Hands-On Deep Learning Algorithms with Python

Optimizing the loss

Now we need to optimize our generator and discriminator. So, we collect the parameters of the discriminator and generator as and respectively:

training_vars = tf.trainable_variables()theta_D = [var for var in training_vars if 'dis' in var.name]theta_G = [var for var in training_vars if 'gen' in var.name]

Optimize the loss using the Adam optimizer:

learning_rate = 0.001D_optimizer = tf.train.AdamOptimizer(learning_rate).minimize(D_loss,var_list = theta_D)G_optimizer = tf.train.AdamOptimizer(learning_rate).minimize(G_loss, ...