Hands-On Deep Learning Algorithms with Python

Defining the discriminator

We learned that both the discriminator and Q network take the generator image and return the output so they both share some layers. Since they both share some layers, we attach the Q network to the discriminator, as we learned in the architecture of InfoGAN. Instead of using fully connected layers in the discriminator, we use a convolutional network, as we learned in the discriminator of DCGAN:

def discriminator(x,reuse=None):

Define the first layer, which performs the convolution operation followed by a leaky ReLU activation:

    conv1 = tf.layers.conv2d(x,                              filters=64,                              kernel_size=4,                             strides=2,                             padding='same', kernel_initializer=tf.contrib.layers.xavier_initializer(), ...