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mhjensen 021cd0f677 updating keras
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<!-- navigation toc: --> <li><a href="._week41-bs001.html#___sec0" style="font-size: 80%;">Plan for week 41</a></li>
<!-- navigation toc: --> <li><a href="._week41-bs002.html#___sec1" style="font-size: 80%;">Setting up the Back propagation algorithm</a></li>
<!-- navigation toc: --> <li><a href="._week41-bs003.html#___sec2" style="font-size: 80%;">Setting up a Multi-layer perceptron model for classification</a></li>
<!-- navigation toc: --> <li><a href="._week41-bs004.html#___sec3" style="font-size: 80%;">Defining the cost function</a></li>
<!-- navigation toc: --> <li><a href="._week41-bs005.html#___sec4" style="font-size: 80%;">Example: binary classification problem</a></li>
<!-- navigation toc: --> <li><a href="._week41-bs006.html#___sec5" style="font-size: 80%;">The Softmax function</a></li>
<!-- navigation toc: --> <li><a href="._week41-bs007.html#___sec6" style="font-size: 80%;">Developing a code for doing neural networks with back propagation</a></li>
<!-- navigation toc: --> <li><a href="._week41-bs008.html#___sec7" style="font-size: 80%;">Collect and pre-process data</a></li>
<!-- navigation toc: --> <li><a href="._week41-bs009.html#___sec8" style="font-size: 80%;">Train and test datasets</a></li>
<!-- navigation toc: --> <li><a href="._week41-bs010.html#___sec9" style="font-size: 80%;">Define model and architecture</a></li>
<!-- navigation toc: --> <li><a href="._week41-bs011.html#___sec10" style="font-size: 80%;">Layers</a></li>
<!-- navigation toc: --> <li><a href="._week41-bs012.html#___sec11" style="font-size: 80%;">Weights and biases</a></li>
<!-- navigation toc: --> <li><a href="._week41-bs013.html#___sec12" style="font-size: 80%;">Feed-forward pass</a></li>
<!-- navigation toc: --> <li><a href="._week41-bs014.html#___sec13" style="font-size: 80%;">Matrix multiplications</a></li>
<!-- navigation toc: --> <li><a href="._week41-bs015.html#___sec14" style="font-size: 80%;">Choose cost function and optimizer</a></li>
<!-- navigation toc: --> <li><a href="._week41-bs016.html#___sec15" style="font-size: 80%;">Optimizing the cost function</a></li>
<!-- navigation toc: --> <li><a href="._week41-bs017.html#___sec16" style="font-size: 80%;">Regularization</a></li>
<!-- navigation toc: --> <li><a href="._week41-bs018.html#___sec17" style="font-size: 80%;">Matrix multiplication</a></li>
<!-- navigation toc: --> <li><a href="._week41-bs019.html#___sec18" style="font-size: 80%;">Improving performance</a></li>
<!-- navigation toc: --> <li><a href="._week41-bs020.html#___sec19" style="font-size: 80%;">Full object-oriented implementation</a></li>
<!-- navigation toc: --> <li><a href="._week41-bs021.html#___sec20" style="font-size: 80%;">Evaluate model performance on test data</a></li>
<!-- navigation toc: --> <li><a href="._week41-bs022.html#___sec21" style="font-size: 80%;">Adjust hyperparameters</a></li>
<!-- navigation toc: --> <li><a href="._week41-bs023.html#___sec22" style="font-size: 80%;">Visualization</a></li>
<!-- navigation toc: --> <li><a href="._week41-bs024.html#___sec23" style="font-size: 80%;">scikit-learn implementation</a></li>
<!-- navigation toc: --> <li><a href="._week41-bs025.html#___sec24" style="font-size: 80%;">Visualization</a></li>
<!-- navigation toc: --> <li><a href="._week41-bs026.html#___sec25" style="font-size: 80%;">Building neural networks in Tensorflow and Keras</a></li>
<!-- navigation toc: --> <li><a href="._week41-bs027.html#___sec26" style="font-size: 80%;">Tensorflow</a></li>
<!-- navigation toc: --> <li><a href="._week41-bs028.html#___sec27" style="font-size: 80%;">Using Keras</a></li>
<!-- navigation toc: --> <li><a href="._week41-bs029.html#___sec28" style="font-size: 80%;">Collect and pre-process data</a></li>
<!-- navigation toc: --> <li><a href="#___sec29" style="font-size: 80%;">The Breast Cancer Data, now with Keras</a></li>
<!-- navigation toc: --> <li><a href="._week41-bs031.html#___sec30" style="font-size: 80%;">Fine-tuning neural network hyperparameters</a></li>
<!-- navigation toc: --> <li><a href="._week41-bs032.html#___sec31" style="font-size: 80%;">Hidden layers</a></li>
<!-- navigation toc: --> <li><a href="._week41-bs033.html#___sec32" style="font-size: 80%;">Which activation function should I use?</a></li>
<!-- navigation toc: --> <li><a href="._week41-bs034.html#___sec33" style="font-size: 80%;">Is the Logistic activation function (Sigmoid) our choice?</a></li>
<!-- navigation toc: --> <li><a href="._week41-bs035.html#___sec34" style="font-size: 80%;">The derivative of the Logistic funtion</a></li>
<!-- navigation toc: --> <li><a href="._week41-bs036.html#___sec35" style="font-size: 80%;">The RELU function family</a></li>
<!-- navigation toc: --> <li><a href="._week41-bs037.html#___sec36" style="font-size: 80%;">Which activation function should we use?</a></li>
<!-- navigation toc: --> <li><a href="._week41-bs038.html#___sec37" style="font-size: 80%;">More on activation functions, output layers</a></li>
<!-- navigation toc: --> <li><a href="._week41-bs039.html#___sec38" style="font-size: 80%;">Batch Normalization</a></li>
<!-- navigation toc: --> <li><a href="._week41-bs040.html#___sec39" style="font-size: 80%;">Dropout</a></li>
<!-- navigation toc: --> <li><a href="._week41-bs041.html#___sec40" style="font-size: 80%;">Gradient Clipping</a></li>
<!-- navigation toc: --> <li><a href="._week41-bs042.html#___sec41" style="font-size: 80%;">A very nice website on Neural Networks</a></li>
<!-- navigation toc: --> <li><a href="._week41-bs043.html#___sec42" style="font-size: 80%;">A top-down perspective on Neural networks</a></li>
<!-- navigation toc: --> <li><a href="._week41-bs044.html#___sec43" style="font-size: 80%;">Limitations of supervised learning with deep networks</a></li>
<!-- navigation toc: --> <li><a href="._week41-bs045.html#___sec44" style="font-size: 80%;">Convolutional Neural Networks (recognizing images)</a></li>
<!-- navigation toc: --> <li><a href="._week41-bs046.html#___sec45" style="font-size: 80%;">Regular NNs don’t scale well to full images</a></li>
<!-- navigation toc: --> <li><a href="._week41-bs047.html#___sec46" style="font-size: 80%;">3D volumes of neurons</a></li>
<!-- navigation toc: --> <li><a href="._week41-bs048.html#___sec47" style="font-size: 80%;">Layers used to build CNNs</a></li>
<!-- navigation toc: --> <li><a href="._week41-bs049.html#___sec48" style="font-size: 80%;">Transforming images</a></li>
<!-- navigation toc: --> <li><a href="._week41-bs050.html#___sec49" style="font-size: 80%;">CNNs in brief</a></li>
<!-- navigation toc: --> <li><a href="._week41-bs051.html#___sec50" style="font-size: 80%;">CNNs in more detail, building convolutional neural networks in Tensorflow and Keras</a></li>
<!-- navigation toc: --> <li><a href="._week41-bs052.html#___sec51" style="font-size: 80%;">Setting it up</a></li>
<!-- navigation toc: --> <li><a href="._week41-bs053.html#___sec52" style="font-size: 80%;">The MNIST dataset again</a></li>
<!-- navigation toc: --> <li><a href="._week41-bs054.html#___sec53" style="font-size: 80%;">Strong correlations</a></li>
<!-- navigation toc: --> <li><a href="._week41-bs055.html#___sec54" style="font-size: 80%;">Layers of a CNN</a></li>
<!-- navigation toc: --> <li><a href="._week41-bs056.html#___sec55" style="font-size: 80%;">Systematic reduction</a></li>
<!-- navigation toc: --> <li><a href="._week41-bs057.html#___sec56" style="font-size: 80%;">Prerequisites: Collect and pre-process data</a></li>
<!-- navigation toc: --> <li><a href="._week41-bs058.html#___sec57" style="font-size: 80%;">Importing Keras and Tensorflow</a></li>
<!-- navigation toc: --> <li><a href="._week41-bs059.html#___sec58" style="font-size: 80%;">Running with Keras</a></li>
<!-- navigation toc: --> <li><a href="._week41-bs060.html#___sec59" style="font-size: 80%;">Final part</a></li>
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<h2 id="___sec29" class="anchor">The Breast Cancer Data, now with Keras </h2>
<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">from</span> <span style="color: #0000FF; font-weight: bold">tensorflow.keras.layers</span> <span style="color: #008000; font-weight: bold">import</span> Input
<span style="color: #008000; font-weight: bold">from</span> <span style="color: #0000FF; font-weight: bold">tensorflow.keras.models</span> <span style="color: #008000; font-weight: bold">import</span> Sequential <span style="color: #408080; font-style: italic">#This allows appending layers to existing models</span>
<span style="color: #008000; font-weight: bold">from</span> <span style="color: #0000FF; font-weight: bold">tensorflow.keras.layers</span> <span style="color: #008000; font-weight: bold">import</span> Dense <span style="color: #408080; font-style: italic">#This allows defining the characteristics of a particular layer</span>
<span style="color: #008000; font-weight: bold">from</span> <span style="color: #0000FF; font-weight: bold">tensorflow.keras</span> <span style="color: #008000; font-weight: bold">import</span> optimizers <span style="color: #408080; font-style: italic">#This allows using whichever optimiser we want (sgd,adam,RMSprop)</span>
<span style="color: #008000; font-weight: bold">from</span> <span style="color: #0000FF; font-weight: bold">tensorflow.keras</span> <span style="color: #008000; font-weight: bold">import</span> regularizers <span style="color: #408080; font-style: italic">#This allows using whichever regularizer we want (l1,l2,l1_l2)</span>
<span style="color: #008000; font-weight: bold">from</span> <span style="color: #0000FF; font-weight: bold">tensorflow.keras.utils</span> <span style="color: #008000; font-weight: bold">import</span> to_categorical <span style="color: #408080; font-style: italic">#This allows using categorical cross entropy as the cost function</span>
<span style="color: #008000; font-weight: bold">import</span> <span style="color: #0000FF; font-weight: bold">numpy</span> <span style="color: #008000; font-weight: bold">as</span> <span style="color: #0000FF; font-weight: bold">np</span>
<span style="color: #008000; font-weight: bold">import</span> <span style="color: #0000FF; font-weight: bold">matplotlib.pyplot</span> <span style="color: #008000; font-weight: bold">as</span> <span style="color: #0000FF; font-weight: bold">plt</span>
<span style="color: #008000; font-weight: bold">import</span> <span style="color: #0000FF; font-weight: bold">seaborn</span> <span style="color: #008000; font-weight: bold">as</span> <span style="color: #0000FF; font-weight: bold">sns</span>
<span style="color: #008000; font-weight: bold">from</span> <span style="color: #0000FF; font-weight: bold">sklearn.model_selection</span> <span style="color: #008000; font-weight: bold">import</span> train_test_split <span style="color: #008000; font-weight: bold">as</span> splitter
<span style="color: #008000; font-weight: bold">from</span> <span style="color: #0000FF; font-weight: bold">sklearn.datasets</span> <span style="color: #008000; font-weight: bold">import</span> load_breast_cancer
<span style="color: #008000; font-weight: bold">import</span> <span style="color: #0000FF; font-weight: bold">pickle</span>
<span style="color: #008000; font-weight: bold">import</span> <span style="color: #0000FF; font-weight: bold">os</span>
<span style="color: #BA2121; font-style: italic">&quot;&quot;&quot;Load breast cancer dataset&quot;&quot;&quot;</span>
np<span style="color: #666666">.</span>random<span style="color: #666666">.</span>seed(<span style="color: #666666">0</span>) <span style="color: #408080; font-style: italic">#create same seed for random number every time</span>
cancer<span style="color: #666666">=</span>load_breast_cancer() <span style="color: #408080; font-style: italic">#Download breast cancer dataset</span>
inputs<span style="color: #666666">=</span>cancer<span style="color: #666666">.</span>data <span style="color: #408080; font-style: italic">#Feature matrix of 569 rows (samples) and 30 columns (parameters)</span>
outputs<span style="color: #666666">=</span>cancer<span style="color: #666666">.</span>target <span style="color: #408080; font-style: italic">#Label array of 569 rows (0 for benign and 1 for malignant)</span>
labels<span style="color: #666666">=</span>cancer<span style="color: #666666">.</span>feature_names[<span style="color: #666666">0</span>:<span style="color: #666666">30</span>]
<span style="color: #008000">print</span>(<span style="color: #BA2121">&#39;The content of the breast cancer dataset is:&#39;</span>) <span style="color: #408080; font-style: italic">#Print information about the datasets</span>
<span style="color: #008000">print</span>(labels)
<span style="color: #008000">print</span>(<span style="color: #BA2121">&#39;-------------------------&#39;</span>)
<span style="color: #008000">print</span>(<span style="color: #BA2121">&quot;inputs = &quot;</span> <span style="color: #666666">+</span> <span style="color: #008000">str</span>(inputs<span style="color: #666666">.</span>shape))
<span style="color: #008000">print</span>(<span style="color: #BA2121">&quot;outputs = &quot;</span> <span style="color: #666666">+</span> <span style="color: #008000">str</span>(outputs<span style="color: #666666">.</span>shape))
<span style="color: #008000">print</span>(<span style="color: #BA2121">&quot;labels = &quot;</span><span style="color: #666666">+</span> <span style="color: #008000">str</span>(labels<span style="color: #666666">.</span>shape))
x<span style="color: #666666">=</span>inputs <span style="color: #408080; font-style: italic">#Reassign the Feature and Label matrices to other variables</span>
y<span style="color: #666666">=</span>outputs
<span style="color: #408080; font-style: italic">#%% </span>
<span style="color: #408080; font-style: italic"># Visualisation of dataset (for correlation analysis)</span>
plt<span style="color: #666666">.</span>figure()
plt<span style="color: #666666">.</span>scatter(x[:,<span style="color: #666666">0</span>],x[:,<span style="color: #666666">2</span>],s<span style="color: #666666">=40</span>,c<span style="color: #666666">=</span>y,cmap<span style="color: #666666">=</span>plt<span style="color: #666666">.</span>cm<span style="color: #666666">.</span>Spectral)
plt<span style="color: #666666">.</span>xlabel(<span style="color: #BA2121">&#39;Mean radius&#39;</span>,fontweight<span style="color: #666666">=</span><span style="color: #BA2121">&#39;bold&#39;</span>)
plt<span style="color: #666666">.</span>ylabel(<span style="color: #BA2121">&#39;Mean perimeter&#39;</span>,fontweight<span style="color: #666666">=</span><span style="color: #BA2121">&#39;bold&#39;</span>)
plt<span style="color: #666666">.</span>show()
plt<span style="color: #666666">.</span>figure()
plt<span style="color: #666666">.</span>scatter(x[:,<span style="color: #666666">5</span>],x[:,<span style="color: #666666">6</span>],s<span style="color: #666666">=40</span>,c<span style="color: #666666">=</span>y, cmap<span style="color: #666666">=</span>plt<span style="color: #666666">.</span>cm<span style="color: #666666">.</span>Spectral)
plt<span style="color: #666666">.</span>xlabel(<span style="color: #BA2121">&#39;Mean compactness&#39;</span>,fontweight<span style="color: #666666">=</span><span style="color: #BA2121">&#39;bold&#39;</span>)
plt<span style="color: #666666">.</span>ylabel(<span style="color: #BA2121">&#39;Mean concavity&#39;</span>,fontweight<span style="color: #666666">=</span><span style="color: #BA2121">&#39;bold&#39;</span>)
plt<span style="color: #666666">.</span>show()
plt<span style="color: #666666">.</span>figure()
plt<span style="color: #666666">.</span>scatter(x[:,<span style="color: #666666">0</span>],x[:,<span style="color: #666666">1</span>],s<span style="color: #666666">=40</span>,c<span style="color: #666666">=</span>y,cmap<span style="color: #666666">=</span>plt<span style="color: #666666">.</span>cm<span style="color: #666666">.</span>Spectral)
plt<span style="color: #666666">.</span>xlabel(<span style="color: #BA2121">&#39;Mean radius&#39;</span>,fontweight<span style="color: #666666">=</span><span style="color: #BA2121">&#39;bold&#39;</span>)
plt<span style="color: #666666">.</span>ylabel(<span style="color: #BA2121">&#39;Mean texture&#39;</span>,fontweight<span style="color: #666666">=</span><span style="color: #BA2121">&#39;bold&#39;</span>)
plt<span style="color: #666666">.</span>show()
plt<span style="color: #666666">.</span>figure()
plt<span style="color: #666666">.</span>scatter(x[:,<span style="color: #666666">2</span>],x[:,<span style="color: #666666">1</span>],s<span style="color: #666666">=40</span>,c<span style="color: #666666">=</span>y,cmap<span style="color: #666666">=</span>plt<span style="color: #666666">.</span>cm<span style="color: #666666">.</span>Spectral)
plt<span style="color: #666666">.</span>xlabel(<span style="color: #BA2121">&#39;Mean perimeter&#39;</span>,fontweight<span style="color: #666666">=</span><span style="color: #BA2121">&#39;bold&#39;</span>)
plt<span style="color: #666666">.</span>ylabel(<span style="color: #BA2121">&#39;Mean compactness&#39;</span>,fontweight<span style="color: #666666">=</span><span style="color: #BA2121">&#39;bold&#39;</span>)
plt<span style="color: #666666">.</span>show()
<span style="color: #408080; font-style: italic"># Generate training and testing datasets</span>
<span style="color: #408080; font-style: italic">#Select features relevant to classification (texture,perimeter,compactness and symmetery) </span>
<span style="color: #408080; font-style: italic">#and add to input matrix</span>
temp1<span style="color: #666666">=</span>np<span style="color: #666666">.</span>reshape(x[:,<span style="color: #666666">1</span>],(<span style="color: #008000">len</span>(x[:,<span style="color: #666666">1</span>]),<span style="color: #666666">1</span>))
temp2<span style="color: #666666">=</span>np<span style="color: #666666">.</span>reshape(x[:,<span style="color: #666666">2</span>],(<span style="color: #008000">len</span>(x[:,<span style="color: #666666">2</span>]),<span style="color: #666666">1</span>))
X<span style="color: #666666">=</span>np<span style="color: #666666">.</span>hstack((temp1,temp2))
temp<span style="color: #666666">=</span>np<span style="color: #666666">.</span>reshape(x[:,<span style="color: #666666">5</span>],(<span style="color: #008000">len</span>(x[:,<span style="color: #666666">5</span>]),<span style="color: #666666">1</span>))
X<span style="color: #666666">=</span>np<span style="color: #666666">.</span>hstack((X,temp))
temp<span style="color: #666666">=</span>np<span style="color: #666666">.</span>reshape(x[:,<span style="color: #666666">8</span>],(<span style="color: #008000">len</span>(x[:,<span style="color: #666666">8</span>]),<span style="color: #666666">1</span>))
X<span style="color: #666666">=</span>np<span style="color: #666666">.</span>hstack((X,temp))
X_train,X_test,y_train,y_test<span style="color: #666666">=</span>splitter(X,y,test_size<span style="color: #666666">=0.1</span>) <span style="color: #408080; font-style: italic">#Split datasets into training and testing</span>
y_train<span style="color: #666666">=</span>to_categorical(y_train) <span style="color: #408080; font-style: italic">#Convert labels to categorical when using categorical cross entropy</span>
y_test<span style="color: #666666">=</span>to_categorical(y_test)
<span style="color: #008000; font-weight: bold">del</span> temp1,temp2,temp
<span style="color: #408080; font-style: italic"># %%</span>
<span style="color: #408080; font-style: italic"># Define tunable parameters&quot;</span>
eta<span style="color: #666666">=</span>np<span style="color: #666666">.</span>logspace(<span style="color: #666666">-3</span>,<span style="color: #666666">-1</span>,<span style="color: #666666">3</span>) <span style="color: #408080; font-style: italic">#Define vector of learning rates (parameter to SGD optimiser)</span>
lamda<span style="color: #666666">=0.01</span> <span style="color: #408080; font-style: italic">#Define hyperparameter</span>
n_layers<span style="color: #666666">=2</span> <span style="color: #408080; font-style: italic">#Define number of hidden layers in the model</span>
n_neuron<span style="color: #666666">=</span>np<span style="color: #666666">.</span>logspace(<span style="color: #666666">0</span>,<span style="color: #666666">3</span>,<span style="color: #666666">4</span>,dtype<span style="color: #666666">=</span><span style="color: #008000">int</span>) <span style="color: #408080; font-style: italic">#Define number of neurons per layer</span>
epochs<span style="color: #666666">=100</span> <span style="color: #408080; font-style: italic">#Number of reiterations over the input data</span>
batch_size<span style="color: #666666">=100</span> <span style="color: #408080; font-style: italic">#Number of samples per gradient update</span>
<span style="color: #408080; font-style: italic"># %%</span>
<span style="color: #BA2121; font-style: italic">&quot;&quot;&quot;Define function to return Deep Neural Network model&quot;&quot;&quot;</span>
<span style="color: #008000; font-weight: bold">def</span> <span style="color: #0000FF">NN_model</span>(inputsize,n_layers,n_neuron,eta,lamda):
model<span style="color: #666666">=</span>Sequential()
<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>(n_layers): <span style="color: #408080; font-style: italic">#Run loop to add hidden layers to the model</span>
<span style="color: #008000; font-weight: bold">if</span> (i<span style="color: #666666">==0</span>): <span style="color: #408080; font-style: italic">#First layer requires input dimensions</span>
model<span style="color: #666666">.</span>add(Dense(n_neuron,activation<span style="color: #666666">=</span><span style="color: #BA2121">&#39;relu&#39;</span>,kernel_regularizer<span style="color: #666666">=</span>regularizers<span style="color: #666666">.</span>l2(lamda),input_dim<span style="color: #666666">=</span>inputsize))
<span style="color: #008000; font-weight: bold">else</span>: <span style="color: #408080; font-style: italic">#Subsequent layers are capable of automatic shape inferencing</span>
model<span style="color: #666666">.</span>add(Dense(n_neuron,activation<span style="color: #666666">=</span><span style="color: #BA2121">&#39;relu&#39;</span>,kernel_regularizer<span style="color: #666666">=</span>regularizers<span style="color: #666666">.</span>l2(lamda)))
model<span style="color: #666666">.</span>add(Dense(<span style="color: #666666">2</span>,activation<span style="color: #666666">=</span><span style="color: #BA2121">&#39;softmax&#39;</span>)) <span style="color: #408080; font-style: italic">#2 outputs - ordered and disordered (softmax for prob)</span>
sgd<span style="color: #666666">=</span>optimizers<span style="color: #666666">.</span>SGD(lr<span style="color: #666666">=</span>eta)
model<span style="color: #666666">.</span>compile(loss<span style="color: #666666">=</span><span style="color: #BA2121">&#39;categorical_crossentropy&#39;</span>,optimizer<span style="color: #666666">=</span>sgd,metrics<span style="color: #666666">=</span>[<span style="color: #BA2121">&#39;accuracy&#39;</span>])
<span style="color: #008000; font-weight: bold">return</span> model
Train_accuracy<span style="color: #666666">=</span>np<span style="color: #666666">.</span>zeros((<span style="color: #008000">len</span>(n_neuron),<span style="color: #008000">len</span>(eta))) <span style="color: #408080; font-style: italic">#Define matrices to store accuracy scores as a function</span>
Test_accuracy<span style="color: #666666">=</span>np<span style="color: #666666">.</span>zeros((<span style="color: #008000">len</span>(n_neuron),<span style="color: #008000">len</span>(eta))) <span style="color: #408080; font-style: italic">#of learning rate and number of hidden neurons for </span>
<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">len</span>(n_neuron)): <span style="color: #408080; font-style: italic">#run loops over hidden neurons and learning rates to calculate </span>
<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">len</span>(eta)): <span style="color: #408080; font-style: italic">#accuracy scores </span>
DNN_model<span style="color: #666666">=</span>NN_model(X_train<span style="color: #666666">.</span>shape[<span style="color: #666666">1</span>],n_layers,n_neuron[i],eta[j],lamda)
DNN_model<span style="color: #666666">.</span>fit(X_train,y_train,epochs<span style="color: #666666">=</span>epochs,batch_size<span style="color: #666666">=</span>batch_size,verbose<span style="color: #666666">=1</span>)
Train_accuracy[i,j]<span style="color: #666666">=</span>DNN_model<span style="color: #666666">.</span>evaluate(X_train,y_train)[<span style="color: #666666">1</span>]
Test_accuracy[i,j]<span style="color: #666666">=</span>DNN_model<span style="color: #666666">.</span>evaluate(X_test,y_test)[<span style="color: #666666">1</span>]
<span style="color: #008000; font-weight: bold">def</span> <span style="color: #0000FF">plot_data</span>(x,y,data,title<span style="color: #666666">=</span><span style="color: #008000; font-weight: bold">None</span>):
<span style="color: #408080; font-style: italic"># plot results</span>
fontsize<span style="color: #666666">=16</span>
fig <span style="color: #666666">=</span> plt<span style="color: #666666">.</span>figure()
ax <span style="color: #666666">=</span> fig<span style="color: #666666">.</span>add_subplot(<span style="color: #666666">111</span>)
cax <span style="color: #666666">=</span> ax<span style="color: #666666">.</span>matshow(data, interpolation<span style="color: #666666">=</span><span style="color: #BA2121">&#39;nearest&#39;</span>, vmin<span style="color: #666666">=0</span>, vmax<span style="color: #666666">=1</span>)
cbar<span style="color: #666666">=</span>fig<span style="color: #666666">.</span>colorbar(cax)
cbar<span style="color: #666666">.</span>ax<span style="color: #666666">.</span>set_ylabel(<span style="color: #BA2121">&#39;accuracy (%)&#39;</span>,rotation<span style="color: #666666">=90</span>,fontsize<span style="color: #666666">=</span>fontsize)
cbar<span style="color: #666666">.</span>set_ticks([<span style="color: #666666">0</span>,<span style="color: #666666">.2</span>,<span style="color: #666666">.4</span>,<span style="color: #666666">0.6</span>,<span style="color: #666666">0.8</span>,<span style="color: #666666">1.0</span>])
cbar<span style="color: #666666">.</span>set_ticklabels([<span style="color: #BA2121">&#39;0%&#39;</span>,<span style="color: #BA2121">&#39;20%&#39;</span>,<span style="color: #BA2121">&#39;40%&#39;</span>,<span style="color: #BA2121">&#39;60%&#39;</span>,<span style="color: #BA2121">&#39;80%&#39;</span>,<span style="color: #BA2121">&#39;100%&#39;</span>])
<span style="color: #408080; font-style: italic"># put text on matrix elements</span>
<span style="color: #008000; font-weight: bold">for</span> i, x_val <span style="color: #AA22FF; font-weight: bold">in</span> <span style="color: #008000">enumerate</span>(np<span style="color: #666666">.</span>arange(<span style="color: #008000">len</span>(x))):
<span style="color: #008000; font-weight: bold">for</span> j, y_val <span style="color: #AA22FF; font-weight: bold">in</span> <span style="color: #008000">enumerate</span>(np<span style="color: #666666">.</span>arange(<span style="color: #008000">len</span>(y))):
c <span style="color: #666666">=</span> <span style="color: #BA2121">&quot;$</span><span style="color: #BB6688; font-weight: bold">{0:.1f}</span><span style="color: #BB6622; font-weight: bold">\\</span><span style="color: #BA2121">%$&quot;</span><span style="color: #666666">.</span>format( <span style="color: #666666">100*</span>data[j,i])
ax<span style="color: #666666">.</span>text(x_val, y_val, c, va<span style="color: #666666">=</span><span style="color: #BA2121">&#39;center&#39;</span>, ha<span style="color: #666666">=</span><span style="color: #BA2121">&#39;center&#39;</span>)
<span style="color: #408080; font-style: italic"># convert axis vaues to to string labels</span>
x<span style="color: #666666">=</span>[<span style="color: #008000">str</span>(i) <span style="color: #008000; font-weight: bold">for</span> i <span style="color: #AA22FF; font-weight: bold">in</span> x]
y<span style="color: #666666">=</span>[<span style="color: #008000">str</span>(i) <span style="color: #008000; font-weight: bold">for</span> i <span style="color: #AA22FF; font-weight: bold">in</span> y]
ax<span style="color: #666666">.</span>set_xticklabels([<span style="color: #BA2121">&#39;&#39;</span>]<span style="color: #666666">+</span>x)
ax<span style="color: #666666">.</span>set_yticklabels([<span style="color: #BA2121">&#39;&#39;</span>]<span style="color: #666666">+</span>y)
ax<span style="color: #666666">.</span>set_xlabel(<span style="color: #BA2121">&#39;$</span><span style="color: #BB6622; font-weight: bold">\\</span><span style="color: #BA2121">mathrm{learning</span><span style="color: #BB6622; font-weight: bold">\\</span><span style="color: #BA2121"> rate}$&#39;</span>,fontsize<span style="color: #666666">=</span>fontsize)
ax<span style="color: #666666">.</span>set_ylabel(<span style="color: #BA2121">&#39;$</span><span style="color: #BB6622; font-weight: bold">\\</span><span style="color: #BA2121">mathrm{hidden</span><span style="color: #BB6622; font-weight: bold">\\</span><span style="color: #BA2121"> neurons}$&#39;</span>,fontsize<span style="color: #666666">=</span>fontsize)
<span style="color: #008000; font-weight: bold">if</span> title <span style="color: #AA22FF; font-weight: bold">is</span> <span style="color: #AA22FF; font-weight: bold">not</span> <span style="color: #008000; font-weight: bold">None</span>:
ax<span style="color: #666666">.</span>set_title(title)
plt<span style="color: #666666">.</span>tight_layout()
plt<span style="color: #666666">.</span>show()
plot_data(eta,n_neuron,Train_accuracy, <span style="color: #BA2121">&#39;training&#39;</span>)
plot_data(eta,n_neuron,Test_accuracy, <span style="color: #BA2121">&#39;testing&#39;</span>)
</pre></div>
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