Merge branch 'main' into master

Author: svekars

advanced_source/cpp_frontend.rst

@@ -969,15 +969,15 @@ the data loader every epoch and then write the GAN training code:
       discriminator->zero_grad();
       torch::Tensor real_images = batch.data;
       torch::Tensor real_labels = torch::empty(batch.data.size(0)).uniform_(0.8, 1.0);
-      torch::Tensor real_output = discriminator->forward(real_images);
+      torch::Tensor real_output = discriminator->forward(real_images).reshape(real_labels.sizes());
       torch::Tensor d_loss_real = torch::binary_cross_entropy(real_output, real_labels);
       d_loss_real.backward();
 
       // Train discriminator with fake images.
       torch::Tensor noise = torch::randn({batch.data.size(0), kNoiseSize, 1, 1});
       torch::Tensor fake_images = generator->forward(noise);
       torch::Tensor fake_labels = torch::zeros(batch.data.size(0));
-      torch::Tensor fake_output = discriminator->forward(fake_images.detach());
+      torch::Tensor fake_output = discriminator->forward(fake_images.detach()).reshape(fake_labels.sizes());
       torch::Tensor d_loss_fake = torch::binary_cross_entropy(fake_output, fake_labels);
       d_loss_fake.backward();
 
@@ -987,7 +987,7 @@ the data loader every epoch and then write the GAN training code:
       // Train generator.
       generator->zero_grad();
       fake_labels.fill_(1);
-      fake_output = discriminator->forward(fake_images);
+      fake_output = discriminator->forward(fake_images).reshape(fake_labels.sizes());
       torch::Tensor g_loss = torch::binary_cross_entropy(fake_output, fake_labels);
       g_loss.backward();
       generator_optimizer.step();