October 2018
Intermediate to advanced
252 pages
6h 49m
English
Content preview from Keras Deep Learning Cookbook
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How to do it...
Create a sequential model with the appropriate network topology:
- Input layer with shape (*, 784), and output (*, 512)
- Hidden layer with input (*, 512) and output (*, 512)
- Output layer with input dimension as (*, 512) and output (*, 10)
Activation functions for layer 1 and layer 3 are shown in the points:
- Layer 1 and Layer 1-relu
- Layer 3-softmax
y_train = keras.utils.to_categorical(y_train, num_classes)y_test = keras.utils.to_categorical(y_test, num_classes)model = Sequential()model.add(Dense(512, activation='relu', input_shape=(784,)))model.add(Dropout(0.2))model.add(Dense(512, activation='relu'))model.add(Dropout(0.2))model.add(Dense(num_classes, activation='softmax'))model.summary()adam = keras.optimizers.Adam(lr=0.001, ...
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