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Exercise

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:

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

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

Instructions
100 XP
  • 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.
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