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Convolutional Neural Networks
These are followed by a standard dense output layer. All the activation functions
used are ReLU functions. There is a new layer that we also discussed in Chapter 1,
Neural Network Foundations with TensorFlow 2.0 BatchNormalization() which is
used to introduce a form of regularization between modules:
def build_model():
model = models.Sequential()
# 1st block
model.add(layers.Conv2D(32, (3,3), padding='same',
input_shape=x_train.shape[1:], activation='relu'))
model.add(layers.BatchNormalization())
model.add(layers.Conv2D(32, (3,3), padding='same',
activation='relu'))
model.add(layers.BatchNormalization())
model.add(layers.MaxPooling2D(pool_size=(2,2)))
model.add(layers.Dropout(0.2))
# 2nd block
model.add(layers.Conv2D(64, (3,3), padding='same',
activation='relu'))
model.add(layers.BatchNormalization())
model.add(layers.Conv2D(64, (3,3), padding='same',
activation='relu'))
model.add(layers.BatchNormalization())
model.add(layers.MaxPooling2D(pool_size=(2,2)))
model.add(layers.Dropout(0.3))
# 3d block
model.add(layers.Conv2D(128, (3,3), padding='same',
activation='relu'))
model.add(layers.BatchNormalization())
model.add(layers.Conv2D(128, (3,3), padding='same',
activation='relu'))
model.add(layers.BatchNormalization())
model.add(layers.MaxPooling2D(pool_size=(2,2)))
model.add(layers.Dropout(0.4))
# dense
model.add(layers.Flatten())
model.add(layers.Dense(NUM_CLASSES, activation='softmax'))
return model
model.summary()
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