python-kerasHow can I use batch normalization in Python Keras?

Batch normalization is a technique used to normalize the input layer by adjusting and scaling the activations of the previous layer. It can be used to reduce overfitting and to speed up the training process of a deep neural network.

In Python Keras, batch normalization can be implemented by using the BatchNormalization layer. This layer takes an input shape and applies a transformation that maintains the mean output close to 0 and the standard deviation close to 1.

Example code

model = Sequential()
model.add(Dense(64, input_shape=(32,)))
model.add(BatchNormalization())
model.add(Activation('relu'))
model.add(Dense(10))
model.add(Activation('softmax'))

The example code above creates a model with a Dense layer as the input layer, followed by a BatchNormalization layer, an Activation layer with relu as the activation function, another Dense layer as the output layer, and a final Activation layer with softmax as the activation function.

Code explanation

• model = Sequential(): This line creates a Sequential model object.
• model.add(Dense(64, input_shape=(32,))): This line adds a Dense layer with 64 units as the input layer.
• model.add(BatchNormalization()): This line adds a BatchNormalization layer which will normalize the input layer.
• model.add(Activation('relu')): This line adds an Activation layer with relu as the activation function.
• model.add(Dense(10)): This line adds a Dense layer with 10 units as the output layer.
• model.add(Activation('softmax')): This line adds an Activation layer with softmax as the activation function.

Helpful links

• Keras Documentation: Batch Normalization
• TensorFlow Tutorial: Batch Normalization

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