def generate_contaminated_data(
eps, num_data, theta=None,
type_cont="gauss_5",
coord_median_as_origin=True):
if theta is None:
dim = 100
theta = torch.zeros(dim)
randidx = torch.rand(num_data) < eps
dirty_data = generate_dirty_data(type_cont, theta, randidx.sum().item())
data = theta + torch.randn((num_data, len(theta)), device=theta.device)
data[randidx] = dirty_data
if coord_median_as_origin:
from utils import coord_median
coordmedian = coord_median(data)
data = data - coordmedian
theta = theta - coordmedian
return data, theta
theta = theta.to(device)
data_loader = torch.utils.data.DataLoader(TensorDataset(data),
batch_size=args.real_batch_size,
shuffle=True,
num_workers=0)
noise_generator = NoiseGenerator().to(device)
'''
We recommend not using coordinate-wise median as initialization.
The global minimum of Wasserstein GAN has mean square error very close to the coordinate-wise median,
thus we prefer the training starting from somewhere else in order to see the progress of training.
'''
generator = Generator(
p=args.p,
initializer=1.3 * coord_median(data_loader.dataset.tensors[0]),
# 0.5 * torch.ones(args.p),
).to(device)
sinkhorn = SinkhornIteration(lam=args.lam,
max_iter=args.sinkhorn_max_iter,
device=device,
const=args.const,
thres=args.thres)
g_optim = torch.optim.SGD(generator.parameters(),
lr=args.g_sgd_lr,
momentum=args.g_sgd_momentum)
print('initial dist {:.4f}'.format(
torch.norm(generator.eta - theta).item()))
data_loader = torch.utils.data.DataLoader(TensorDataset(data),
batch_size=args.real_batch_size,
shuffle=True,
num_workers=0)
noise_generator = NoiseGenerator().to(device)
'''
Do not use coordinate-wise median as initialization.
The global minimum of MMD GAN has mean square error very close to the coordinate-wise median,
thus we prefer the training starting from somewhere else in order to see the progress of training.
'''
generator = Generator(
p=args.p,
# initializer=torch.ones(args.p),
initializer=1.5 * coord_median(data),
).to(device)
mmd = MMD(sigma=args.sigma, device=device)
g_optim = torch.optim.SGD(generator.parameters(),
lr=args.g_sgd_lr,
momentum=args.g_sgd_momentum)
print('initial dist {:.4f}'.format(
torch.norm(generator.eta - theta).item()))
lst_eta = [generator.get_numpy_eta()]
for i in range(args.num_epoch):
total_loss = 0