losses.py

def gan_model_loss(input_real, input_z, output_channel_dim, alpha):
    """
    Get the loss for the discriminator and generator
    
    Arguments:
    ---------
    :param input_real: Images from the real dataset
    :param input_z: Z input
    :param out_channel_dim: The number of channels in the output image
    ---------
    Returns: 
    A tuple of (discriminator loss, generator loss)
    """
    # Build the Generator network 
    g_model_output = build_generator(input_z, output_channel_dim)   
    
    # Build the discriminator network 
    
    # For real inputs
    real_d_model, real_d_logits = build_discriminator(input_real, alpha=alpha)
    
    # For fake inputs (generated output from the generator model)
    fake_d_model, fake_d_logits = build_discriminator(g_model_output, is_reuse=True, alpha=alpha)
    
    # Calculate losses for each network
    d_loss_real = tf.reduce_mean(
                  tf.nn.sigmoid_cross_entropy_with_logits(logits=real_d_logits, 
                                                          labels=tf.ones_like(real_d_model)))
    d_loss_fake = tf.reduce_mean(
                  tf.nn.sigmoid_cross_entropy_with_logits(logits=fake_d_logits, 
                                                          labels=tf.zeros_like(fake_d_model)))
    
    # Discriminator loss is the sum of real and fake loss
    d_loss = d_loss_real + d_loss_fake

    # Generator loss
    g_loss = tf.reduce_mean(
             tf.nn.sigmoid_cross_entropy_with_logits(logits=fake_d_logits,
                                                     labels=tf.ones_like(fake_d_model)))
    
    return d_loss, g_loss