> [================================>-------] 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> [================================>-------] 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> [================================>-------] 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> [================================>-------] 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> [================================>-------] 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> [================================>-------] 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> [================================>-------] 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> [================================>-------] 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> [================================>-------] 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> [================================>-------] 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> [================================>-------] 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> [================================>-------] 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> [================================>-------] 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> [================================>-------] 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> [================================>-------] 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> [================================>-------] 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> [================================>-------] 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> [================================>-------] 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> [================================>-------] 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> [================================>-------] 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> [================================>-------] 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> [================================>-------] 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> [=================================>------] 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> [=================================>------] 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> [=================================>------] 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> [=================================>------] 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> [=================================>------] 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> [=================================>------] 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> [=================================>------] 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> [=================================>------] 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> [=================================>------] 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> [=================================>------] 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> [=================================>------] 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> [=================================>------] 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> [=================================>------] 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> [=================================>------] 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> [=================================>------] 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> [=================================>------] 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> [=================================>------] 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> [=================================>------] 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> [=================================>------] 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> [=================================>------] 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> [=================================>------] 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> [=================================>------] 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> [=================================>------] 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> [=================================>------] 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> [=================================>------] 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> [==================================>-----] 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> [==================================>-----] 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> [==================================>-----] 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> [==================================>-----] 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> [==================================>-----] 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> [==================================>-----] 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> [==================================>-----] 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> [==================================>-----] 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> [==================================>-----] 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> [==================================>-----] 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> [==================================>-----] 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> [==================================>-----] 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> [==================================>-----] 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> [==================================>-----] 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> [==================================>-----] 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> [==================================>-----] 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> [==================================>-----] 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> [==================================>-----] 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> [==================================>-----] 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> [==================================>-----] 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> [==================================>-----] 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> [==================================>-----] 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> [==================================>-----] 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> [==================================>-----] 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> [==================================>-----] 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> [===================================>----] 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> [===================================>----] 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> [===================================>----] 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> [===================================>----] 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> [===================================>----] 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> [===================================>----] 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> [===================================>----] 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> [===================================>----] 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> [===================================>----] 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> [===================================>----] 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> [===================================>----] 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> [===================================>----] 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> [===================================>----] 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> [===================================>----] 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> [===================================>----] 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> [===================================>----] 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> [===================================>----] 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> [===================================>----] 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> [===================================>----] 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> [===================================>----] 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> [===================================>----] 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> [===================================>----] 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> [===================================>----] 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> [===================================>----] 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> [===================================>----] 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> [====================================>---] 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> [====================================>---] 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> [====================================>---] 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> [====================================>---] 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> [====================================>---] 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> [====================================>---] 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> [====================================>---] 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> [====================================>---] 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> [====================================>---] 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> [====================================>---] 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> [====================================>---] 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> [====================================>---] 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> [====================================>---] 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> [====================================>---] 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> [====================================>---] 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> [====================================>---] 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> [====================================>---] 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> [====================================>---] 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> [====================================>---] 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> [====================================>---] 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> [====================================>---] 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> [====================================>---] 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> [====================================>---] 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> [====================================>---] 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> [====================================>---] 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> [=====================================>--] 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> [=====================================>--] 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> [=====================================>--] 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> [=====================================>--] 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> [=====================================>--] 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> [=====================================>--] 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> [=====================================>--] 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> [=====================================>--] 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> [=====================================>--] 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> [=====================================>--] 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> [=====================================>--] 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> [=====================================>--] 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> [=====================================>--] 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> [=====================================>--] 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> [=====================================>--] 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> [=====================================>--] 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> [=====================================>--] 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> [=====================================>--] 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> [=====================================>--] 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> [=====================================>--] 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> [=====================================>--] 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> [=====================================>--] 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> [=====================================>--] 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> [=====================================>--] 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> [=====================================>--] 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> [======================================>-] 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> [======================================>-] 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> [======================================>-] 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> [======================================>-] 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> [======================================>-] 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> [======================================>-] 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> [======================================>-] 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> [======================================>-] 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> [======================================>-] 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> [======================================>-] 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> [======================================>-] 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> [======================================>-] 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> [======================================>-] 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> [======================================>-] 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> [======================================>-] 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> [======================================>-] 99.00% | train_error: 0.173 | train_acc: 0.983
|
||
</pre></div>
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</div>
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<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [======================================>-] 99.10% | train_error: 0.173 | train_acc: 0.983
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</pre></div>
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</div>
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<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [======================================>-] 99.20% | train_error: 0.171 | train_acc: 0.984
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</pre></div>
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</div>
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<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [======================================>-] 99.30% | train_error: 0.168 | train_acc: 0.984
|
||
</pre></div>
|
||
</div>
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<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [======================================>-] 99.40% | train_error: 0.167 | train_acc: 0.984
|
||
</pre></div>
|
||
</div>
|
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<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [======================================>-] 99.50% | train_error: 0.174 | train_acc: 0.983
|
||
</pre></div>
|
||
</div>
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<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [======================================>-] 99.60% | train_error: 0.158 | train_acc: 0.985
|
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</pre></div>
|
||
</div>
|
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<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [======================================>-] 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> [======================================>-] 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> [======================================>-] 99.90% | train_error: 0.175 | train_acc: 0.983
|
||
</pre></div>
|
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</div>
|
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<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>
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</pre></div>
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</div>
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<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [=======================================>] 100.0% | train_error: 0.175 | train_acc: 0.983
|
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</pre></div>
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