losses.py

import tensorflow as tf
def generator_loss(generated):
    return tf.keras.losses.BinaryCrossentropy(from_logits=True, reduction=tf.keras.losses.Reduction.NONE)(tf.ones_like(generated), generated)

def discriminator_loss(real, generated):
    real_loss = tf.keras.losses.BinaryCrossentropy(from_logits=True, reduction=tf.keras.losses.Reduction.NONE)(tf.ones_like(real), real)

    generated_loss = tf.keras.losses.BinaryCrossentropy(from_logits=True, reduction=tf.keras.losses.Reduction.NONE)(tf.zeros_like(generated), generated)

    total_disc_loss = real_loss + generated_loss

    return total_disc_loss * 0.5

def calc_cycle_loss(real_image, cycled_image, LAMBDA):
    loss1 = tf.reduce_mean(tf.abs(real_image - cycled_image))

    return LAMBDA * loss1

def identity_loss(real_image, same_image, LAMBDA):
    loss = tf.reduce_mean(tf.abs(real_image - same_image))
    return LAMBDA * 0.5 * loss