loss_function.py

# Pattern Recognition final project Group 8 Kaixiang Huang
# support function for cycleGAN

import keras.backend as K

def loss_fn(input, target):
    return K.mean(K.square(input + 1e-12 - target), axis=-1)

def cycle_variables(netG1, netG2):
    real_input = netG1.inputs[0]
    fake_output = netG1.outputs[0]
    rec_input = netG2([fake_output])
    fn_generate = K.function([real_input], [fake_output, rec_input])
    return real_input, fake_output, rec_input, fn_generate


def loss_generator(netD, real, fake, rec):
    output_real = netD([real])
    output_fake = netD([fake])
    loss_D_real = loss_fn(output_real, K.ones_like(output_real))
    loss_D_fake = loss_fn(output_fake, K.zeros_like(output_fake))
    loss_G = loss_fn(output_fake, K.ones_like(output_fake))
    loss_D = loss_D_real + loss_D_fake
    loss_cyc = K.mean(K.abs(rec - real))
    return loss_D, loss_G, loss_cyc