lars/FYS-STK4155
1
0
Fork 0
Code Issues Pull Requests Actions Packages Projects Releases Wiki Activity

updated typos

Browse Source
This commit is contained in:
mhjensen
2018-10-04 05:35:51 +02:00
parent ff08e04b6e
commit 0c44eedada
64 changed files with 1948 additions and 2013 deletions
Download Patch File Download Diff File Expand all files Collapse all files
doc/pub/NeuralNet/html/._NeuralNet-bs052.html
+29 -77
View File
@@ -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="#___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="#___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>
@@ -231,69 +232,18 @@ MathJax.Hub.Config({
<a name="part0052"></a>
<!-- !split -->
<h2 id="___sec51" class="anchor">Collect and pre-process data </h2>
<h2 id="___sec51" class="anchor">Building neural networks in Tensorflow and Keras </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.
<!-- 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"># import necessary packages</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">from</span> <span style="color: #0000FF; font-weight: bold">sklearn</span> <span style="color: #008000; font-weight: bold">import</span> datasets
<span style="color: #408080; font-style: italic"># ensure the same random numbers appear every time</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"># display images in notebook</span>
<span style="color: #666666">%</span>matplotlib inline
plt<span style="color: #666666">.</span>rcParams[<span style="color: #BA2121">&#39;figure.figsize&#39;</span>] <span style="color: #666666">=</span> (<span style="color: #666666">12</span>,<span style="color: #666666">12</span>)
<span style="color: #408080; font-style: italic"># download MNIST dataset</span>
digits <span style="color: #666666">=</span> datasets<span style="color: #666666">.</span>load_digits()
<span style="color: #408080; font-style: italic"># define inputs and labels</span>
inputs <span style="color: #666666">=</span> digits<span style="color: #666666">.</span>images
labels <span style="color: #666666">=</span> digits<span style="color: #666666">.</span>target
<span style="color: #008000; font-weight: bold">print</span>(<span style="color: #BA2121">&quot;inputs = (n_inputs, pixel_width, pixel_height) = &quot;</span> <span style="color: #666666">+</span> <span style="color: #008000">str</span>(inputs<span style="color: #666666">.</span>shape))
<span style="color: #008000; font-weight: bold">print</span>(<span style="color: #BA2121">&quot;labels = (n_inputs) = &quot;</span> <span style="color: #666666">+</span> <span style="color: #008000">str</span>(labels<span style="color: #666666">.</span>shape))
<span style="color: #408080; font-style: italic"># flatten the image</span>
<span style="color: #408080; font-style: italic"># the value -1 means dimension is inferred from the remaining dimensions: 8x8 = 64</span>
n_inputs <span style="color: #666666">=</span> <span style="color: #008000">len</span>(inputs)
inputs <span style="color: #666666">=</span> inputs<span style="color: #666666">.</span>reshape(n_inputs, <span style="color: #666666">-1</span>)
<span style="color: #008000; font-weight: bold">print</span>(<span style="color: #BA2121">&quot;X = (n_inputs, n_features) = &quot;</span> <span style="color: #666666">+</span> <span style="color: #008000">str</span>(inputs<span style="color: #666666">.</span>shape))
<span style="color: #408080; font-style: italic"># choose some random images to display</span>
indices <span style="color: #666666">=</span> np<span style="color: #666666">.</span>arange(n_inputs)
random_indices <span style="color: #666666">=</span> np<span style="color: #666666">.</span>random<span style="color: #666666">.</span>choice(indices, size<span style="color: #666666">=5</span>)
<span style="color: #008000; font-weight: bold">for</span> i, image <span style="color: #AA22FF; font-weight: bold">in</span> <span style="color: #008000">enumerate</span>(digits<span style="color: #666666">.</span>images[random_indices]):
plt<span style="color: #666666">.</span>subplot(<span style="color: #666666">1</span>, <span style="color: #666666">5</span>, i<span style="color: #666666">+1</span>)
plt<span style="color: #666666">.</span>axis(<span style="color: #BA2121">&#39;off&#39;</span>)
plt<span style="color: #666666">.</span>imshow(image, cmap<span style="color: #666666">=</span>plt<span style="color: #666666">.</span>cm<span style="color: #666666">.</span>gray_r, interpolation<span style="color: #666666">=</span><span style="color: #BA2121">&#39;nearest&#39;</span>)
plt<span style="color: #666666">.</span>title(<span style="color: #BA2121">&quot;Label: </span><span style="color: #BB6688; font-weight: bold">%d</span><span style="color: #BA2121">&quot;</span> <span style="color: #666666">%</span> digits<span style="color: #666666">.</span>target[random_indices[i]])
plt<span style="color: #666666">.</span>show()
</pre></div>
<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.
<!-- 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">keras.utils</span> <span style="color: #008000; font-weight: bold">import</span> to_categorical
<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: #408080; font-style: italic"># one-hot representation of labels</span>
labels <span style="color: #666666">=</span> to_categorical(labels)
<span style="color: #408080; font-style: italic"># split into train and test data</span>
train_size <span style="color: #666666">=</span> <span style="color: #666666">0.8</span>
test_size <span style="color: #666666">=</span> <span style="color: #666666">1</span> <span style="color: #666666">-</span> train_size
X_train, X_test, Y_train, Y_test <span style="color: #666666">=</span> train_test_split(inputs, labels, train_size<span style="color: #666666">=</span>train_size,
test_size<span style="color: #666666">=</span>test_size)
</pre></div>
<p>
<p>
<!-- navigation buttons at the bottom of the page -->
@@ -313,6 +263,8 @@ X_train, X_test, Y_train, Y_test <span style="color: #666666">=</span> train_tes
<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-bs053.html">&raquo;</a></li>
</ul>
<!-- ------------------- end of main content --------------- -->