updated typos

2018-10-04 05:35:51 +02:00
parent ff08e04b6e
commit 0c44eedada
64 changed files with 1948 additions and 2013 deletions
@@ -110,20 +110,19 @@ Automatically generated HTML file from DocOnce source
              ('Improving performance', 2, None, '___sec44'),
              ('Full object-oriented implementation', 2, None, '___sec45'),
              ('Evaluate model performance on test data', 2, None, '___sec46'),
-              ('Adjust hyperparameters (if necessary, network architecture',
-               2,
-               None,
-               '___sec47'),
-              ('scikit-learn implementation', 2, None, '___sec48'),
+              ('Adjust hyperparameters', 2, None, '___sec47'),
+              ('Visualization', 2, None, '___sec48'),
+              ('scikit-learn implementation', 2, None, '___sec49'),
+              ('Visualization', 2, None, '___sec50'),
              ('Building neural networks in Tensorflow and Keras',
               2,
               None,
-               '___sec49'),
-              ('Tensorflow', 2, None, '___sec50'),
-              ('Collect and pre-process data', 2, None, '___sec51'),
-              ('Using TensorFlow backend', 2, None, '___sec52'),
-              ('Optimizing and using gradient descent', 2, None, '___sec53'),
-              ('Using Keras', 2, None, '___sec54')]}
+               '___sec51'),
+              ('Tensorflow', 2, None, '___sec52'),
+              ('Collect and pre-process data', 2, None, '___sec53'),
+              ('Using TensorFlow backend', 2, None, '___sec54'),
+              ('Optimizing and using gradient descent', 2, None, '___sec55'),
+              ('Using Keras', 2, None, '___sec56')]}
 end of tocinfo -->

 <body>
@@ -208,14 +207,16 @@ MathJax.Hub.Config({
     <!-- navigation toc: --> <li><a href="._NeuralNet-bs045.html#___sec44" style="font-size: 80%;"><b>Improving performance</b></a></li>
     <!-- navigation toc: --> <li><a href="._NeuralNet-bs046.html#___sec45" style="font-size: 80%;"><b>Full object-oriented implementation</b></a></li>
     <!-- navigation toc: --> <li><a href="._NeuralNet-bs047.html#___sec46" style="font-size: 80%;"><b>Evaluate model performance on test data</b></a></li>
-     <!-- navigation toc: --> <li><a href="._NeuralNet-bs048.html#___sec47" style="font-size: 80%;"><b>Adjust hyperparameters (if necessary, network architecture</b></a></li>
-     <!-- navigation toc: --> <li><a href="._NeuralNet-bs049.html#___sec48" style="font-size: 80%;"><b>scikit-learn implementation</b></a></li>
-     <!-- navigation toc: --> <li><a href="#___sec49" style="font-size: 80%;"><b>Building neural networks in Tensorflow and Keras</b></a></li>
-     <!-- navigation toc: --> <li><a href="._NeuralNet-bs051.html#___sec50" style="font-size: 80%;"><b>Tensorflow</b></a></li>
-     <!-- navigation toc: --> <li><a href="._NeuralNet-bs052.html#___sec51" style="font-size: 80%;"><b>Collect and pre-process data</b></a></li>
-     <!-- navigation toc: --> <li><a href="._NeuralNet-bs053.html#___sec52" style="font-size: 80%;"><b>Using TensorFlow backend</b></a></li>
-     <!-- navigation toc: --> <li><a href="._NeuralNet-bs054.html#___sec53" style="font-size: 80%;"><b>Optimizing and using gradient descent</b></a></li>
-     <!-- navigation toc: --> <li><a href="._NeuralNet-bs055.html#___sec54" style="font-size: 80%;"><b>Using Keras</b></a></li>
+     <!-- navigation toc: --> <li><a href="._NeuralNet-bs048.html#___sec47" style="font-size: 80%;"><b>Adjust hyperparameters</b></a></li>
+     <!-- navigation toc: --> <li><a href="._NeuralNet-bs049.html#___sec48" style="font-size: 80%;"><b>Visualization</b></a></li>
+     <!-- navigation toc: --> <li><a href="#___sec49" style="font-size: 80%;"><b>scikit-learn implementation</b></a></li>
+     <!-- navigation toc: --> <li><a href="._NeuralNet-bs051.html#___sec50" style="font-size: 80%;"><b>Visualization</b></a></li>
+     <!-- navigation toc: --> <li><a href="._NeuralNet-bs052.html#___sec51" style="font-size: 80%;"><b>Building neural networks in Tensorflow and Keras</b></a></li>
+     <!-- navigation toc: --> <li><a href="._NeuralNet-bs053.html#___sec52" style="font-size: 80%;"><b>Tensorflow</b></a></li>
+     <!-- navigation toc: --> <li><a href="._NeuralNet-bs054.html#___sec53" style="font-size: 80%;"><b>Collect and pre-process data</b></a></li>
+     <!-- navigation toc: --> <li><a href="._NeuralNet-bs055.html#___sec54" style="font-size: 80%;"><b>Using TensorFlow backend</b></a></li>
+     <!-- navigation toc: --> <li><a href="._NeuralNet-bs056.html#___sec55" style="font-size: 80%;"><b>Optimizing and using gradient descent</b></a></li>
+     <!-- navigation toc: --> <li><a href="._NeuralNet-bs057.html#___sec56" style="font-size: 80%;"><b>Using Keras</b></a></li>

        </ul>
      </li>
@@ -231,18 +232,42 @@ MathJax.Hub.Config({
 <a name="part0050"></a>
 <!-- !split -->

-<h2 id="___sec49" class="anchor">Building neural networks in Tensorflow and Keras </h2>
+<h2 id="___sec49" class="anchor">scikit-learn implementation </h2>

 <p>
-Now we want  to build on the experience gained from our neural network implementation in NumPy and scikit-learn
-and use it to construct a neural network in Tensorflow. Once we have constructed a neural network in NumPy
-and Tensorflow, building one in Keras is really quite trivial, though the performance may suffer.
+<b>scikit-learn</b> focuses more
+on traditional machine learning methods, such as regression,
+clustering, decision trees, etc. As such, it has only two types of
+neural networks: Multi Layer Perceptron outputting continuous values,
+<em>MPLRegressor</em>, and Multi Layer Perceptron outputting labels,
+<em>MLPClassifier</em>. We will see how simple it is to use these classes.

 <p>
-In our previous example we used only one hidden layer, and in this we will use two. From this it should be quite
-clear how to build one using an arbitrary number of hidden layers, using data structures such as Python lists or
-NumPy arrays.
+<b>scikit-learn</b> implements a few improvements from our neural network,
+such as early stopping, a varying learning rate, different
+optimization methods, etc. We would therefore expect a better
+performance overall.

+<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">from</span> <span style="color: #0000FF; font-weight: bold">sklearn.neural_network</span> <span style="color: #008000; font-weight: bold">import</span> MLPClassifier
+<span style="color: #408080; font-style: italic"># store models for later use</span>
+DNN_scikit <span style="color: #666666">=</span> np<span style="color: #666666">.</span>zeros((<span style="color: #008000">len</span>(eta_vals), <span style="color: #008000">len</span>(lmbd_vals)), dtype<span style="color: #666666">=</span><span style="color: #008000">object</span>)
+
+<span style="color: #008000; font-weight: bold">for</span> i, eta <span style="color: #AA22FF; font-weight: bold">in</span> <span style="color: #008000">enumerate</span>(eta_vals):
+    <span style="color: #008000; font-weight: bold">for</span> j, lmbd <span style="color: #AA22FF; font-weight: bold">in</span> <span style="color: #008000">enumerate</span>(lmbd_vals):
+        dnn <span style="color: #666666">=</span> MLPClassifier(hidden_layer_sizes<span style="color: #666666">=</span>(n_hidden_neurons), activation<span style="color: #666666">=</span><span style="color: #BA2121">&#39;logistic&#39;</span>,
+                            alpha<span style="color: #666666">=</span>lmbd, learning_rate_init<span style="color: #666666">=</span>eta, max_iter<span style="color: #666666">=</span>epochs)
+        dnn<span style="color: #666666">.</span>fit(X_train, Y_train)
+        
+        DNN_scikit[i][j] <span style="color: #666666">=</span> dnn
+        
+        <span style="color: #008000; font-weight: bold">print</span>(<span style="color: #BA2121">&quot;Learning rate  = &quot;</span>, eta)
+        <span style="color: #008000; font-weight: bold">print</span>(<span style="color: #BA2121">&quot;Lambda = &quot;</span>, lmbd)
+        <span style="color: #008000; font-weight: bold">print</span>(<span style="color: #BA2121">&quot;Accuracy score on test set: &quot;</span>, dnn<span style="color: #666666">.</span>score(X_test, Y_test))
+        <span style="color: #008000; font-weight: bold">print</span>()
+</pre></div>
 <p>
 <p>
 <!-- navigation buttons at the bottom of the page -->
@@ -264,6 +289,8 @@ NumPy arrays.
  <li><a href="._NeuralNet-bs053.html">54</a></li>
  <li><a href="._NeuralNet-bs054.html">55</a></li>
  <li><a href="._NeuralNet-bs055.html">56</a></li>
+  <li><a href="._NeuralNet-bs056.html">57</a></li>
+  <li><a href="._NeuralNet-bs057.html">58</a></li>
  <li><a href="._NeuralNet-bs051.html">&raquo;</a></li>
 </ul>
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