Hi there
In order to learn about GANs I adapted code for a WGAN and played around a bit (from Machine-Learning-Collection/ML/Pytorch/GANs/3. WGAN at master · aladdinpersson/Machine-Learning-Collection · GitHub).


I noticed that if I add a TanH function to the discriminator model. Training becomes unstable and generally much slower. Why is that?


Does someone has an idea how I can converge during training the output of the discriminator of a WGAN to match a certain label (e.g. -1 and +1 or 0 and 1) without adding a transfer function to the model itself?


This is the current training loop:
        # Train Critic: max E[critic(real)] - E[critic(fake)]
        for _ in range(CRITIC_ITERATIONS):
            noise = torch.randn(cur_batch_size, RAND_DIM, 1, 1).to(device)
            fake = gen(noise)
            critic_real = critic(real).reshape(-1)
            critic_fake = critic(fake).reshape(-1)

            mean_real = torch.mean(critic_real)
            mean_fake = torch.mean(critic_fake)

            loss_critic = mean_fake - mean_real
            critic.zero_grad()
            loss_critic.backward(retain_graph=True)
            opt_crit.step()

            # clip critic weights between -0.01, 0.01
            for p in critic.parameters():
                p.data.clamp_(-0.01, 0.01)

        # Train Generator: max E[critic(gen_fake)] <-> min -E[critic(gen_fake)]
        loss_gen = -torch.mean(critic(fake).reshape(-1))
        gen.zero_grad()
        loss_gen.backward()
        opt_gen.step()