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

Before Change

        Docs.
        
        images, _ = batch
        images = images.view(images.size(0), -1)
        bs = images.shape[0]
        z = torch.randn(bs, 128).to(self.device)
        generated_images = self.model["generator"](z)

        // generator step
        // predictions & labels
        generated_labels = torch.ones(bs, 1).to(self.device)
        generated_pred = self.model["discriminator"](generated_images)

        // loss
        loss_generator = F.binary_cross_entropy(
            generated_pred, generated_labels
        )
        self.state.batch_metrics["loss_generator"] = loss_generator

        // discriminator step
        // real
        images_labels = torch.ones(bs, 1).to(self.device)
        images_pred = self.model["discriminator"](images)
        real_loss = F.binary_cross_entropy(images_pred, images_labels)

        // 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

        // Assemble labels discriminating real from fake images
        labels = torch.cat(
            [torch.ones((batch_size, 1)), torch.zeros((batch_size, 1))]
        ).to(self.device)
        // Add random noise to the labels - important trick!
        labels += 0.05 * torch.rand(labels.shape).to(self.device)

        // Train the discriminator
        predictions = self.model["discriminator"](combined_images)
        batch_metrics[

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