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mhjensen a20960aa4a updated can notes
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<!-- navigation toc: --> <li><a href="._NeuralNet-bs001.html#___sec0" style="font-size: 80%;"><b>Neural networks</b></a></li>
<!-- navigation toc: --> <li><a href="._NeuralNet-bs002.html#___sec1" style="font-size: 80%;"><b>Artificial neurons</b></a></li>
<!-- navigation toc: --> <li><a href="._NeuralNet-bs003.html#___sec2" style="font-size: 80%;"><b>Neural network types</b></a></li>
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<!-- navigation toc: --> <li><a href="._NeuralNet-bs008.html#___sec7" style="font-size: 80%;"><b>Multilayer perceptrons</b></a></li>
<!-- navigation toc: --> <li><a href="._NeuralNet-bs009.html#___sec8" style="font-size: 80%;"><b>Why multilayer perceptrons?</b></a></li>
<!-- navigation toc: --> <li><a href="._NeuralNet-bs010.html#___sec9" style="font-size: 80%;"><b>Mathematical model</b></a></li>
<!-- navigation toc: --> <li><a href="._NeuralNet-bs011.html#___sec10" style="font-size: 80%;"><b>Mathematical model</b></a></li>
<!-- navigation toc: --> <li><a href="._NeuralNet-bs012.html#___sec11" style="font-size: 80%;"><b>Mathematical model</b></a></li>
<!-- navigation toc: --> <li><a href="._NeuralNet-bs013.html#___sec12" style="font-size: 80%;"><b>Mathematical model</b></a></li>
<!-- navigation toc: --> <li><a href="._NeuralNet-bs014.html#___sec13" style="font-size: 80%;"><b>Mathematical model</b></a></li>
<!-- navigation toc: --> <li><a href="._NeuralNet-bs015.html#___sec14" style="font-size: 80%;">&nbsp;&nbsp;&nbsp;Matrix-vector notation</a></li>
<!-- navigation toc: --> <li><a href="._NeuralNet-bs016.html#___sec15" style="font-size: 80%;">&nbsp;&nbsp;&nbsp;Matrix-vector notation and activation</a></li>
<!-- navigation toc: --> <li><a href="._NeuralNet-bs017.html#___sec16" style="font-size: 80%;">&nbsp;&nbsp;&nbsp;Activation functions</a></li>
<!-- navigation toc: --> <li><a href="._NeuralNet-bs018.html#___sec17" style="font-size: 80%;">&nbsp;&nbsp;&nbsp;Activation functions, Logistic and Hyperbolic ones</a></li>
<!-- navigation toc: --> <li><a href="._NeuralNet-bs019.html#___sec18" style="font-size: 80%;">&nbsp;&nbsp;&nbsp;Relevance</a></li>
<!-- navigation toc: --> <li><a href="._NeuralNet-bs020.html#___sec19" style="font-size: 80%;"><b>The multilayer perceptron (MLP)</b></a></li>
<!-- navigation toc: --> <li><a href="._NeuralNet-bs021.html#___sec20" style="font-size: 80%;"><b>From one to many layers, the universal approximation theorem</b></a></li>
<!-- navigation toc: --> <li><a href="._NeuralNet-bs022.html#___sec21" style="font-size: 80%;"><b>Deriving the back propagation code for a multilayer perceptron model</b></a></li>
<!-- navigation toc: --> <li><a href="._NeuralNet-bs023.html#___sec22" style="font-size: 80%;"><b>Definitions</b></a></li>
<!-- navigation toc: --> <li><a href="._NeuralNet-bs024.html#___sec23" style="font-size: 80%;"><b>Derivatives and the chain rule</b></a></li>
<!-- navigation toc: --> <li><a href="._NeuralNet-bs025.html#___sec24" style="font-size: 80%;"><b>Derivative of the cost function</b></a></li>
<!-- navigation toc: --> <li><a href="._NeuralNet-bs026.html#___sec25" style="font-size: 80%;"><b>Bringing it together, first back propagation equation</b></a></li>
<!-- navigation toc: --> <li><a href="._NeuralNet-bs027.html#___sec26" style="font-size: 80%;"><b>Derivatives in terms of \( z_j^L \)</b></a></li>
<!-- navigation toc: --> <li><a href="._NeuralNet-bs028.html#___sec27" style="font-size: 80%;"><b>Bringing it together</b></a></li>
<!-- navigation toc: --> <li><a href="._NeuralNet-bs029.html#___sec28" style="font-size: 80%;"><b>Final back propagating equation</b></a></li>
<!-- navigation toc: --> <li><a href="._NeuralNet-bs030.html#___sec29" style="font-size: 80%;"><b>Setting up the Back propagation algorithm</b></a></li>
<!-- navigation toc: --> <li><a href="._NeuralNet-bs031.html#___sec30" style="font-size: 80%;"><b>Setting up a Multi-layer perceptron model for classification</b></a></li>
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<!-- navigation toc: --> <li><a href="._NeuralNet-bs033.html#___sec32" style="font-size: 80%;"><b>Example: binary classification problem</b></a></li>
<!-- navigation toc: --> <li><a href="._NeuralNet-bs034.html#___sec33" style="font-size: 80%;"><b>The Softmax function</b></a></li>
<!-- navigation toc: --> <li><a href="._NeuralNet-bs035.html#___sec34" style="font-size: 80%;"><b>Developing a code for doing neural networks with back propagation</b></a></li>
<!-- navigation toc: --> <li><a href="._NeuralNet-bs036.html#___sec35" style="font-size: 80%;"><b>Collect and pre-process data</b></a></li>
<!-- navigation toc: --> <li><a href="._NeuralNet-bs037.html#___sec36" style="font-size: 80%;"><b>Train and test datasets</b></a></li>
<!-- navigation toc: --> <li><a href="._NeuralNet-bs038.html#___sec37" style="font-size: 80%;"><b>Define model and architecture</b></a></li>
<!-- navigation toc: --> <li><a href="._NeuralNet-bs039.html#___sec38" style="font-size: 80%;"><b>Layers</b></a></li>
<!-- navigation toc: --> <li><a href="._NeuralNet-bs040.html#___sec39" style="font-size: 80%;"><b>Weights and biases</b></a></li>
<!-- navigation toc: --> <li><a href="._NeuralNet-bs041.html#___sec40" style="font-size: 80%;"><b>Feed-forward pass</b></a></li>
<!-- navigation toc: --> <li><a href="._NeuralNet-bs042.html#___sec41" style="font-size: 80%;"><b>Matrix multiplications</b></a></li>
<!-- navigation toc: --> <li><a href="._NeuralNet-bs043.html#___sec42" style="font-size: 80%;"><b>Choose cost function and optimizer</b></a></li>
<!-- navigation toc: --> <li><a href="._NeuralNet-bs044.html#___sec43" style="font-size: 80%;"><b>Optimizing the cost function</b></a></li>
<!-- navigation toc: --> <li><a href="._NeuralNet-bs045.html#___sec44" style="font-size: 80%;"><b>Regularization</b></a></li>
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<!-- navigation toc: --> <li><a href="._NeuralNet-bs047.html#___sec46" style="font-size: 80%;"><b>Improving performance</b></a></li>
<!-- navigation toc: --> <li><a href="._NeuralNet-bs048.html#___sec47" style="font-size: 80%;"><b>Full object-oriented implementation</b></a></li>
<!-- navigation toc: --> <li><a href="._NeuralNet-bs049.html#___sec48" style="font-size: 80%;"><b>Evaluate model performance on test data</b></a></li>
<!-- navigation toc: --> <li><a href="._NeuralNet-bs050.html#___sec49" style="font-size: 80%;"><b>Adjust hyperparameters</b></a></li>
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<!-- navigation toc: --> <li><a href="._NeuralNet-bs052.html#___sec51" style="font-size: 80%;"><b>scikit-learn implementation</b></a></li>
<!-- navigation toc: --> <li><a href="._NeuralNet-bs053.html#___sec52" style="font-size: 80%;"><b>Visualization</b></a></li>
<!-- navigation toc: --> <li><a href="._NeuralNet-bs054.html#___sec53" style="font-size: 80%;"><b>Building neural networks in Tensorflow and Keras</b></a></li>
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<!-- navigation toc: --> <li><a href="._NeuralNet-bs056.html#___sec55" style="font-size: 80%;"><b>Collect and pre-process data</b></a></li>
<!-- navigation toc: --> <li><a href="#___sec56" style="font-size: 80%;"><b>Using TensorFlow backend</b></a></li>
<!-- navigation toc: --> <li><a href="._NeuralNet-bs058.html#___sec57" style="font-size: 80%;"><b>Optimizing and using gradient descent</b></a></li>
<!-- navigation toc: --> <li><a href="._NeuralNet-bs059.html#___sec58" style="font-size: 80%;"><b>Using Keras</b></a></li>
<!-- navigation toc: --> <li><a href="._NeuralNet-bs060.html#___sec59" style="font-size: 80%;"><b>Which activation function should I use?</b></a></li>
<!-- navigation toc: --> <li><a href="._NeuralNet-bs061.html#___sec60" style="font-size: 80%;"><b>Is the Logistic activation function (Sigmoid) our choice?</b></a></li>
<!-- navigation toc: --> <li><a href="._NeuralNet-bs062.html#___sec61" style="font-size: 80%;"><b>The derivative of the Logistic funtion</b></a></li>
<!-- navigation toc: --> <li><a href="._NeuralNet-bs063.html#___sec62" style="font-size: 80%;"><b>The RELU function family</b></a></li>
<!-- navigation toc: --> <li><a href="._NeuralNet-bs064.html#___sec63" style="font-size: 80%;"><b>Which activation function should we use?</b></a></li>
<!-- navigation toc: --> <li><a href="._NeuralNet-bs065.html#___sec64" style="font-size: 80%;"><b>A top-down perspective on Neural networks</b></a></li>
<!-- navigation toc: --> <li><a href="._NeuralNet-bs066.html#___sec65" style="font-size: 80%;"><b>Limitations of supervised learning with deep networks</b></a></li>
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<h2 id="___sec56" class="anchor">Using TensorFlow backend </h2>
<ol>
<li> Define model and architecture</li>
<li> Choose cost function and optimizer</li>
</ol>
<p>
<!-- code=python (!bc pycod) typeset with pygments style "default" -->
<div class="highlight" style="background: #f8f8f8"><pre style="line-height: 125%"><span></span><span style="color: #008000; font-weight: bold">import</span> <span style="color: #0000FF; font-weight: bold">tensorflow</span> <span style="color: #008000; font-weight: bold">as</span> <span style="color: #0000FF; font-weight: bold">tf</span>
<span style="color: #008000; font-weight: bold">class</span> <span style="color: #0000FF; font-weight: bold">NeuralNetworkTensorflow</span>:
<span style="color: #008000; font-weight: bold">def</span> <span style="color: #0000FF">__init__</span>(
<span style="color: #008000">self</span>,
X_train,
Y_train,
X_test,
Y_test,
n_neurons_layer1<span style="color: #666666">=100</span>,
n_neurons_layer2<span style="color: #666666">=50</span>,
n_categories<span style="color: #666666">=2</span>,
epochs<span style="color: #666666">=10</span>,
batch_size<span style="color: #666666">=100</span>,
eta<span style="color: #666666">=0.1</span>,
lmbd<span style="color: #666666">=0.0</span>):
<span style="color: #408080; font-style: italic"># keep track of number of steps</span>
<span style="color: #008000">self</span><span style="color: #666666">.</span>global_step <span style="color: #666666">=</span> tf<span style="color: #666666">.</span>Variable(<span style="color: #666666">0</span>, dtype<span style="color: #666666">=</span>tf<span style="color: #666666">.</span>int32, trainable<span style="color: #666666">=</span><span style="color: #008000">False</span>, name<span style="color: #666666">=</span><span style="color: #BA2121">&#39;global_step&#39;</span>)
<span style="color: #008000">self</span><span style="color: #666666">.</span>X_train <span style="color: #666666">=</span> X_train
<span style="color: #008000">self</span><span style="color: #666666">.</span>Y_train <span style="color: #666666">=</span> Y_train
<span style="color: #008000">self</span><span style="color: #666666">.</span>X_test <span style="color: #666666">=</span> X_test
<span style="color: #008000">self</span><span style="color: #666666">.</span>Y_test <span style="color: #666666">=</span> Y_test
<span style="color: #008000">self</span><span style="color: #666666">.</span>n_inputs <span style="color: #666666">=</span> X_train<span style="color: #666666">.</span>shape[<span style="color: #666666">0</span>]
<span style="color: #008000">self</span><span style="color: #666666">.</span>n_features <span style="color: #666666">=</span> X_train<span style="color: #666666">.</span>shape[<span style="color: #666666">1</span>]
<span style="color: #008000">self</span><span style="color: #666666">.</span>n_neurons_layer1 <span style="color: #666666">=</span> n_neurons_layer1
<span style="color: #008000">self</span><span style="color: #666666">.</span>n_neurons_layer2 <span style="color: #666666">=</span> n_neurons_layer2
<span style="color: #008000">self</span><span style="color: #666666">.</span>n_categories <span style="color: #666666">=</span> n_categories
<span style="color: #008000">self</span><span style="color: #666666">.</span>epochs <span style="color: #666666">=</span> epochs
<span style="color: #008000">self</span><span style="color: #666666">.</span>batch_size <span style="color: #666666">=</span> batch_size
<span style="color: #008000">self</span><span style="color: #666666">.</span>iterations <span style="color: #666666">=</span> <span style="color: #008000">self</span><span style="color: #666666">.</span>n_inputs <span style="color: #666666">//</span> <span style="color: #008000">self</span><span style="color: #666666">.</span>batch_size
<span style="color: #008000">self</span><span style="color: #666666">.</span>eta <span style="color: #666666">=</span> eta
<span style="color: #008000">self</span><span style="color: #666666">.</span>lmbd <span style="color: #666666">=</span> lmbd
<span style="color: #408080; font-style: italic"># build network piece by piece</span>
<span style="color: #408080; font-style: italic"># name scopes (with) are used to enforce creation of new variables</span>
<span style="color: #408080; font-style: italic"># https://www.tensorflow.org/guide/variables</span>
<span style="color: #008000">self</span><span style="color: #666666">.</span>create_placeholders()
<span style="color: #008000">self</span><span style="color: #666666">.</span>create_DNN()
<span style="color: #008000">self</span><span style="color: #666666">.</span>create_loss()
<span style="color: #008000">self</span><span style="color: #666666">.</span>create_optimiser()
<span style="color: #008000">self</span><span style="color: #666666">.</span>create_accuracy()
<span style="color: #008000; font-weight: bold">def</span> <span style="color: #0000FF">create_placeholders</span>(<span style="color: #008000">self</span>):
<span style="color: #408080; font-style: italic"># placeholders are fine here, but &quot;Datasets&quot; are the preferred method</span>
<span style="color: #408080; font-style: italic"># of streaming data into a model</span>
<span style="color: #008000; font-weight: bold">with</span> tf<span style="color: #666666">.</span>name_scope(<span style="color: #BA2121">&#39;data&#39;</span>):
<span style="color: #008000">self</span><span style="color: #666666">.</span>X <span style="color: #666666">=</span> tf<span style="color: #666666">.</span>placeholder(tf<span style="color: #666666">.</span>float32, shape<span style="color: #666666">=</span>(<span style="color: #008000">None</span>, <span style="color: #008000">self</span><span style="color: #666666">.</span>n_features), name<span style="color: #666666">=</span><span style="color: #BA2121">&#39;X_data&#39;</span>)
<span style="color: #008000">self</span><span style="color: #666666">.</span>Y <span style="color: #666666">=</span> tf<span style="color: #666666">.</span>placeholder(tf<span style="color: #666666">.</span>float32, shape<span style="color: #666666">=</span>(<span style="color: #008000">None</span>, <span style="color: #008000">self</span><span style="color: #666666">.</span>n_categories), name<span style="color: #666666">=</span><span style="color: #BA2121">&#39;Y_data&#39;</span>)
<span style="color: #008000; font-weight: bold">def</span> <span style="color: #0000FF">create_DNN</span>(<span style="color: #008000">self</span>):
<span style="color: #008000; font-weight: bold">with</span> tf<span style="color: #666666">.</span>name_scope(<span style="color: #BA2121">&#39;DNN&#39;</span>):
<span style="color: #408080; font-style: italic"># the weights are stored to calculate regularization loss later</span>
<span style="color: #408080; font-style: italic"># Fully connected layer 1</span>
<span style="color: #008000">self</span><span style="color: #666666">.</span>W_fc1 <span style="color: #666666">=</span> <span style="color: #008000">self</span><span style="color: #666666">.</span>weight_variable([<span style="color: #008000">self</span><span style="color: #666666">.</span>n_features, <span style="color: #008000">self</span><span style="color: #666666">.</span>n_neurons_layer1], name<span style="color: #666666">=</span><span style="color: #BA2121">&#39;fc1&#39;</span>, dtype<span style="color: #666666">=</span>tf<span style="color: #666666">.</span>float32)
b_fc1 <span style="color: #666666">=</span> <span style="color: #008000">self</span><span style="color: #666666">.</span>bias_variable([<span style="color: #008000">self</span><span style="color: #666666">.</span>n_neurons_layer1], name<span style="color: #666666">=</span><span style="color: #BA2121">&#39;fc1&#39;</span>, dtype<span style="color: #666666">=</span>tf<span style="color: #666666">.</span>float32)
a_fc1 <span style="color: #666666">=</span> tf<span style="color: #666666">.</span>nn<span style="color: #666666">.</span>sigmoid(tf<span style="color: #666666">.</span>matmul(<span style="color: #008000">self</span><span style="color: #666666">.</span>X, <span style="color: #008000">self</span><span style="color: #666666">.</span>W_fc1) <span style="color: #666666">+</span> b_fc1)
<span style="color: #408080; font-style: italic"># Fully connected layer 2</span>
<span style="color: #008000">self</span><span style="color: #666666">.</span>W_fc2 <span style="color: #666666">=</span> <span style="color: #008000">self</span><span style="color: #666666">.</span>weight_variable([<span style="color: #008000">self</span><span style="color: #666666">.</span>n_neurons_layer1, <span style="color: #008000">self</span><span style="color: #666666">.</span>n_neurons_layer2], name<span style="color: #666666">=</span><span style="color: #BA2121">&#39;fc2&#39;</span>, dtype<span style="color: #666666">=</span>tf<span style="color: #666666">.</span>float32)
b_fc2 <span style="color: #666666">=</span> <span style="color: #008000">self</span><span style="color: #666666">.</span>bias_variable([<span style="color: #008000">self</span><span style="color: #666666">.</span>n_neurons_layer2], name<span style="color: #666666">=</span><span style="color: #BA2121">&#39;fc2&#39;</span>, dtype<span style="color: #666666">=</span>tf<span style="color: #666666">.</span>float32)
a_fc2 <span style="color: #666666">=</span> tf<span style="color: #666666">.</span>nn<span style="color: #666666">.</span>sigmoid(tf<span style="color: #666666">.</span>matmul(a_fc1, <span style="color: #008000">self</span><span style="color: #666666">.</span>W_fc2) <span style="color: #666666">+</span> b_fc2)
<span style="color: #408080; font-style: italic"># Output layer</span>
<span style="color: #008000">self</span><span style="color: #666666">.</span>W_out <span style="color: #666666">=</span> <span style="color: #008000">self</span><span style="color: #666666">.</span>weight_variable([<span style="color: #008000">self</span><span style="color: #666666">.</span>n_neurons_layer2, <span style="color: #008000">self</span><span style="color: #666666">.</span>n_categories], name<span style="color: #666666">=</span><span style="color: #BA2121">&#39;out&#39;</span>, dtype<span style="color: #666666">=</span>tf<span style="color: #666666">.</span>float32)
b_out <span style="color: #666666">=</span> <span style="color: #008000">self</span><span style="color: #666666">.</span>bias_variable([<span style="color: #008000">self</span><span style="color: #666666">.</span>n_categories], name<span style="color: #666666">=</span><span style="color: #BA2121">&#39;out&#39;</span>, dtype<span style="color: #666666">=</span>tf<span style="color: #666666">.</span>float32)
<span style="color: #008000">self</span><span style="color: #666666">.</span>z_out <span style="color: #666666">=</span> tf<span style="color: #666666">.</span>matmul(a_fc2, <span style="color: #008000">self</span><span style="color: #666666">.</span>W_out) <span style="color: #666666">+</span> b_out
<span style="color: #008000; font-weight: bold">def</span> <span style="color: #0000FF">create_loss</span>(<span style="color: #008000">self</span>):
<span style="color: #008000; font-weight: bold">with</span> tf<span style="color: #666666">.</span>name_scope(<span style="color: #BA2121">&#39;loss&#39;</span>):
softmax_loss <span style="color: #666666">=</span> tf<span style="color: #666666">.</span>reduce_mean(tf<span style="color: #666666">.</span>nn<span style="color: #666666">.</span>softmax_cross_entropy_with_logits_v2(labels<span style="color: #666666">=</span><span style="color: #008000">self</span><span style="color: #666666">.</span>Y, logits<span style="color: #666666">=</span><span style="color: #008000">self</span><span style="color: #666666">.</span>z_out))
regularizer_loss_fc1 <span style="color: #666666">=</span> tf<span style="color: #666666">.</span>nn<span style="color: #666666">.</span>l2_loss(<span style="color: #008000">self</span><span style="color: #666666">.</span>W_fc1)
regularizer_loss_fc2 <span style="color: #666666">=</span> tf<span style="color: #666666">.</span>nn<span style="color: #666666">.</span>l2_loss(<span style="color: #008000">self</span><span style="color: #666666">.</span>W_fc2)
regularizer_loss_out <span style="color: #666666">=</span> tf<span style="color: #666666">.</span>nn<span style="color: #666666">.</span>l2_loss(<span style="color: #008000">self</span><span style="color: #666666">.</span>W_out)
regularizer_loss <span style="color: #666666">=</span> <span style="color: #008000">self</span><span style="color: #666666">.</span>lmbd<span style="color: #666666">*</span>(regularizer_loss_fc1 <span style="color: #666666">+</span> regularizer_loss_fc2 <span style="color: #666666">+</span> regularizer_loss_out)
<span style="color: #008000">self</span><span style="color: #666666">.</span>loss <span style="color: #666666">=</span> softmax_loss <span style="color: #666666">+</span> regularizer_loss
<span style="color: #008000; font-weight: bold">def</span> <span style="color: #0000FF">create_accuracy</span>(<span style="color: #008000">self</span>):
<span style="color: #008000; font-weight: bold">with</span> tf<span style="color: #666666">.</span>name_scope(<span style="color: #BA2121">&#39;accuracy&#39;</span>):
probabilities <span style="color: #666666">=</span> tf<span style="color: #666666">.</span>nn<span style="color: #666666">.</span>softmax(<span style="color: #008000">self</span><span style="color: #666666">.</span>z_out)
predictions <span style="color: #666666">=</span> tf<span style="color: #666666">.</span>argmax(probabilities, axis<span style="color: #666666">=1</span>)
labels <span style="color: #666666">=</span> tf<span style="color: #666666">.</span>argmax(<span style="color: #008000">self</span><span style="color: #666666">.</span>Y, axis<span style="color: #666666">=1</span>)
correct_predictions <span style="color: #666666">=</span> tf<span style="color: #666666">.</span>equal(predictions, labels)
correct_predictions <span style="color: #666666">=</span> tf<span style="color: #666666">.</span>cast(correct_predictions, tf<span style="color: #666666">.</span>float32)
<span style="color: #008000">self</span><span style="color: #666666">.</span>accuracy <span style="color: #666666">=</span> tf<span style="color: #666666">.</span>reduce_mean(correct_predictions)
<span style="color: #008000; font-weight: bold">def</span> <span style="color: #0000FF">create_optimiser</span>(<span style="color: #008000">self</span>):
<span style="color: #008000; font-weight: bold">with</span> tf<span style="color: #666666">.</span>name_scope(<span style="color: #BA2121">&#39;optimizer&#39;</span>):
<span style="color: #008000">self</span><span style="color: #666666">.</span>optimizer <span style="color: #666666">=</span> tf<span style="color: #666666">.</span>train<span style="color: #666666">.</span>GradientDescentOptimizer(learning_rate<span style="color: #666666">=</span><span style="color: #008000">self</span><span style="color: #666666">.</span>eta)<span style="color: #666666">.</span>minimize(<span style="color: #008000">self</span><span style="color: #666666">.</span>loss, global_step<span style="color: #666666">=</span><span style="color: #008000">self</span><span style="color: #666666">.</span>global_step)
<span style="color: #008000; font-weight: bold">def</span> <span style="color: #0000FF">weight_variable</span>(<span style="color: #008000">self</span>, shape, name<span style="color: #666666">=</span><span style="color: #BA2121">&#39;&#39;</span>, dtype<span style="color: #666666">=</span>tf<span style="color: #666666">.</span>float32):
initial <span style="color: #666666">=</span> tf<span style="color: #666666">.</span>truncated_normal(shape, stddev<span style="color: #666666">=0.1</span>)
<span style="color: #008000; font-weight: bold">return</span> tf<span style="color: #666666">.</span>Variable(initial, name<span style="color: #666666">=</span>name, dtype<span style="color: #666666">=</span>dtype)
<span style="color: #008000; font-weight: bold">def</span> <span style="color: #0000FF">bias_variable</span>(<span style="color: #008000">self</span>, shape, name<span style="color: #666666">=</span><span style="color: #BA2121">&#39;&#39;</span>, dtype<span style="color: #666666">=</span>tf<span style="color: #666666">.</span>float32):
initial <span style="color: #666666">=</span> tf<span style="color: #666666">.</span>constant(<span style="color: #666666">0.1</span>, shape<span style="color: #666666">=</span>shape)
<span style="color: #008000; font-weight: bold">return</span> tf<span style="color: #666666">.</span>Variable(initial, name<span style="color: #666666">=</span>name, dtype<span style="color: #666666">=</span>dtype)
<span style="color: #008000; font-weight: bold">def</span> <span style="color: #0000FF">fit</span>(<span style="color: #008000">self</span>):
data_indices <span style="color: #666666">=</span> np<span style="color: #666666">.</span>arange(<span style="color: #008000">self</span><span style="color: #666666">.</span>n_inputs)
<span style="color: #008000; font-weight: bold">with</span> tf<span style="color: #666666">.</span>Session() <span style="color: #008000; font-weight: bold">as</span> sess:
sess<span style="color: #666666">.</span>run(tf<span style="color: #666666">.</span>global_variables_initializer())
<span style="color: #008000; font-weight: bold">for</span> i <span style="color: #AA22FF; font-weight: bold">in</span> <span style="color: #008000">range</span>(<span style="color: #008000">self</span><span style="color: #666666">.</span>epochs):
<span style="color: #008000; font-weight: bold">for</span> j <span style="color: #AA22FF; font-weight: bold">in</span> <span style="color: #008000">range</span>(<span style="color: #008000">self</span><span style="color: #666666">.</span>iterations):
chosen_datapoints <span style="color: #666666">=</span> np<span style="color: #666666">.</span>random<span style="color: #666666">.</span>choice(data_indices, size<span style="color: #666666">=</span><span style="color: #008000">self</span><span style="color: #666666">.</span>batch_size, replace<span style="color: #666666">=</span><span style="color: #008000">False</span>)
batch_X, batch_Y <span style="color: #666666">=</span> <span style="color: #008000">self</span><span style="color: #666666">.</span>X_train[chosen_datapoints], <span style="color: #008000">self</span><span style="color: #666666">.</span>Y_train[chosen_datapoints]
sess<span style="color: #666666">.</span>run([DNN<span style="color: #666666">.</span>loss, DNN<span style="color: #666666">.</span>optimizer],
feed_dict<span style="color: #666666">=</span>{DNN<span style="color: #666666">.</span>X: batch_X,
DNN<span style="color: #666666">.</span>Y: batch_Y})
accuracy <span style="color: #666666">=</span> sess<span style="color: #666666">.</span>run(DNN<span style="color: #666666">.</span>accuracy,
feed_dict<span style="color: #666666">=</span>{DNN<span style="color: #666666">.</span>X: batch_X,
DNN<span style="color: #666666">.</span>Y: batch_Y})
step <span style="color: #666666">=</span> sess<span style="color: #666666">.</span>run(DNN<span style="color: #666666">.</span>global_step)
<span style="color: #008000">self</span><span style="color: #666666">.</span>train_loss, <span style="color: #008000">self</span><span style="color: #666666">.</span>train_accuracy <span style="color: #666666">=</span> sess<span style="color: #666666">.</span>run([DNN<span style="color: #666666">.</span>loss, DNN<span style="color: #666666">.</span>accuracy],
feed_dict<span style="color: #666666">=</span>{DNN<span style="color: #666666">.</span>X: <span style="color: #008000">self</span><span style="color: #666666">.</span>X_train,
DNN<span style="color: #666666">.</span>Y: <span style="color: #008000">self</span><span style="color: #666666">.</span>Y_train})
<span style="color: #008000">self</span><span style="color: #666666">.</span>test_loss, <span style="color: #008000">self</span><span style="color: #666666">.</span>test_accuracy <span style="color: #666666">=</span> sess<span style="color: #666666">.</span>run([DNN<span style="color: #666666">.</span>loss, DNN<span style="color: #666666">.</span>accuracy],
feed_dict<span style="color: #666666">=</span>{DNN<span style="color: #666666">.</span>X: <span style="color: #008000">self</span><span style="color: #666666">.</span>X_test,
DNN<span style="color: #666666">.</span>Y: <span style="color: #008000">self</span><span style="color: #666666">.</span>Y_test})
</pre></div>
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