Hands-On Deep Learning Algorithms with Python

Defining the generator

Generator, G, takes the noise, , and also the conditional variable, , as an input and returns an image. We define the generator as a simple two- layer feed-forward network:

def generator(z, c,reuse=False):    with tf.variable_scope('generator', reuse=reuse):

Initialize the weights:

            w_init = tf.contrib.layers.xavier_initializer()

Concatenate the noise, , and the conditional variable, :

            inputs = tf.concat([z, c], 1)

Define the ...