cycle_gan_train.py

def train(self, epochs, batch_size, train_data_path):

    real = np.ones((batch_size,) + self.disc_patch)
    fake = np.zeros((batch_size,) + self.disc_patch)

    history = []

    for epoch in range(epochs):
        for i, (imagesX, imagesY) in enumerate(self._image_helper.load_batch_of_train_images(train_data_path, batch_size)):
            print ("---------------------------------------------------------")
            print ("******************Epoch {} | Batch {}***************************".format(epoch, i))
            print("Generate images...")
            fakeY = self._generatorXY.predict(imagesX)
            fakeX = self._generatorYX.predict(imagesY)

            print("Train Discriminators...")
            discriminatorX_loss_real = self._discriminatorX.train_on_batch(imagesX, real)
            discriminatorX_loss_fake = self._discriminatorX.train_on_batch(fakeX, fake)
            discriminatorX_loss = 0.5 * np.add(discriminatorX_loss_real, discriminatorX_loss_fake)

            discriminatorY_loss_real = self._discriminatorY.train_on_batch(imagesY, real)
            discriminatorY_loss_fake = self._discriminatorY.train_on_batch(fakeY, fake)
            discriminatorY_loss = 0.5 * np.add(discriminatorY_loss_real, discriminatorY_loss_fake)

            mean_discriminator_loss = 0.5 * np.add(discriminatorX_loss, discriminatorY_loss)

            print("Train Generators...")
            generator_loss = self.gan.train_on_batch([imagesX, imagesY],
                                                    [real, real,
                                                    imagesX, imagesY,
                                                    imagesX, imagesY])

            print ("Discriminator loss: {}".format(mean_discriminator_loss[0]))
            print ("Generator loss: {}".format(generator_loss[0]))
            print ("---------------------------------------------------------")

            history.append({"D":mean_discriminator_loss[0],"G":generator_loss})

            if i%100 ==0:
                self._save_images("{}_{}".format(epoch, i), train_data_path)

    self._plot_loss(history)