Hands-On Deep Learning Algorithms with Python

Defining the discriminator

We define a discriminator as a convolutional network with three convolutional layers followed by a fully connected layer. It is composed of a series of convolutional and batch norm layers with leaky ReLU activations. We apply batch normalization at all layers except at the input layer:

def discriminator(input_images, reuse=False, is_training=False, alpha=0.1):        with tf.variable_scope('discriminator', reuse= reuse):

First convolutional layer with leaky ReLU activation:

        layer1 = tf.layers.conv2d(input_images,                                   filters=64,                                   kernel_size=5,                                   strides=2,                                   padding='same',                                   kernel_initializer=kernel_init,                                   name='conv1')        layer1 = tf.nn.leaky_relu(layer1, alpha=0.2, name='leaky_relu1')

Second convolutional layer with batch ...