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.

Latihan ini adalah bagian dari kursus

Image Modeling with Keras

Lihat Kursus

Petunjuk latihan

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.

Latihan interaktif praktis

Cobalah latihan ini dengan menyelesaikan kode contoh berikut.

# 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'))

Edit dan Jalankan Kode