64a2763f3e0ff21c6de3bbbe6cbddab1857fc080,cyclegan/cyclegan.py,CycleGAN,train,#CycleGAN#,157

Before Change

    def train(self, epochs, batch_size=128, sample_interval=50):

        half_batch = int(batch_size / 2)

        start_time = datetime.datetime.now()

        for epoch in range(epochs):

            // ----------------------
            //  Train Discriminators
            // ----------------------

            imgs_A = self.data_loader.load_data(domain="A", batch_size=half_batch)
            imgs_B = self.data_loader.load_data(domain="B", batch_size=half_batch)

            // Translate images to opposite domain
            fake_B = self.g_AB.predict(imgs_A)
            fake_A = self.g_BA.predict(imgs_B)

After Change

                elapsed_time = datetime.datetime.now() - start_time

                // Plot the progress
                print ("[Epoch %d/%d] [Batch %d/%d] time: %s [D loss: %f, acc: %3d%%] [G loss: %05f, adv: %05f, recon: %05f, id: %05f]" \
                                                                        % ( epoch, epochs,
                                                                            batch_i, self.data_loader.n_batches,
                                                                            elapsed_time,
                                                                            d_loss[0], 100*d_loss[1],
                                                                            g_loss[0],
                                                                            np.mean(g_loss[1:3]),
                                                                            np.mean(g_loss[3:5]),
                                                                            np.mean(g_loss[5:6])))

                // If at save interval                                        => save generated image samples
                if batch_i % sample_interval == 0:
                    self.sample_images(epoch, batch_i)

Instances