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 first layer:

            dense1 = tf.layers.dense(inputs, 128, kernel_initializer=w_init)
            relu1 = tf.nn.relu(dense1)

Define the second layer and compute the output with the tanh activation function:

            logits = tf.layers.dense(relu1, 784, kernel_initializer=w_init)
            output = tf.nn.tanh(logits)

            return output