Now, let's define the model optimizer, which is pretty much similar to the ones that we defined before:

def model_optimizer(disc_loss, gen_loss, learning_rate, beta1):

        # Get weights and biases to update. Get them separately for the discriminator and the generator
        trainable_vars = tf.trainable_variables()
        disc_vars = [var for var in trainable_vars if var.name.startswith('discriminator')]
        gen_vars = [var for var in trainable_vars if var.name.startswith('generator')]
        for t in trainable_vars:
            assert t in disc_vars or t in gen_vars

        # Minimize both gen and disc costs simultaneously
        disc_train_optimizer = tf.train.AdamOptimizer(learning_rate, beta1=beta1).minimize(disc_loss,
                                                                                           var_list=disc_vars)
        gen_train_optimizer = tf.train.AdamOptimizer(learning_rate, beta1=beta1).minimize(gen_loss, var_list=gen_vars)
        shrink_learning_rate = tf.assign(learning_rate, learning_rate * 0.9)

        return disc_train_optimizer, gen_train_optimizer, shrink_learning_rate