Hands-On Deep Learning Algorithms with Python

Defining the discriminator

We know that discriminator returns the probability of the given image being real. We define the discriminator also as a feedforward network with three layers:

def discriminator(X,reuse=None):        with tf.variable_scope('discriminator',reuse=reuse):                hidden1 = tf.layers.dense(inputs=X,units=128,activation=tf.nn.leaky_relu)        hidden2 = tf.layers.dense(inputs=hidden1,units=128,activation=tf.nn.leaky_relu)        logits = tf.layers.dense(inputs=hidden2,units=1)        output = tf.sigmoid(logits)                return logits