return output
NetD = Discriminator()
NetG = Generator()
MSE_LOSS = torch.nn.MSELoss()
optimizerD = torch.optim.Adam(NetD.parameters(),
lr=LEARNING_RATE,
betas=(0.5, 0.999))
optimizerG = torch.optim.Adam(NetG.parameters(),
lr=LEARNING_RATE,
betas=(0.5, 0.999))
dataset = epfd.MNISTDataSetForPytorch(root=DATA_PATH,
transform=tv.transforms.Compose(
[tv.transforms.ToTensor()]))
train_loader = torch.utils.data.DataLoader(dataset,
batch_size=BATCH_SIZE,
shuffle=True)
fix_noise = torch.randn(100, NOISE_DIM)
fix_noise_var = torch.autograd.Variable(fix_noise)
if torch.cuda.is_available() > 0:
NetG = NetG.cuda()
NetD = NetD.cuda()
MSE_LOSS = MSE_LOSS.cuda()
fix_noise_var = fix_noise_var.cuda()
bar = eup.ProgressBar(EPOCH, len(train_loader), "D Loss:%.3f;G Loss:%.3f")
m.bias.data.zero_()
elif isinstance(m, torch.nn.Linear):
m.weight.data.normal_(0, 0.02)
m.bias.data.zero_()
NetD = Discriminator()
NetG = Generator()
optimizerD = torch.optim.RMSprop(NetD.parameters(), lr=LEARNING_RATE)
optimizerG = torch.optim.RMSprop(NetG.parameters(), lr=LEARNING_RATE)
trans = tv.transforms.Compose(
[tv.transforms.ToTensor(),
tv.transforms.Normalize([0.5], [0.5])])
dataset = epfd.MNISTDataSetForPytorch(root=DATA_PATH, transform=trans)
train_loader = torch.utils.data.DataLoader(dataset,
batch_size=BATCH_SIZE,
shuffle=True)
one = torch.FloatTensor([1])
mone = -1 * one
one_var = torch.autograd.Variable(one)
mone_var = torch.autograd.Variable(mone)
fix_noise = torch.FloatTensor(100, NOISE_DIM).normal_(0, 1)
fix_noise_var = torch.autograd.Variable(fix_noise)
if torch.cuda.is_available() > 0:
NetG = NetG.cuda()
NetD = NetD.cuda()
return network
NetD = Discriminator()
NetG = Generator()
optimizerD = torch.optim.Adam(NetD.parameters(),
lr=LEARNING_RATE,
betas=(0.5, 0.999))
optimizerG = torch.optim.Adam(NetG.parameters(),
lr=LEARNING_RATE,
betas=(0.5, 0.999))
dataset = epfd.MNISTDataSetForPytorch(
root=DATA_PATH,
transform=tv.transforms.Compose([
# tv.transforms.Resize(CONFIG["IMAGE_SIZE"]),
tv.transforms.ToTensor(),
# tv.transforms.Normalize([0.5] * 3, [0.5] * 3)
]))
train_loader = torch.utils.data.DataLoader(dataset,
batch_size=BATCH_SIZE,
shuffle=True)
fix_noise = torch.randn(100, NOISE_DIM)
fix_noise_var = torch.autograd.Variable(fix_noise)
if torch.cuda.is_available() > 0:
NetG = NetG.cuda()
NetD = NetD.cuda()
fix_noise_var = fix_noise_var.cuda()