f93beff338925cc1bf1b3ff1b32a2c440a4c9427,tests/_tests_scripts/z_mvp_mnist_gan.py,CustomRunner,_handle_batch,#CustomRunner#Any#,18

Before Change

        // fake
        generated_labels_ = torch.zeros(bs, 1).to(self.device)
        generated_pred_ = self.model["discriminator"](
            generated_images.detach()
        )
        fake_loss = F.binary_cross_entropy(generated_pred_, generated_labels_)

        // loss
        loss_discriminator = (real_loss + fake_loss) / 2.0
        self.state.batch_metrics["loss_discriminator"] = loss_discriminator


def main():
    

After Change

            self.device
        )
        // Assemble labels that say "all real images"
        misleading_labels = torch.zeros((batch_size, 1)).to(self.device)

        // Train the generator
        generated_images = self.model["generator"](random_latent_vectors)
        predictions = self.model["discriminator"](generated_images)

Instances