optimizers.py

def gan_model_optimizers(d_loss, g_loss, disc_lr, gen_lr, beta1):
    """
    Get optimization operations
    
    Arguments:
    ----------
    :param d_loss: Discriminator loss Tensor
    :param g_loss: Generator loss Tensor
    :param disc_lr: Placeholder for Learning Rate for discriminator
    :param gen_lr: Placeholder for Learning Rate for generator
    :param beta1: The exponential decay rate for the 1st moment in the optimizer
    ----------
    Returns: 
    A tuple of (discriminator training operation, generator training operation)
    """    
    # Get the trainable_variables, split into G and D parts
    train_vars = tf.trainable_variables()
    gen_vars = [var for var in train_vars if var.name.startswith("generator")]
    disc_vars = [var for var in train_vars if var.name.startswith("discriminator")]
    
    update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS)
    
    # Generator update
    gen_updates = [op for op in update_ops if op.name.startswith('generator')]
    
    # Optimizers
    with tf.control_dependencies(gen_updates):
        disc_train_opt = tf.train.AdamOptimizer(learning_rate = disc_lr, beta1 = beta1).minimize(d_loss, var_list = disc_vars)
        gen_train_opt = tf.train.AdamOptimizer(learning_rate = gen_lr, beta1 = beta1).minimize(g_loss, var_list = gen_vars)
        
    return disc_train_opt, gen_train_opt