typo in tensorflow
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Morten Hjorth-Jensen
@@ -2254,7 +2254,7 @@ lmbd_vals <span style="color: #666666">=</span> np<span style="color: #666666">.
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model<span style="color: #666666">.</span>add(Dense(n_neurons_layer2, activation<span style="color: #666666">=</span><span style="color: #BA2121">'sigmoid'</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(Dense(n_categories, activation<span style="color: #666666">=</span><span style="color: #BA2121">'softmax'</span>))
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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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sgd <span style="color: #666666">=</span> optimizers<span style="color: #666666">.</span>SGD(learning_rate<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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@@ -2477,7 +2477,7 @@ batch_size<span style="color: #666666">=100</span>
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<span style="color: #008000; font-weight: bold">else</span>: <span style="color: #408080; font-style: italic">#Subsequent layers are capable of automatic shape inferencing</span>
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model<span style="color: #666666">.</span>add(Dense(n_neuron,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(lamda)))
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model<span style="color: #666666">.</span>add(Dense(<span style="color: #666666">2</span>,activation<span style="color: #666666">=</span><span style="color: #BA2121">'softmax'</span>)) <span style="color: #408080; font-style: italic">#2 outputs - ordered and disordered (softmax for prob)</span>
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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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sgd<span style="color: #666666">=</span>optimizers<span style="color: #666666">.</span>SGD(learning_rate<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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@@ -2119,7 +2119,7 @@ lmbd_vals = np.logspace(-<span style="color: #B452CD">5</span>, <span style="col
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model.add(Dense(n_neurons_layer2, activation=<span style="color: #CD5555">'sigmoid'</span>, kernel_regularizer=regularizers.l2(lmbd)))
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model.add(Dense(n_categories, activation=<span style="color: #CD5555">'softmax'</span>))
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sgd = optimizers.SGD(lr=eta)
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sgd = optimizers.SGD(learning_rate=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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@@ -2342,7 +2342,7 @@ batch_size=<span style="color: #B452CD">100</span>
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<span style="color: #8B008B; font-weight: bold">else</span>: <span style="color: #228B22">#Subsequent layers are capable of automatic shape inferencing</span>
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model.add(Dense(n_neuron,activation=<span style="color: #CD5555">'relu'</span>,kernel_regularizer=regularizers.l2(lamda)))
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model.add(Dense(<span style="color: #B452CD">2</span>,activation=<span style="color: #CD5555">'softmax'</span>)) <span style="color: #228B22">#2 outputs - ordered and disordered (softmax for prob)</span>
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sgd=optimizers.SGD(lr=eta)
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sgd=optimizers.SGD(learning_rate=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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@@ -2145,7 +2145,7 @@ lmbd_vals = np.logspace(-<span style="color: #B452CD">5</span>, <span style="col
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model.add(Dense(n_neurons_layer2, activation=<span style="color: #CD5555">'sigmoid'</span>, kernel_regularizer=regularizers.l2(lmbd)))
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model.add(Dense(n_categories, activation=<span style="color: #CD5555">'softmax'</span>))
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sgd = optimizers.SGD(lr=eta)
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sgd = optimizers.SGD(learning_rate=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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@@ -2368,7 +2368,7 @@ batch_size=<span style="color: #B452CD">100</span>
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<span style="color: #8B008B; font-weight: bold">else</span>: <span style="color: #228B22">#Subsequent layers are capable of automatic shape inferencing</span>
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model.add(Dense(n_neuron,activation=<span style="color: #CD5555">'relu'</span>,kernel_regularizer=regularizers.l2(lamda)))
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model.add(Dense(<span style="color: #B452CD">2</span>,activation=<span style="color: #CD5555">'softmax'</span>)) <span style="color: #228B22">#2 outputs - ordered and disordered (softmax for prob)</span>
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sgd=optimizers.SGD(lr=eta)
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sgd=optimizers.SGD(learning_rate=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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@@ -2222,7 +2222,7 @@ lmbd_vals <span style="color: #666666">=</span> np<span style="color: #666666">.
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model<span style="color: #666666">.</span>add(Dense(n_neurons_layer2, activation<span style="color: #666666">=</span><span style="color: #BA2121">'sigmoid'</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(Dense(n_categories, activation<span style="color: #666666">=</span><span style="color: #BA2121">'softmax'</span>))
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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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sgd <span style="color: #666666">=</span> optimizers<span style="color: #666666">.</span>SGD(learning_rate<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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@@ -2445,7 +2445,7 @@ batch_size<span style="color: #666666">=100</span>
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<span style="color: #008000; font-weight: bold">else</span>: <span style="color: #408080; font-style: italic">#Subsequent layers are capable of automatic shape inferencing</span>
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model<span style="color: #666666">.</span>add(Dense(n_neuron,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(lamda)))
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model<span style="color: #666666">.</span>add(Dense(<span style="color: #666666">2</span>,activation<span style="color: #666666">=</span><span style="color: #BA2121">'softmax'</span>)) <span style="color: #408080; font-style: italic">#2 outputs - ordered and disordered (softmax for prob)</span>
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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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sgd<span style="color: #666666">=</span>optimizers<span style="color: #666666">.</span>SGD(learning_rate<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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@@ -1406,7 +1406,7 @@ def create_neural_network_keras(n_neurons_layer1, n_neurons_layer2, n_categories
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model.add(Dense(n_neurons_layer2, activation='sigmoid', kernel_regularizer=regularizers.l2(lmbd)))
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model.add(Dense(n_categories, activation='softmax'))
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sgd = optimizers.SGD(lr=eta)
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sgd = optimizers.SGD(learning_rate=eta)
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model.compile(loss='categorical_crossentropy', optimizer=sgd, metrics=['accuracy'])
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return model
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@@ -1582,7 +1582,7 @@ def NN_model(inputsize,n_layers,n_neuron,eta,lamda):
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else: #Subsequent layers are capable of automatic shape inferencing
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model.add(Dense(n_neuron,activation='relu',kernel_regularizer=regularizers.l2(lamda)))
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model.add(Dense(2,activation='softmax')) #2 outputs - ordered and disordered (softmax for prob)
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sgd=optimizers.SGD(lr=eta)
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sgd=optimizers.SGD(learning_rate=eta)
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model.compile(loss='categorical_crossentropy',optimizer=sgd,metrics=['accuracy'])
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return model
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