print_every = config_d['print_every']
z_size = config_d['noise_dim']
n_critic = config_d['critic_iter']
lr = config_d['lr']
beta1 = config_d['beta1']
beta2 = config_d['beta2']
D = Discriminator()
G = Generator(z_size=z_size)
print(D)
print(G)
if cuda:
G.cuda()
D.cuda()
print('GPU available for training. Models moved to GPU')
else:
print('Training on CPU.')
d_optimizer = optim.Adam(D.parameters(), lr=lr, betas=[beta1, beta2])
g_optimizer = optim.Adam(G.parameters(), lr=lr, betas=[beta1, beta2])
losses_train = []
losses_val = []
reg_lambda = config_d['gp_lambda']
Nbatch = sdat_train.get_batch_size()
N_train_btot = sdat_train.get_Nbatches_tot()
N_val_btot = sdat_val.get_Nbatches_tot()
# os.makedirs(save_path, exist_ok=True)
root_path = "/Users/yuming/OneDrive/sync/semester/ML/hw/project/dataset/"
data_path = root_path + "faces/"
save_path = root_path + "fake-faces/"
save_csv_loss_g = root_path + "csv/loss_g.csv"
save_csv_loss_d = root_path + "csv/loss_d.csv"
# Initialize generator and discriminator
generator = Generator(opt.latent_dim, img_shape)
discriminator = Discriminator(img_shape)
cuda_enabled = torch.cuda.is_available()
if cuda_enabled:
generator.cuda()
discriminator.cuda()
Tensor = torch.cuda.FloatTensor
else:
Tensor = torch.FloatTensor
# Tensor = torch.cuda.FloatTensor if cuda_enabled else torch.FloatTensor
image_raw_data = fetch_dataset(data_path)
data_loader = DataLoader(image_raw_data,
batch_size=opt.batch_size,
shuffle=True)
# Optimizers
optimizer_G = torch.optim.Adam(generator.parameters(),
lr=opt.lr,
betas=(opt.b1, opt.b2))
optimizer_D = torch.optim.Adam(discriminator.parameters(),