Hands-On Deep Learning Algorithms with Python

Optimizing the loss

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

training_vars = tf.trainable_variables()theta_D = [var for var in training_vars if var.name.startswith('discriminator')]theta_G = [var for var in training_vars if var.name.startswith('generator')]

Optimize the loss using the Adam optimizer:

learning_rate = 0.001D_optimizer = tf.train.AdamOptimizer(learning_rate, beta1=0.5).minimize(D_loss,                          var_list=theta_D)G_optimizer = tf.train.AdamOptimizer(learning_rate, beta1=0.5).minimize(G_loss,                        var_list=theta_G)