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

updating keras

Browse Source
This commit is contained in:
mhjensen
2020-10-09 06:45:29 +02:00
parent 6dd6d78510
commit 4461d2e24b
10 changed files with 152 additions and 132 deletions
Download Patch File Download Diff File Expand all files Collapse all files
doc/pub/week41/html/._week41-bs028.html
+2 -8
View File
@@ -258,7 +258,6 @@ MathJax.Hub.Config({
<p>
Keras is a high level <a href="https://en.wikipedia.org/wiki/Application_programming_interface" target="_self">neural network</a>
that supports Tensorflow, CTNK and Theano as backends.
If you have Tensorflow installed Keras is available through the <em>tf.keras</em> module.
If you have Anaconda installed you may run the following command
<p>
@@ -266,15 +265,10 @@ If you have Anaconda installed you may run the following command
<div class="highlight" style="background: #f8f8f8"><pre style="line-height: 125%"><span></span>conda install keras
</pre></div>
<p>
Alternatively, if you have Tensorflow or one of the other supported backends install you may use the pip package manager:
You can look up the <a href="https://keras.io/" target="_self">instructions here</a> for more information.
<p>
<!-- code=python (!bc pycod) typeset with pygments style "default" -->
<div class="highlight" style="background: #f8f8f8"><pre style="line-height: 125%"><span></span>pip install keras
</pre></div>
<p>
or look up the <a href="https://keras.io/" target="_self">instructions here</a>.
We will to a large extent use <b>keras</b> in this course.
<p>
<p>
doc/pub/week41/html/._week41-bs029.html
+11 -1
View File
@@ -255,6 +255,9 @@ MathJax.Hub.Config({
<h2 id="___sec28" class="anchor">Collect and pre-process data </h2>
<p>
Let us look again at the MINST data set.
<p>
<!-- code=python (!bc pycod) typeset with pygments style "default" -->
@@ -326,7 +329,14 @@ X_train, X_test, Y_train, Y_test <span style="color: #666666">=</span> train_tes
<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">def</span> <span style="color: #0000FF">create_neural_network_keras</span>(n_neurons_layer1, n_neurons_layer2, n_categories, eta, lmbd):
<div class="highlight" style="background: #f8f8f8"><pre style="line-height: 125%"><span></span>epochs <span style="color: #666666">=</span> <span style="color: #666666">100</span>
batch_size <span style="color: #666666">=</span> <span style="color: #666666">100</span>
n_neurons_layer1 <span style="color: #666666">=</span> <span style="color: #666666">100</span>
n_neurons_layer2 <span style="color: #666666">=</span> <span style="color: #666666">50</span>
n_categories <span style="color: #666666">=</span> <span style="color: #666666">10</span>
eta_vals <span style="color: #666666">=</span> np<span style="color: #666666">.</span>logspace(<span style="color: #666666">-5</span>, <span style="color: #666666">1</span>, <span style="color: #666666">7</span>)
lmbd_vals <span style="color: #666666">=</span> np<span style="color: #666666">.</span>logspace(<span style="color: #666666">-5</span>, <span style="color: #666666">1</span>, <span style="color: #666666">7</span>)
<span style="color: #008000; font-weight: bold">def</span> <span style="color: #0000FF">create_neural_network_keras</span>(n_neurons_layer1, n_neurons_layer2, n_categories, eta, lmbd):
model <span style="color: #666666">=</span> Sequential()
model<span style="color: #666666">.</span>add(Dense(n_neurons_layer1, activation<span style="color: #666666">=</span><span style="color: #BA2121">&#39;sigmoid&#39;</span>, kernel_regularizer<span style="color: #666666">=</span>regularizers<span style="color: #666666">.</span>l2(lmbd)))
model<span style="color: #666666">.</span>add(Dense(n_neurons_layer2, activation<span style="color: #666666">=</span><span style="color: #BA2121">&#39;sigmoid&#39;</span>, kernel_regularizer<span style="color: #666666">=</span>regularizers<span style="color: #666666">.</span>l2(lmbd)))
doc/pub/week41/html/._week41-bs057.html
+10 -1
View File
@@ -257,7 +257,16 @@ MathJax.Hub.Config({
<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">keras.utils</span> <span style="color: #008000; font-weight: bold">import</span> to_categorical
<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">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">from</span> <span style="color: #0000FF; font-weight: bold">tensorflow.keras</span> <span style="color: #008000; font-weight: bold">import</span> Conv2D
<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> MaxPooling2D
<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> Flatten
<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"># representation of labels</span>
doc/pub/week41/html/._week41-bs058.html
+1 -9
View File
@@ -258,15 +258,7 @@ MathJax.Hub.Config({
<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">keras.models</span> <span style="color: #008000; font-weight: bold">import</span> Sequential
<span style="color: #008000; font-weight: bold">from</span> <span style="color: #0000FF; font-weight: bold">keras.layers.convolutional</span> <span style="color: #008000; font-weight: bold">import</span> Conv2D
<span style="color: #008000; font-weight: bold">from</span> <span style="color: #0000FF; font-weight: bold">keras.layers.convolutional</span> <span style="color: #008000; font-weight: bold">import</span> MaxPooling2D
<span style="color: #008000; font-weight: bold">from</span> <span style="color: #0000FF; font-weight: bold">keras.layers</span> <span style="color: #008000; font-weight: bold">import</span> Flatten
<span style="color: #008000; font-weight: bold">from</span> <span style="color: #0000FF; font-weight: bold">keras.layers</span> <span style="color: #008000; font-weight: bold">import</span> Dense
<span style="color: #008000; font-weight: bold">from</span> <span style="color: #0000FF; font-weight: bold">keras.regularizers</span> <span style="color: #008000; font-weight: bold">import</span> l2
<span style="color: #008000; font-weight: bold">from</span> <span style="color: #0000FF; font-weight: bold">keras.optimizers</span> <span style="color: #008000; font-weight: bold">import</span> SGD
<span style="color: #008000; font-weight: bold">def</span> <span style="color: #0000FF">create_convolutional_neural_network_keras</span>(input_shape, receptive_field,
<div class="highlight" style="background: #f8f8f8"><pre style="line-height: 125%"><span></span><span style="color: #008000; font-weight: bold">def</span> <span style="color: #0000FF">create_convolutional_neural_network_keras</span>(input_shape, receptive_field,
n_filters, n_neurons_connected, n_categories,
eta, lmbd):
model <span style="color: #666666">=</span> Sequential()
doc/pub/week41/html/week41-reveal.html
+24 -19
View File
@@ -1534,7 +1534,6 @@ conda activate tf-gpu
<p>
Keras is a high level <a href="https://en.wikipedia.org/wiki/Application_programming_interface" target="_blank">neural network</a>
that supports Tensorflow, CTNK and Theano as backends.
If you have Tensorflow installed Keras is available through the <em>tf.keras</em> module.
If you have Anaconda installed you may run the following command
<p>
@@ -1542,21 +1541,19 @@ If you have Anaconda installed you may run the following command
<div class="highlight" style="background: #eeeedd"><pre style="font-size: 80%; line-height: 125%"><span></span>conda install keras
</pre></div>
<p>
Alternatively, if you have Tensorflow or one of the other supported backends install you may use the pip package manager:
You can look up the <a href="https://keras.io/" target="_blank">instructions here</a> for more information.
<p>
<!-- code=python (!bc pycod) typeset with pygments style "perldoc" -->
<div class="highlight" style="background: #eeeedd"><pre style="font-size: 80%; line-height: 125%"><span></span>pip install keras
</pre></div>
<p>
or look up the <a href="https://keras.io/" target="_blank">instructions here</a>.
We will to a large extent use <b>keras</b> in this course.
</section>
<section>
<h2 id="___sec28">Collect and pre-process data </h2>
<p>
Let us look again at the MINST data set.
<p>
<!-- code=python (!bc pycod) typeset with pygments style "perldoc" -->
@@ -1628,7 +1625,14 @@ X_train, X_test, Y_train, Y_test = train_test_split(inputs, labels, train_size=t
<p>
<!-- code=python (!bc pycod) typeset with pygments style "perldoc" -->
<div class="highlight" style="background: #eeeedd"><pre style="font-size: 80%; line-height: 125%"><span></span><span style="color: #8B008B; font-weight: bold">def</span> <span style="color: #008b45">create_neural_network_keras</span>(n_neurons_layer1, n_neurons_layer2, n_categories, eta, lmbd):
<div class="highlight" style="background: #eeeedd"><pre style="font-size: 80%; line-height: 125%"><span></span>epochs = <span style="color: #B452CD">100</span>
batch_size = <span style="color: #B452CD">100</span>
n_neurons_layer1 = <span style="color: #B452CD">100</span>
n_neurons_layer2 = <span style="color: #B452CD">50</span>
n_categories = <span style="color: #B452CD">10</span>
eta_vals = np.logspace(-<span style="color: #B452CD">5</span>, <span style="color: #B452CD">1</span>, <span style="color: #B452CD">7</span>)
lmbd_vals = np.logspace(-<span style="color: #B452CD">5</span>, <span style="color: #B452CD">1</span>, <span style="color: #B452CD">7</span>)
<span style="color: #8B008B; font-weight: bold">def</span> <span style="color: #008b45">create_neural_network_keras</span>(n_neurons_layer1, n_neurons_layer2, n_categories, eta, lmbd):
model = Sequential()
model.add(Dense(n_neurons_layer1, activation=<span style="color: #CD5555">&#39;sigmoid&#39;</span>, kernel_regularizer=regularizers.l2(lmbd)))
model.add(Dense(n_neurons_layer2, activation=<span style="color: #CD5555">&#39;sigmoid&#39;</span>, kernel_regularizer=regularizers.l2(lmbd)))
@@ -2533,7 +2537,16 @@ plt.show()
<p>
<!-- code=python (!bc pycod) typeset with pygments style "perldoc" -->
<div class="highlight" style="background: #eeeedd"><pre style="font-size: 80%; line-height: 125%"><span></span><span style="color: #8B008B; font-weight: bold">from</span> <span style="color: #008b45; text-decoration: underline">keras.utils</span> <span style="color: #8B008B; font-weight: bold">import</span> to_categorical
<div class="highlight" style="background: #eeeedd"><pre style="font-size: 80%; line-height: 125%"><span></span><span style="color: #8B008B; font-weight: bold">from</span> <span style="color: #008b45; text-decoration: underline">tensorflow.keras.layers</span> <span style="color: #8B008B; font-weight: bold">import</span> Input
<span style="color: #8B008B; font-weight: bold">from</span> <span style="color: #008b45; text-decoration: underline">tensorflow.keras.models</span> <span style="color: #8B008B; font-weight: bold">import</span> Sequential <span style="color: #228B22">#This allows appending layers to existing models</span>
<span style="color: #8B008B; font-weight: bold">from</span> <span style="color: #008b45; text-decoration: underline">tensorflow.keras.layers</span> <span style="color: #8B008B; font-weight: bold">import</span> Dense <span style="color: #228B22">#This allows defining the characteristics of a particular layer</span>
<span style="color: #8B008B; font-weight: bold">from</span> <span style="color: #008b45; text-decoration: underline">tensorflow.keras</span> <span style="color: #8B008B; font-weight: bold">import</span> optimizers <span style="color: #228B22">#This allows using whichever optimiser we want (sgd,adam,RMSprop)</span>
<span style="color: #8B008B; font-weight: bold">from</span> <span style="color: #008b45; text-decoration: underline">tensorflow.keras</span> <span style="color: #8B008B; font-weight: bold">import</span> regularizers <span style="color: #228B22">#This allows using whichever regularizer we want (l1,l2,l1_l2)</span>
<span style="color: #8B008B; font-weight: bold">from</span> <span style="color: #008b45; text-decoration: underline">tensorflow.keras.utils</span> <span style="color: #8B008B; font-weight: bold">import</span> to_categorical <span style="color: #228B22">#This allows using categorical cross entropy as the cost function</span>
<span style="color: #8B008B; font-weight: bold">from</span> <span style="color: #008b45; text-decoration: underline">tensorflow.keras</span> <span style="color: #8B008B; font-weight: bold">import</span> Conv2D
<span style="color: #8B008B; font-weight: bold">from</span> <span style="color: #008b45; text-decoration: underline">tensorflow.keras</span> <span style="color: #8B008B; font-weight: bold">import</span> MaxPooling2D
<span style="color: #8B008B; font-weight: bold">from</span> <span style="color: #008b45; text-decoration: underline">tensorflow.keras</span> <span style="color: #8B008B; font-weight: bold">import</span> Flatten
<span style="color: #8B008B; font-weight: bold">from</span> <span style="color: #008b45; text-decoration: underline">sklearn.model_selection</span> <span style="color: #8B008B; font-weight: bold">import</span> train_test_split
<span style="color: #228B22"># representation of labels</span>
@@ -2555,15 +2568,7 @@ X_train, X_test, Y_train, Y_test = train_test_split(inputs, labels, train_size=t
<p>
<!-- code=python (!bc pycod) typeset with pygments style "perldoc" -->
<div class="highlight" style="background: #eeeedd"><pre style="font-size: 80%; line-height: 125%"><span></span><span style="color: #8B008B; font-weight: bold">from</span> <span style="color: #008b45; text-decoration: underline">keras.models</span> <span style="color: #8B008B; font-weight: bold">import</span> Sequential
<span style="color: #8B008B; font-weight: bold">from</span> <span style="color: #008b45; text-decoration: underline">keras.layers.convolutional</span> <span style="color: #8B008B; font-weight: bold">import</span> Conv2D
<span style="color: #8B008B; font-weight: bold">from</span> <span style="color: #008b45; text-decoration: underline">keras.layers.convolutional</span> <span style="color: #8B008B; font-weight: bold">import</span> MaxPooling2D
<span style="color: #8B008B; font-weight: bold">from</span> <span style="color: #008b45; text-decoration: underline">keras.layers</span> <span style="color: #8B008B; font-weight: bold">import</span> Flatten
<span style="color: #8B008B; font-weight: bold">from</span> <span style="color: #008b45; text-decoration: underline">keras.layers</span> <span style="color: #8B008B; font-weight: bold">import</span> Dense
<span style="color: #8B008B; font-weight: bold">from</span> <span style="color: #008b45; text-decoration: underline">keras.regularizers</span> <span style="color: #8B008B; font-weight: bold">import</span> l2
<span style="color: #8B008B; font-weight: bold">from</span> <span style="color: #008b45; text-decoration: underline">keras.optimizers</span> <span style="color: #8B008B; font-weight: bold">import</span> SGD
<span style="color: #8B008B; font-weight: bold">def</span> <span style="color: #008b45">create_convolutional_neural_network_keras</span>(input_shape, receptive_field,
<div class="highlight" style="background: #eeeedd"><pre style="font-size: 80%; line-height: 125%"><span></span><span style="color: #8B008B; font-weight: bold">def</span> <span style="color: #008b45">create_convolutional_neural_network_keras</span>(input_shape, receptive_field,
n_filters, n_neurons_connected, n_categories,
eta, lmbd):
model = Sequential()
doc/pub/week41/html/week41-solarized.html
+24 -19
View File
@@ -1463,7 +1463,6 @@ conda activate tf-gpu
<p>
Keras is a high level <a href="https://en.wikipedia.org/wiki/Application_programming_interface" target="_blank">neural network</a>
that supports Tensorflow, CTNK and Theano as backends.
If you have Tensorflow installed Keras is available through the <em>tf.keras</em> module.
If you have Anaconda installed you may run the following command
<p>
@@ -1471,21 +1470,19 @@ If you have Anaconda installed you may run the following command
<div class="highlight" style="background: #eeeedd"><pre style="line-height: 125%"><span></span>conda install keras
</pre></div>
<p>
Alternatively, if you have Tensorflow or one of the other supported backends install you may use the pip package manager:
You can look up the <a href="https://keras.io/" target="_blank">instructions here</a> for more information.
<p>
<!-- code=python (!bc pycod) typeset with pygments style "perldoc" -->
<div class="highlight" style="background: #eeeedd"><pre style="line-height: 125%"><span></span>pip install keras
</pre></div>
<p>
or look up the <a href="https://keras.io/" target="_blank">instructions here</a>.
We will to a large extent use <b>keras</b> in this course.
<p>
<!-- !split --><br><br><br><br><br><br><br><br><br><br>
<h2 id="___sec28">Collect and pre-process data </h2>
<p>
Let us look again at the MINST data set.
<p>
<!-- code=python (!bc pycod) typeset with pygments style "perldoc" -->
@@ -1557,7 +1554,14 @@ X_train, X_test, Y_train, Y_test = train_test_split(inputs, labels, train_size=t
<p>
<!-- code=python (!bc pycod) typeset with pygments style "perldoc" -->
<div class="highlight" style="background: #eeeedd"><pre style="line-height: 125%"><span></span><span style="color: #8B008B; font-weight: bold">def</span> <span style="color: #008b45">create_neural_network_keras</span>(n_neurons_layer1, n_neurons_layer2, n_categories, eta, lmbd):
<div class="highlight" style="background: #eeeedd"><pre style="line-height: 125%"><span></span>epochs = <span style="color: #B452CD">100</span>
batch_size = <span style="color: #B452CD">100</span>
n_neurons_layer1 = <span style="color: #B452CD">100</span>
n_neurons_layer2 = <span style="color: #B452CD">50</span>
n_categories = <span style="color: #B452CD">10</span>
eta_vals = np.logspace(-<span style="color: #B452CD">5</span>, <span style="color: #B452CD">1</span>, <span style="color: #B452CD">7</span>)
lmbd_vals = np.logspace(-<span style="color: #B452CD">5</span>, <span style="color: #B452CD">1</span>, <span style="color: #B452CD">7</span>)
<span style="color: #8B008B; font-weight: bold">def</span> <span style="color: #008b45">create_neural_network_keras</span>(n_neurons_layer1, n_neurons_layer2, n_categories, eta, lmbd):
model = Sequential()
model.add(Dense(n_neurons_layer1, activation=<span style="color: #CD5555">&#39;sigmoid&#39;</span>, kernel_regularizer=regularizers.l2(lmbd)))
model.add(Dense(n_neurons_layer2, activation=<span style="color: #CD5555">&#39;sigmoid&#39;</span>, kernel_regularizer=regularizers.l2(lmbd)))
@@ -2447,7 +2451,16 @@ plt.show()
<p>
<!-- code=python (!bc pycod) typeset with pygments style "perldoc" -->
<div class="highlight" style="background: #eeeedd"><pre style="line-height: 125%"><span></span><span style="color: #8B008B; font-weight: bold">from</span> <span style="color: #008b45; text-decoration: underline">keras.utils</span> <span style="color: #8B008B; font-weight: bold">import</span> to_categorical
<div class="highlight" style="background: #eeeedd"><pre style="line-height: 125%"><span></span><span style="color: #8B008B; font-weight: bold">from</span> <span style="color: #008b45; text-decoration: underline">tensorflow.keras.layers</span> <span style="color: #8B008B; font-weight: bold">import</span> Input
<span style="color: #8B008B; font-weight: bold">from</span> <span style="color: #008b45; text-decoration: underline">tensorflow.keras.models</span> <span style="color: #8B008B; font-weight: bold">import</span> Sequential <span style="color: #228B22">#This allows appending layers to existing models</span>
<span style="color: #8B008B; font-weight: bold">from</span> <span style="color: #008b45; text-decoration: underline">tensorflow.keras.layers</span> <span style="color: #8B008B; font-weight: bold">import</span> Dense <span style="color: #228B22">#This allows defining the characteristics of a particular layer</span>
<span style="color: #8B008B; font-weight: bold">from</span> <span style="color: #008b45; text-decoration: underline">tensorflow.keras</span> <span style="color: #8B008B; font-weight: bold">import</span> optimizers <span style="color: #228B22">#This allows using whichever optimiser we want (sgd,adam,RMSprop)</span>
<span style="color: #8B008B; font-weight: bold">from</span> <span style="color: #008b45; text-decoration: underline">tensorflow.keras</span> <span style="color: #8B008B; font-weight: bold">import</span> regularizers <span style="color: #228B22">#This allows using whichever regularizer we want (l1,l2,l1_l2)</span>
<span style="color: #8B008B; font-weight: bold">from</span> <span style="color: #008b45; text-decoration: underline">tensorflow.keras.utils</span> <span style="color: #8B008B; font-weight: bold">import</span> to_categorical <span style="color: #228B22">#This allows using categorical cross entropy as the cost function</span>
<span style="color: #8B008B; font-weight: bold">from</span> <span style="color: #008b45; text-decoration: underline">tensorflow.keras</span> <span style="color: #8B008B; font-weight: bold">import</span> Conv2D
<span style="color: #8B008B; font-weight: bold">from</span> <span style="color: #008b45; text-decoration: underline">tensorflow.keras</span> <span style="color: #8B008B; font-weight: bold">import</span> MaxPooling2D
<span style="color: #8B008B; font-weight: bold">from</span> <span style="color: #008b45; text-decoration: underline">tensorflow.keras</span> <span style="color: #8B008B; font-weight: bold">import</span> Flatten
<span style="color: #8B008B; font-weight: bold">from</span> <span style="color: #008b45; text-decoration: underline">sklearn.model_selection</span> <span style="color: #8B008B; font-weight: bold">import</span> train_test_split
<span style="color: #228B22"># representation of labels</span>
@@ -2468,15 +2481,7 @@ X_train, X_test, Y_train, Y_test = train_test_split(inputs, labels, train_size=t
<p>
<!-- code=python (!bc pycod) typeset with pygments style "perldoc" -->
<div class="highlight" style="background: #eeeedd"><pre style="line-height: 125%"><span></span><span style="color: #8B008B; font-weight: bold">from</span> <span style="color: #008b45; text-decoration: underline">keras.models</span> <span style="color: #8B008B; font-weight: bold">import</span> Sequential
<span style="color: #8B008B; font-weight: bold">from</span> <span style="color: #008b45; text-decoration: underline">keras.layers.convolutional</span> <span style="color: #8B008B; font-weight: bold">import</span> Conv2D
<span style="color: #8B008B; font-weight: bold">from</span> <span style="color: #008b45; text-decoration: underline">keras.layers.convolutional</span> <span style="color: #8B008B; font-weight: bold">import</span> MaxPooling2D
<span style="color: #8B008B; font-weight: bold">from</span> <span style="color: #008b45; text-decoration: underline">keras.layers</span> <span style="color: #8B008B; font-weight: bold">import</span> Flatten
<span style="color: #8B008B; font-weight: bold">from</span> <span style="color: #008b45; text-decoration: underline">keras.layers</span> <span style="color: #8B008B; font-weight: bold">import</span> Dense
<span style="color: #8B008B; font-weight: bold">from</span> <span style="color: #008b45; text-decoration: underline">keras.regularizers</span> <span style="color: #8B008B; font-weight: bold">import</span> l2
<span style="color: #8B008B; font-weight: bold">from</span> <span style="color: #008b45; text-decoration: underline">keras.optimizers</span> <span style="color: #8B008B; font-weight: bold">import</span> SGD
<span style="color: #8B008B; font-weight: bold">def</span> <span style="color: #008b45">create_convolutional_neural_network_keras</span>(input_shape, receptive_field,
<div class="highlight" style="background: #eeeedd"><pre style="line-height: 125%"><span></span><span style="color: #8B008B; font-weight: bold">def</span> <span style="color: #008b45">create_convolutional_neural_network_keras</span>(input_shape, receptive_field,
n_filters, n_neurons_connected, n_categories,
eta, lmbd):
model = Sequential()
doc/pub/week41/html/week41.html
+24 -19
View File
@@ -1468,7 +1468,6 @@ conda activate tf<span style="color: #666666">-</span>gpu
<p>
Keras is a high level <a href="https://en.wikipedia.org/wiki/Application_programming_interface" target="_blank">neural network</a>
that supports Tensorflow, CTNK and Theano as backends.
If you have Tensorflow installed Keras is available through the <em>tf.keras</em> module.
If you have Anaconda installed you may run the following command
<p>
@@ -1476,21 +1475,19 @@ If you have Anaconda installed you may run the following command
<div class="highlight" style="background: #f8f8f8"><pre style="line-height: 125%"><span></span>conda install keras
</pre></div>
<p>
Alternatively, if you have Tensorflow or one of the other supported backends install you may use the pip package manager:
You can look up the <a href="https://keras.io/" target="_blank">instructions here</a> for more information.
<p>
<!-- code=python (!bc pycod) typeset with pygments style "default" -->
<div class="highlight" style="background: #f8f8f8"><pre style="line-height: 125%"><span></span>pip install keras
</pre></div>
<p>
or look up the <a href="https://keras.io/" target="_blank">instructions here</a>.
We will to a large extent use <b>keras</b> in this course.
<p>
<!-- !split --><br><br><br><br><br><br><br><br><br><br>
<h2 id="___sec28">Collect and pre-process data </h2>
<p>
Let us look again at the MINST data set.
<p>
<!-- code=python (!bc pycod) typeset with pygments style "default" -->
@@ -1562,7 +1559,14 @@ X_train, X_test, Y_train, Y_test <span style="color: #666666">=</span> train_tes
<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">def</span> <span style="color: #0000FF">create_neural_network_keras</span>(n_neurons_layer1, n_neurons_layer2, n_categories, eta, lmbd):
<div class="highlight" style="background: #f8f8f8"><pre style="line-height: 125%"><span></span>epochs <span style="color: #666666">=</span> <span style="color: #666666">100</span>
batch_size <span style="color: #666666">=</span> <span style="color: #666666">100</span>
n_neurons_layer1 <span style="color: #666666">=</span> <span style="color: #666666">100</span>
n_neurons_layer2 <span style="color: #666666">=</span> <span style="color: #666666">50</span>
n_categories <span style="color: #666666">=</span> <span style="color: #666666">10</span>
eta_vals <span style="color: #666666">=</span> np<span style="color: #666666">.</span>logspace(<span style="color: #666666">-5</span>, <span style="color: #666666">1</span>, <span style="color: #666666">7</span>)
lmbd_vals <span style="color: #666666">=</span> np<span style="color: #666666">.</span>logspace(<span style="color: #666666">-5</span>, <span style="color: #666666">1</span>, <span style="color: #666666">7</span>)
<span style="color: #008000; font-weight: bold">def</span> <span style="color: #0000FF">create_neural_network_keras</span>(n_neurons_layer1, n_neurons_layer2, n_categories, eta, lmbd):
model <span style="color: #666666">=</span> Sequential()
model<span style="color: #666666">.</span>add(Dense(n_neurons_layer1, activation<span style="color: #666666">=</span><span style="color: #BA2121">&#39;sigmoid&#39;</span>, kernel_regularizer<span style="color: #666666">=</span>regularizers<span style="color: #666666">.</span>l2(lmbd)))
model<span style="color: #666666">.</span>add(Dense(n_neurons_layer2, activation<span style="color: #666666">=</span><span style="color: #BA2121">&#39;sigmoid&#39;</span>, kernel_regularizer<span style="color: #666666">=</span>regularizers<span style="color: #666666">.</span>l2(lmbd)))
@@ -2452,7 +2456,16 @@ plt<span style="color: #666666">.</span>show()
<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">keras.utils</span> <span style="color: #008000; font-weight: bold">import</span> to_categorical
<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">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">from</span> <span style="color: #0000FF; font-weight: bold">tensorflow.keras</span> <span style="color: #008000; font-weight: bold">import</span> Conv2D
<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> MaxPooling2D
<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> Flatten
<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"># representation of labels</span>
@@ -2473,15 +2486,7 @@ X_train, X_test, Y_train, Y_test <span style="color: #666666">=</span> train_tes
<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">keras.models</span> <span style="color: #008000; font-weight: bold">import</span> Sequential
<span style="color: #008000; font-weight: bold">from</span> <span style="color: #0000FF; font-weight: bold">keras.layers.convolutional</span> <span style="color: #008000; font-weight: bold">import</span> Conv2D
<span style="color: #008000; font-weight: bold">from</span> <span style="color: #0000FF; font-weight: bold">keras.layers.convolutional</span> <span style="color: #008000; font-weight: bold">import</span> MaxPooling2D
<span style="color: #008000; font-weight: bold">from</span> <span style="color: #0000FF; font-weight: bold">keras.layers</span> <span style="color: #008000; font-weight: bold">import</span> Flatten
<span style="color: #008000; font-weight: bold">from</span> <span style="color: #0000FF; font-weight: bold">keras.layers</span> <span style="color: #008000; font-weight: bold">import</span> Dense
<span style="color: #008000; font-weight: bold">from</span> <span style="color: #0000FF; font-weight: bold">keras.regularizers</span> <span style="color: #008000; font-weight: bold">import</span> l2
<span style="color: #008000; font-weight: bold">from</span> <span style="color: #0000FF; font-weight: bold">keras.optimizers</span> <span style="color: #008000; font-weight: bold">import</span> SGD
<span style="color: #008000; font-weight: bold">def</span> <span style="color: #0000FF">create_convolutional_neural_network_keras</span>(input_shape, receptive_field,
<div class="highlight" style="background: #f8f8f8"><pre style="line-height: 125%"><span></span><span style="color: #008000; font-weight: bold">def</span> <span style="color: #0000FF">create_convolutional_neural_network_keras</span>(input_shape, receptive_field,
n_filters, n_neurons_connected, n_categories,
eta, lmbd):
model <span style="color: #666666">=</span> Sequential()
doc/pub/week41/ipynb/ipynb-week41-src.tar.gz
BIN
View File
Binary file not shown.
doc/pub/week41/ipynb/week41.ipynb
+34 -40
View File
@@ -1465,7 +1465,6 @@
"\n",
"Keras is a high level [neural network](https://en.wikipedia.org/wiki/Application_programming_interface)\n",
"that supports Tensorflow, CTNK and Theano as backends. \n",
"If you have Tensorflow installed Keras is available through the *tf.keras* module. \n",
"If you have Anaconda installed you may run the following command"
]
},
@@ -1484,7 +1483,13 @@
"cell_type": "markdown",
"metadata": {},
"source": [
"Alternatively, if you have Tensorflow or one of the other supported backends install you may use the pip package manager:"
"You can look up the [instructions here](https://keras.io/) for more information.\n",
"\n",
"We will to a large extent use **keras** in this course. \n",
"\n",
"## Collect and pre-process data\n",
"\n",
"Let us look again at the MINST data set."
]
},
{
@@ -1494,27 +1499,6 @@
"collapsed": false
},
"outputs": [],
"source": [
"pip install keras"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"or look up the [instructions here](https://keras.io/).\n",
"\n",
"\n",
"## Collect and pre-process data"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# import necessary packages\n",
"import numpy as np\n",
@@ -1563,7 +1547,7 @@
},
{
"cell_type": "code",
"execution_count": 18,
"execution_count": 17,
"metadata": {
"collapsed": false
},
@@ -1590,13 +1574,20 @@
},
{
"cell_type": "code",
"execution_count": 19,
"execution_count": 18,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"\n",
"epochs = 100\n",
"batch_size = 100\n",
"n_neurons_layer1 = 100\n",
"n_neurons_layer2 = 50\n",
"n_categories = 10\n",
"eta_vals = np.logspace(-5, 1, 7)\n",
"lmbd_vals = np.logspace(-5, 1, 7)\n",
"def create_neural_network_keras(n_neurons_layer1, n_neurons_layer2, n_categories, eta, lmbd):\n",
" model = Sequential()\n",
" model.add(Dense(n_neurons_layer1, activation='sigmoid', kernel_regularizer=regularizers.l2(lmbd)))\n",
@@ -1611,7 +1602,7 @@
},
{
"cell_type": "code",
"execution_count": 20,
"execution_count": 19,
"metadata": {
"collapsed": false
},
@@ -1636,7 +1627,7 @@
},
{
"cell_type": "code",
"execution_count": 21,
"execution_count": 20,
"metadata": {
"collapsed": false
},
@@ -1684,7 +1675,7 @@
},
{
"cell_type": "code",
"execution_count": 22,
"execution_count": 21,
"metadata": {
"collapsed": false
},
@@ -2400,7 +2391,7 @@
},
{
"cell_type": "code",
"execution_count": 23,
"execution_count": 22,
"metadata": {
"collapsed": false
},
@@ -2457,13 +2448,22 @@
},
{
"cell_type": "code",
"execution_count": 24,
"execution_count": 23,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"from keras.utils import to_categorical\n",
"from tensorflow.keras.layers import Input\n",
"from tensorflow.keras.models import Sequential #This allows appending layers to existing models\n",
"from tensorflow.keras.layers import Dense #This allows defining the characteristics of a particular layer\n",
"from tensorflow.keras import optimizers #This allows using whichever optimiser we want (sgd,adam,RMSprop)\n",
"from tensorflow.keras import regularizers #This allows using whichever regularizer we want (l1,l2,l1_l2)\n",
"from tensorflow.keras.utils import to_categorical #This allows using categorical cross entropy as the cost function\n",
"from tensorflow.keras import Conv2D\n",
"from tensorflow.keras import MaxPooling2D\n",
"from tensorflow.keras import Flatten\n",
"\n",
"from sklearn.model_selection import train_test_split\n",
"\n",
"# representation of labels\n",
@@ -2487,19 +2487,13 @@
},
{
"cell_type": "code",
"execution_count": 25,
"execution_count": 24,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"from keras.models import Sequential\n",
"from keras.layers.convolutional import Conv2D\n",
"from keras.layers.convolutional import MaxPooling2D\n",
"from keras.layers import Flatten\n",
"from keras.layers import Dense\n",
"from keras.regularizers import l2\n",
"from keras.optimizers import SGD\n",
"\n",
"\n",
"def create_convolutional_neural_network_keras(input_shape, receptive_field,\n",
" n_filters, n_neurons_connected, n_categories,\n",
@@ -2538,7 +2532,7 @@
},
{
"cell_type": "code",
"execution_count": 26,
"execution_count": 25,
"metadata": {
"collapsed": false
},
doc/src/week41/week41.do.txt
+22 -16
View File
@@ -1116,23 +1116,19 @@ conda activate tf-gpu
Keras is a high level "neural network":"https://en.wikipedia.org/wiki/Application_programming_interface"
that supports Tensorflow, CTNK and Theano as backends.
If you have Tensorflow installed Keras is available through the *tf.keras* module.
If you have Anaconda installed you may run the following command
!bc pycod
conda install keras
!ec
Alternatively, if you have Tensorflow or one of the other supported backends install you may use the pip package manager:
!bc pycod
pip install keras
!ec
or look up the "instructions here":"https://keras.io/".
You can look up the "instructions here":"https://keras.io/" for more information.
We will to a large extent use _keras_ in this course.
!split
===== Collect and pre-process data =====
Let us look again at the MINST data set.
!bc pycod
# import necessary packages
import numpy as np
@@ -1203,6 +1199,13 @@ X_train, X_test, Y_train, Y_test = train_test_split(inputs, labels, train_size=t
!bc pycod
epochs = 100
batch_size = 100
n_neurons_layer1 = 100
n_neurons_layer2 = 50
n_categories = 10
eta_vals = np.logspace(-5, 1, 7)
lmbd_vals = np.logspace(-5, 1, 7)
def create_neural_network_keras(n_neurons_layer1, n_neurons_layer2, n_categories, eta, lmbd):
model = Sequential()
model.add(Dense(n_neurons_layer1, activation='sigmoid', kernel_regularizer=regularizers.l2(lmbd)))
@@ -2005,7 +2008,16 @@ plt.show()
!split
===== Importing Keras and Tensorflow =====
!bc pycod
from keras.utils import to_categorical
from tensorflow.keras.layers import Input
from tensorflow.keras.models import Sequential #This allows appending layers to existing models
from tensorflow.keras.layers import Dense #This allows defining the characteristics of a particular layer
from tensorflow.keras import optimizers #This allows using whichever optimiser we want (sgd,adam,RMSprop)
from tensorflow.keras import regularizers #This allows using whichever regularizer we want (l1,l2,l1_l2)
from tensorflow.keras.utils import to_categorical #This allows using categorical cross entropy as the cost function
from tensorflow.keras import Conv2D
from tensorflow.keras import MaxPooling2D
from tensorflow.keras import Flatten
from sklearn.model_selection import train_test_split
# representation of labels
@@ -2023,13 +2035,7 @@ X_train, X_test, Y_train, Y_test = train_test_split(inputs, labels, train_size=t
===== Running with Keras =====
!bc pycod
from keras.models import Sequential
from keras.layers.convolutional import Conv2D
from keras.layers.convolutional import MaxPooling2D
from keras.layers import Flatten
from keras.layers import Dense
from keras.regularizers import l2
from keras.optimizers import SGD
def create_convolutional_neural_network_keras(input_shape, receptive_field,
n_filters, n_neurons_connected, n_categories,