cbcc3f315c653706ceded6ba42f222616322a3f2,ch03/03_atari_gan.py,,,#,132

Before Change

    dis_losses = []
    iter_no = 0

    true_labels_v = Variable(torch.FloatTensor([1.0] * BATCH_SIZE))
    fake_labels_v = Variable(torch.FloatTensor([0.0] * BATCH_SIZE))
    if args.cuda:
        true_labels_v = true_labels_v.cuda()

After Change

    dis_losses = []
    iter_no = 0

    true_labels_v = torch.ones(BATCH_SIZE, dtype=torch.float32, device=device)
    fake_labels_v = torch.zeros(BATCH_SIZE, dtype=torch.float32, device=device)

    for batch_v in iterate_batches(envs):
        // generate extra fake samples, input is 4D: batch, filters, x, y
        gen_input_v = torch.FloatTensor(BATCH_SIZE, LATENT_VECTOR_SIZE, 1, 1).normal_(0, 1).to(device)
        batch_v = batch_v.to(device)
        gen_output_v = net_gener(gen_input_v)

        // train discriminator
        dis_optimizer.zero_grad()
        dis_output_true_v = net_discr(batch_v)
        dis_output_fake_v = net_discr(gen_output_v.detach())
        dis_loss = objective(dis_output_true_v, true_labels_v) + objective(dis_output_fake_v, fake_labels_v)
        dis_loss.backward()
        dis_optimizer.step()
        dis_losses.append(dis_loss.item())

        // train generator
        gen_optimizer.zero_grad()

Instances