691e0b50d0b15665df5ec77eeec7c605c4283e1f,hypergan/trainers/alternating_trainer.py,AlternatingTrainer,_step,#AlternatingTrainer#Any#,19

Before Change

            loss = hook.forward()
            if loss[1] is not None:
                g_loss += loss[1]
        g_loss.mean().backward()
        self.g_optimizer.step()

        if self.current_step % 10 == 0:
            self.print_metrics(self.current_step)

After Change

        for p, np in zip(self.gan.d_parameters(), d_grads):
            p.grad = np

        if(len(d_grads) > 0):
                self.d_optimizer.step()

        _, g_grads = self.calculate_gradients(["g"])

Instances