Deep Learning By Example

Model optimizer

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, ...