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="._NeuralNet-bs050.html#___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="._NeuralNet-bs050.html#___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>
@@ -246,28 +247,28 @@ and obtain a matrix that holds the weighted sum of inputs to the hidden layer
 for each input image and each hidden neuron.    
 We also add the bias to obtain a matrix of weighted sums to the hidden layer \( Z^{h} \):  

-$$ Z^{h} = X W^{h} + B^{h} ,$$
+$$ \hat{z}^{l} = \hat{X} \hat{W}^{l} + \hat{b}^{l} ,$$

 <p>
 meaning the same bias (1D array with size equal number of hidden neurons) is added to each input image.  
 This is then passed through the activation:  

-$$ A^{h} = f(Z^h) .$$
+$$ \hat{a}^{l} = f(\hat{z}^l) .$$

 <p>
 This is fed to the output layer:  

-$$ Z^{o} = A^{h} W^{o} + B^{o} .$$
+$$ \hat{z}^{L} = \hat{a}^{L} \hat{W}^{L} + \hat{b}^{L} .$$

 <p>
 Finally we receive our output values for each image and each category by passing it through the softmax function:  

-$$ output = softmax (Z^{o}) = (n_{inputs}, n_{categories}) .$$
+$$ output = softmax (\hat{z}^{L}) = (n_{inputs}, n_{categories}) .$$

 <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: #408080; font-style: italic"># setup the feed-forward pass</span>
+<div class="highlight" style="background: #f8f8f8"><pre style="line-height: 125%"><span></span><span style="color: #408080; font-style: italic"># setup the feed-forward pass, subscript h = hidden layer</span>

 <span style="color: #008000; font-weight: bold">def</span> <span style="color: #0000FF">sigmoid</span>(x):
    <span style="color: #008000; font-weight: bold">return</span> <span style="color: #666666">1/</span>(<span style="color: #666666">1</span> <span style="color: #666666">+</span> np<span style="color: #666666">.</span>exp(<span style="color: #666666">-</span>x))
@@ -329,7 +330,7 @@ predictions <span style="color: #666666">=</span> predict(X_train)
  <li><a href="._NeuralNet-bs048.html">49</a></li>
  <li><a href="._NeuralNet-bs049.html">50</a></li>
  <li><a href="">...</a></li>
-  <li><a href="._NeuralNet-bs055.html">56</a></li>
+  <li><a href="._NeuralNet-bs057.html">58</a></li>
  <li><a href="._NeuralNet-bs041.html">&raquo;</a></li>
 </ul>
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