updating keras
This commit is contained in:
mhjensen
@@ -259,15 +259,16 @@ MathJax.Hub.Config({
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<p>
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<!-- code=python (!bc pycod) typeset with pygments style "default" -->
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<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
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<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</span> <span style="color: #008000; font-weight: bold">import</span> datasets, layers, models
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<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
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<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>
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<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>
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<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>
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<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>
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<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>
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<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
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<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
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<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
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<span style="color: #408080; font-style: italic">#from tensorflow.keras import Conv2D</span>
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<span style="color: #408080; font-style: italic">#from tensorflow.keras import MaxPooling2D</span>
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<span style="color: #408080; font-style: italic">#from tensorflow.keras import Flatten</span>
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<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
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@@ -264,14 +264,14 @@ MathJax.Hub.Config({
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n_filters, n_neurons_connected, n_categories,
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eta, lmbd):
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model <span style="color: #666666">=</span> Sequential()
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model<span style="color: #666666">.</span>add(Conv2D(n_filters, (receptive_field, receptive_field), input_shape<span style="color: #666666">=</span>input_shape, padding<span style="color: #666666">=</span><span style="color: #BA2121">'same'</span>,
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activation<span style="color: #666666">=</span><span style="color: #BA2121">'relu'</span>, kernel_regularizer<span style="color: #666666">=</span>l2(lmbd)))
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model<span style="color: #666666">.</span>add(MaxPooling2D(pool_size<span style="color: #666666">=</span>(<span style="color: #666666">2</span>, <span style="color: #666666">2</span>)))
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model<span style="color: #666666">.</span>add(Flatten())
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model<span style="color: #666666">.</span>add(Dense(n_neurons_connected, activation<span style="color: #666666">=</span><span style="color: #BA2121">'relu'</span>, kernel_regularizer<span style="color: #666666">=</span>l2(lmbd)))
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model<span style="color: #666666">.</span>add(Dense(n_categories, activation<span style="color: #666666">=</span><span style="color: #BA2121">'softmax'</span>, kernel_regularizer<span style="color: #666666">=</span>l2(lmbd)))
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model<span style="color: #666666">.</span>add(layers<span style="color: #666666">.</span>Conv2D(n_filters, (receptive_field, receptive_field), input_shape<span style="color: #666666">=</span>input_shape, padding<span style="color: #666666">=</span><span style="color: #BA2121">'same'</span>,
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activation<span style="color: #666666">=</span><span style="color: #BA2121">'relu'</span>, kernel_regularizer<span style="color: #666666">=</span>regularizers<span style="color: #666666">.</span>l2(lmbd)))
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model<span style="color: #666666">.</span>add(layers<span style="color: #666666">.</span>MaxPooling2D(pool_size<span style="color: #666666">=</span>(<span style="color: #666666">2</span>, <span style="color: #666666">2</span>)))
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model<span style="color: #666666">.</span>add(layers<span style="color: #666666">.</span>Flatten())
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model<span style="color: #666666">.</span>add(layers<span style="color: #666666">.</span>Dense(n_neurons_connected, activation<span style="color: #666666">=</span><span style="color: #BA2121">'relu'</span>, kernel_regularizer<span style="color: #666666">=</span>regularizers<span style="color: #666666">.</span>l2(lmbd)))
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model<span style="color: #666666">.</span>add(layers<span style="color: #666666">.</span>Dense(n_categories, activation<span style="color: #666666">=</span><span style="color: #BA2121">'softmax'</span>, kernel_regularizer<span style="color: #666666">=</span>regularizers<span style="color: #666666">.</span>l2(lmbd)))
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sgd <span style="color: #666666">=</span> SGD(lr<span style="color: #666666">=</span>eta)
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sgd <span style="color: #666666">=</span> optimizers<span style="color: #666666">.</span>SGD(lr<span style="color: #666666">=</span>eta)
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model<span style="color: #666666">.</span>compile(loss<span style="color: #666666">=</span><span style="color: #BA2121">'categorical_crossentropy'</span>, optimizer<span style="color: #666666">=</span>sgd, metrics<span style="color: #666666">=</span>[<span style="color: #BA2121">'accuracy'</span>])
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<span style="color: #008000; font-weight: bold">return</span> model
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@@ -2545,15 +2545,16 @@ plt.show()
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<p>
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<!-- code=python (!bc pycod) typeset with pygments style "perldoc" -->
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<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
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<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</span> <span style="color: #8B008B; font-weight: bold">import</span> datasets, layers, models
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<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
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<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>
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<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>
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<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>
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<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>
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<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>
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<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
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<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
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<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
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<span style="color: #228B22">#from tensorflow.keras import Conv2D</span>
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<span style="color: #228B22">#from tensorflow.keras import MaxPooling2D</span>
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<span style="color: #228B22">#from tensorflow.keras import Flatten</span>
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<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
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@@ -2580,14 +2581,14 @@ X_train, X_test, Y_train, Y_test = train_test_split(inputs, labels, train_size=t
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n_filters, n_neurons_connected, n_categories,
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eta, lmbd):
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model = Sequential()
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model.add(Conv2D(n_filters, (receptive_field, receptive_field), input_shape=input_shape, padding=<span style="color: #CD5555">'same'</span>,
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activation=<span style="color: #CD5555">'relu'</span>, kernel_regularizer=l2(lmbd)))
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model.add(MaxPooling2D(pool_size=(<span style="color: #B452CD">2</span>, <span style="color: #B452CD">2</span>)))
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model.add(Flatten())
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model.add(Dense(n_neurons_connected, activation=<span style="color: #CD5555">'relu'</span>, kernel_regularizer=l2(lmbd)))
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model.add(Dense(n_categories, activation=<span style="color: #CD5555">'softmax'</span>, kernel_regularizer=l2(lmbd)))
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model.add(layers.Conv2D(n_filters, (receptive_field, receptive_field), input_shape=input_shape, padding=<span style="color: #CD5555">'same'</span>,
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activation=<span style="color: #CD5555">'relu'</span>, kernel_regularizer=regularizers.l2(lmbd)))
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model.add(layers.MaxPooling2D(pool_size=(<span style="color: #B452CD">2</span>, <span style="color: #B452CD">2</span>)))
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model.add(layers.Flatten())
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model.add(layers.Dense(n_neurons_connected, activation=<span style="color: #CD5555">'relu'</span>, kernel_regularizer=regularizers.l2(lmbd)))
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model.add(layers.Dense(n_categories, activation=<span style="color: #CD5555">'softmax'</span>, kernel_regularizer=regularizers.l2(lmbd)))
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sgd = SGD(lr=eta)
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sgd = optimizers.SGD(lr=eta)
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model.compile(loss=<span style="color: #CD5555">'categorical_crossentropy'</span>, optimizer=sgd, metrics=[<span style="color: #CD5555">'accuracy'</span>])
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<span style="color: #8B008B; font-weight: bold">return</span> model
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@@ -2460,15 +2460,16 @@ plt.show()
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<p>
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<!-- code=python (!bc pycod) typeset with pygments style "perldoc" -->
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<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
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<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</span> <span style="color: #8B008B; font-weight: bold">import</span> datasets, layers, models
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<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
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<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>
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<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>
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<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>
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<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>
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<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>
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<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
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<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
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<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
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<span style="color: #228B22">#from tensorflow.keras import Conv2D</span>
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<span style="color: #228B22">#from tensorflow.keras import MaxPooling2D</span>
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<span style="color: #228B22">#from tensorflow.keras import Flatten</span>
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<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
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@@ -2494,14 +2495,14 @@ X_train, X_test, Y_train, Y_test = train_test_split(inputs, labels, train_size=t
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n_filters, n_neurons_connected, n_categories,
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eta, lmbd):
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model = Sequential()
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model.add(Conv2D(n_filters, (receptive_field, receptive_field), input_shape=input_shape, padding=<span style="color: #CD5555">'same'</span>,
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activation=<span style="color: #CD5555">'relu'</span>, kernel_regularizer=l2(lmbd)))
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model.add(MaxPooling2D(pool_size=(<span style="color: #B452CD">2</span>, <span style="color: #B452CD">2</span>)))
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model.add(Flatten())
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model.add(Dense(n_neurons_connected, activation=<span style="color: #CD5555">'relu'</span>, kernel_regularizer=l2(lmbd)))
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model.add(Dense(n_categories, activation=<span style="color: #CD5555">'softmax'</span>, kernel_regularizer=l2(lmbd)))
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model.add(layers.Conv2D(n_filters, (receptive_field, receptive_field), input_shape=input_shape, padding=<span style="color: #CD5555">'same'</span>,
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activation=<span style="color: #CD5555">'relu'</span>, kernel_regularizer=regularizers.l2(lmbd)))
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model.add(layers.MaxPooling2D(pool_size=(<span style="color: #B452CD">2</span>, <span style="color: #B452CD">2</span>)))
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model.add(layers.Flatten())
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model.add(layers.Dense(n_neurons_connected, activation=<span style="color: #CD5555">'relu'</span>, kernel_regularizer=regularizers.l2(lmbd)))
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model.add(layers.Dense(n_categories, activation=<span style="color: #CD5555">'softmax'</span>, kernel_regularizer=regularizers.l2(lmbd)))
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sgd = SGD(lr=eta)
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sgd = optimizers.SGD(lr=eta)
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model.compile(loss=<span style="color: #CD5555">'categorical_crossentropy'</span>, optimizer=sgd, metrics=[<span style="color: #CD5555">'accuracy'</span>])
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<span style="color: #8B008B; font-weight: bold">return</span> model
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@@ -2465,15 +2465,16 @@ plt<span style="color: #666666">.</span>show()
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<p>
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<!-- code=python (!bc pycod) typeset with pygments style "default" -->
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<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
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<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</span> <span style="color: #008000; font-weight: bold">import</span> datasets, layers, models
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<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
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<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>
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<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>
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<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>
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<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>
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<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>
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<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: #408080; font-style: italic">#from tensorflow.keras import Conv2D</span>
|
||||
<span style="color: #408080; font-style: italic">#from tensorflow.keras import MaxPooling2D</span>
|
||||
<span style="color: #408080; font-style: italic">#from tensorflow.keras import Flatten</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
|
||||
|
||||
@@ -2499,14 +2500,14 @@ X_train, X_test, Y_train, Y_test <span style="color: #666666">=</span> train_tes
|
||||
n_filters, n_neurons_connected, n_categories,
|
||||
eta, lmbd):
|
||||
model <span style="color: #666666">=</span> Sequential()
|
||||
model<span style="color: #666666">.</span>add(Conv2D(n_filters, (receptive_field, receptive_field), input_shape<span style="color: #666666">=</span>input_shape, padding<span style="color: #666666">=</span><span style="color: #BA2121">'same'</span>,
|
||||
activation<span style="color: #666666">=</span><span style="color: #BA2121">'relu'</span>, kernel_regularizer<span style="color: #666666">=</span>l2(lmbd)))
|
||||
model<span style="color: #666666">.</span>add(MaxPooling2D(pool_size<span style="color: #666666">=</span>(<span style="color: #666666">2</span>, <span style="color: #666666">2</span>)))
|
||||
model<span style="color: #666666">.</span>add(Flatten())
|
||||
model<span style="color: #666666">.</span>add(Dense(n_neurons_connected, activation<span style="color: #666666">=</span><span style="color: #BA2121">'relu'</span>, kernel_regularizer<span style="color: #666666">=</span>l2(lmbd)))
|
||||
model<span style="color: #666666">.</span>add(Dense(n_categories, activation<span style="color: #666666">=</span><span style="color: #BA2121">'softmax'</span>, kernel_regularizer<span style="color: #666666">=</span>l2(lmbd)))
|
||||
model<span style="color: #666666">.</span>add(layers<span style="color: #666666">.</span>Conv2D(n_filters, (receptive_field, receptive_field), input_shape<span style="color: #666666">=</span>input_shape, padding<span style="color: #666666">=</span><span style="color: #BA2121">'same'</span>,
|
||||
activation<span style="color: #666666">=</span><span style="color: #BA2121">'relu'</span>, kernel_regularizer<span style="color: #666666">=</span>regularizers<span style="color: #666666">.</span>l2(lmbd)))
|
||||
model<span style="color: #666666">.</span>add(layers<span style="color: #666666">.</span>MaxPooling2D(pool_size<span style="color: #666666">=</span>(<span style="color: #666666">2</span>, <span style="color: #666666">2</span>)))
|
||||
model<span style="color: #666666">.</span>add(layers<span style="color: #666666">.</span>Flatten())
|
||||
model<span style="color: #666666">.</span>add(layers<span style="color: #666666">.</span>Dense(n_neurons_connected, activation<span style="color: #666666">=</span><span style="color: #BA2121">'relu'</span>, kernel_regularizer<span style="color: #666666">=</span>regularizers<span style="color: #666666">.</span>l2(lmbd)))
|
||||
model<span style="color: #666666">.</span>add(layers<span style="color: #666666">.</span>Dense(n_categories, activation<span style="color: #666666">=</span><span style="color: #BA2121">'softmax'</span>, kernel_regularizer<span style="color: #666666">=</span>regularizers<span style="color: #666666">.</span>l2(lmbd)))
|
||||
|
||||
sgd <span style="color: #666666">=</span> SGD(lr<span style="color: #666666">=</span>eta)
|
||||
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">'categorical_crossentropy'</span>, optimizer<span style="color: #666666">=</span>sgd, metrics<span style="color: #666666">=</span>[<span style="color: #BA2121">'accuracy'</span>])
|
||||
|
||||
<span style="color: #008000; font-weight: bold">return</span> model
|
||||
|
||||
Binary file not shown.
@@ -2460,15 +2460,16 @@
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from tensorflow.keras import datasets, layers, models\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",
|
||||
"#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",
|
||||
@@ -2499,20 +2500,18 @@
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"\n",
|
||||
"\n",
|
||||
"def create_convolutional_neural_network_keras(input_shape, receptive_field,\n",
|
||||
" n_filters, n_neurons_connected, n_categories,\n",
|
||||
" eta, lmbd):\n",
|
||||
" model = Sequential()\n",
|
||||
" model.add(Conv2D(n_filters, (receptive_field, receptive_field), input_shape=input_shape, padding='same',\n",
|
||||
" activation='relu', kernel_regularizer=l2(lmbd)))\n",
|
||||
" model.add(MaxPooling2D(pool_size=(2, 2)))\n",
|
||||
" model.add(Flatten())\n",
|
||||
" model.add(Dense(n_neurons_connected, activation='relu', kernel_regularizer=l2(lmbd)))\n",
|
||||
" model.add(Dense(n_categories, activation='softmax', kernel_regularizer=l2(lmbd)))\n",
|
||||
" model.add(layers.Conv2D(n_filters, (receptive_field, receptive_field), input_shape=input_shape, padding='same',\n",
|
||||
" activation='relu', kernel_regularizer=regularizers.l2(lmbd)))\n",
|
||||
" model.add(layers.MaxPooling2D(pool_size=(2, 2)))\n",
|
||||
" model.add(layers.Flatten())\n",
|
||||
" model.add(layers.Dense(n_neurons_connected, activation='relu', kernel_regularizer=regularizers.l2(lmbd)))\n",
|
||||
" model.add(layers.Dense(n_categories, activation='softmax', kernel_regularizer=regularizers.l2(lmbd)))\n",
|
||||
" \n",
|
||||
" sgd = SGD(lr=eta)\n",
|
||||
" sgd = optimizers.SGD(lr=eta)\n",
|
||||
" model.compile(loss='categorical_crossentropy', optimizer=sgd, metrics=['accuracy'])\n",
|
||||
" \n",
|
||||
" return model\n",
|
||||
|
||||
@@ -2018,15 +2018,16 @@ plt.show()
|
||||
!split
|
||||
===== Importing Keras and Tensorflow =====
|
||||
!bc pycod
|
||||
from tensorflow.keras import datasets, layers, models
|
||||
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 tensorflow.keras import Conv2D
|
||||
#from tensorflow.keras import MaxPooling2D
|
||||
#from tensorflow.keras import Flatten
|
||||
|
||||
from sklearn.model_selection import train_test_split
|
||||
|
||||
@@ -2045,20 +2046,18 @@ X_train, X_test, Y_train, Y_test = train_test_split(inputs, labels, train_size=t
|
||||
===== Running with Keras =====
|
||||
|
||||
!bc pycod
|
||||
|
||||
|
||||
def create_convolutional_neural_network_keras(input_shape, receptive_field,
|
||||
n_filters, n_neurons_connected, n_categories,
|
||||
eta, lmbd):
|
||||
model = Sequential()
|
||||
model.add(Conv2D(n_filters, (receptive_field, receptive_field), input_shape=input_shape, padding='same',
|
||||
activation='relu', kernel_regularizer=l2(lmbd)))
|
||||
model.add(MaxPooling2D(pool_size=(2, 2)))
|
||||
model.add(Flatten())
|
||||
model.add(Dense(n_neurons_connected, activation='relu', kernel_regularizer=l2(lmbd)))
|
||||
model.add(Dense(n_categories, activation='softmax', kernel_regularizer=l2(lmbd)))
|
||||
model.add(layers.Conv2D(n_filters, (receptive_field, receptive_field), input_shape=input_shape, padding='same',
|
||||
activation='relu', kernel_regularizer=regularizers.l2(lmbd)))
|
||||
model.add(layers.MaxPooling2D(pool_size=(2, 2)))
|
||||
model.add(layers.Flatten())
|
||||
model.add(layers.Dense(n_neurons_connected, activation='relu', kernel_regularizer=regularizers.l2(lmbd)))
|
||||
model.add(layers.Dense(n_categories, activation='softmax', kernel_regularizer=regularizers.l2(lmbd)))
|
||||
|
||||
sgd = SGD(lr=eta)
|
||||
sgd = optimizers.SGD(lr=eta)
|
||||
model.compile(loss='categorical_crossentropy', optimizer=sgd, metrics=['accuracy'])
|
||||
|
||||
return model
|
||||
|
||||
Reference in New Issue
Block a user