X = Variable(X)
z = Variable(z)
label = Variable(label)
for it in range(10):
for batch_idx, (data, target) in enumerate(train_loader, 0):
netD.zero_grad()
mb_size = data.size(0)
X.data.resize_(data.size()).copy_(data)
X.data.resize_(mb_size, x_dim)
z.data.resize_(mb_size, z_dim).normal_(0, 1)
label.data.resize_(mb_size)
G_share_sample = netG_share(z)
G_indep_sample = create_netG_indeps_sample(netG_indeps, G_share_sample)
############################
# (1) Update D network:
###########################
D_real = netD(X)
D_fake = netD_fake(G_indep_sample, netD)
D_loss = compute_dloss(D_real, D_fake, label)
D_exp.add_scalar_value('D_loss',
D_loss.data[0],
step=batch_idx + it * train_size)
D_loss.backward(retain_variables=True)
D_solver.step()
############################
# (2) Update G network:
torch.load('./result/vanillgan_mnist_latent100/out/netD_epoch_22.pth'))
for it in range(iter):
for batch_idx, (data, target) in enumerate(train_loader, 0):
################
# (0) process data
################
netD.zero_grad()
mb_size = data.size(0)
X.data.resize_(data.size()).copy_(data)
X.data.resize_(mb_size, x_dim)
z.data.resize_(mb_size, z_dim).normal_(0, 1)
label.data.resize_(mb_size)
cnt = batch_idx + it * train_size
G_indep_sample = create_netG_indeps_sample(netG_indeps, z)
G_share_sample = create_netG_share_sample(netG_share, G_indep_sample)
############################
# (1) Update D network: vaillgan loss
###########################
if not netD_continue_trian:
D_real = netD(X)
D_fake = netD_fake(G_share_sample, netD)
D_loss = compute_dloss(D_real, D_fake, label)
D_exp.add_scalar_value('D_loss', D_loss.data[0], step=cnt)
D_loss.backward(retain_variables=True)
D_solver.step()
else:
D_real = netD(X).data.numpy()[0][0]
D_fake = netD_fake(G_share_sample, netD)