Add batch normalization to your network

Batch normalization is another form of regularization that rescales the outputs of a layer to make sure that they have mean 0 and standard deviation 1. In this exercise, we will add batch normalization to the convolutional neural network that we have used in previous exercises:

Convolution (15 units, kernel size 2, 'relu' activation)
Batch normalization
Convolution (5 unites, kernel size 2, 'relu' activation)
Flatten
Dense (3 units, 'softmax' activation)

A Sequential model along with Dense, Conv2D, Flatten, and Dropout objects are available in your workspace.

This exercise is part of the course

Image Modeling with Keras

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Exercise instructions

Add the first convolutional layer. You can use the img_rows and img_cols objects available in your workspace to define the input_shape of this layer.
Add batch normalization applied to the outputs of the first layer.

Hands-on interactive exercise

Have a go at this exercise by completing this sample code.

# Add a convolutional layer
____


# Add batch normalization layer
____

# Add another convolutional layer
model.add(Conv2D(5, kernel_size=2, activation='relu'))

# Flatten and feed to output layer
model.add(Flatten())
model.add(Dense(3, activation='softmax'))

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