)
for i, (imgs, Labels) in enumerate(dataloader_FULL_te):
data_all_te = imgs
label_all_te = Labels
# Repeat experiments K times (K = 10) and report average test power (rejection rate)
for kk in range(K):
torch.manual_seed(kk * 19 + N1)
torch.cuda.manual_seed(kk * 19 + N1)
np.random.seed(seed=1102 * (kk + 10) + N1)
# Initialize deep networks for MMD-D (called featurizer), C2ST-S and C2ST-L (called discriminator)
featurizer = Featurizer()
discriminator = Discriminator()
# Initialize parameters
epsilonOPT = torch.log(
MatConvert(np.random.rand(1) * 10**(-10), device, dtype))
epsilonOPT.requires_grad = True
sigmaOPT = MatConvert(np.ones(1) * np.sqrt(2 * 32 * 32), device, dtype)
sigmaOPT.requires_grad = True
sigma0OPT = MatConvert(np.ones(1) * np.sqrt(0.005), device, dtype)
sigma0OPT.requires_grad = True
print(epsilonOPT.item())
if cuda:
featurizer.cuda()
discriminator.cuda()
adversarial_loss.cuda()
# Collect real MNIST images
np.random.seed(seed=819 * (kk + 9) + N1)
train_data = []
ind_M_all = np.arange(4000)
d0 = trans(data_T_tensor[i])
data_trans[i] = TT(d0)
Ind_v4_all = np.arange(len(data_T))
# Repeat experiments K times (K = 10) and report average test power (rejection rate)
for kk in range(K):
print(kk)
torch.manual_seed(kk * 19 + N1)
torch.cuda.manual_seed(kk * 19 + N1)
np.random.seed(seed=1102 * (kk + 10) + N1)
# Initialize deep networks for MMD-D
featurizer = Featurizer()
discriminator = Discriminator()
# Initialize parameters
epsilonOPT = torch.log(
MatConvert(np.random.rand(1) * 10**(-10), device, dtype))
epsilonOPT.requires_grad = True
sigmaOPT = MatConvert(np.ones(1) * np.sqrt(2 * 32 * 32), device, dtype)
sigmaOPT.requires_grad = True
sigma0OPT = MatConvert(np.ones(1) * np.sqrt(0.005), device, dtype)
sigma0OPT.requires_grad = True
TT_org = MatConvert(np.random.randn(J, 3, 64, 64), device, dtype)
TT_org.requires_grad = True
print(epsilonOPT.item())
if cuda:
featurizer.cuda()
discriminator.cuda()
adversarial_loss.cuda()
# Collect CIFAR10 images
Ind_tr = np.random.choice(len(data_all), N1, replace=False)
torch.cuda.manual_seed(kk * 19 + n)
# Generate HDGM-D
for i in range(Num_clusters):
np.random.seed(seed=1102*kk + i + n)
s1[n * (i):n * (i + 1), :] = np.random.multivariate_normal(mu_mx[i], sigma_mx_1, n)
for i in range(Num_clusters):
np.random.seed(seed=819*kk + 1 + i + n)
s2[n * (i):n * (i + 1), :] = np.random.multivariate_normal(mu_mx[i], sigma_mx_2[i], n)
# REPLACE above line with
# s2[n * (i):n * (i + 1), :] = np.random.multivariate_normal(mu_mx[i], sigma_mx_1, n)
# for validating type-I error (s1 ans s2 are from the same distribution)
if kk==0:
s1_o = s1
s2_o = s2
S = np.concatenate((s1, s2), axis=0)
S = MatConvert(S, device, dtype)
N1 = Num_clusters*n
N2 = Num_clusters*n
# Train C2ST-S
y = (torch.cat((torch.zeros(N1, 1), torch.ones(N2, 1)), 0)).squeeze(1).to(device, dtype).long()
pred, STAT_C2ST_S, model_C2ST_S, w_C2ST_S, b_C2ST_S = C2ST_NN_fit(S, y, N1, x_in, H, x_out, 0.001, N_epoch_C,
batch_size, device, dtype)
# Train C2ST-L
np.random.seed(seed=819*kk + 1 + i + n)
y = (torch.cat((torch.zeros(N1, 1), torch.ones(N2, 1)), 0)).squeeze(1).to(device, dtype).long()
pred, STAT_C2ST_L, model_C2ST_L, w_C2ST_L, b_C2ST_L = C2ST_NN_fit(S, y, N1, x_in, H, x_out, 0.001, N_epoch_C,
batch_size, device, dtype)
# Train MMD-O
np.random.seed(seed=1102)
for i in range(Num_clusters):
np.random.seed(seed=1102 * kk + i + n)
s1[n * (i):n * (i + 1), :] = np.random.multivariate_normal(
mu_mx[i], sigma_mx_1, n)
for i in range(Num_clusters):
np.random.seed(seed=819 * kk + 1 + i + n)
s2[n * (i):n * (i + 1), :] = np.random.multivariate_normal(
mu_mx[i], sigma_mx_2[i], n)
# REPLACE above line with
# s2[n * (i):n * (i + 1), :] = np.random.multivariate_normal(mu_mx[i], sigma_mx_1, n)
# for validating type-I error (s1 ans s2 are from the same distribution)
if kk == 0:
s1_o = s1
s2_o = s2
S = np.concatenate((s1, s2), axis=0)
S = MatConvert(S, device, dtype)
N1 = Num_clusters * n
N2 = Num_clusters * n
# Train deep networks for G+C and D+C
y = (torch.cat((torch.zeros(N1, 1), torch.ones(N2, 1)),
0)).squeeze(1).to(device, dtype).long()
pred, STAT_C2ST, model_C2ST, w_C2ST, b_C2ST = C2ST_NN_fit(
S, y, N1, x_in, H, x_out, learning_rate_C2ST, N_epoch_C, batch_size,
device, dtype)
# Train G+J
np.random.seed(seed=1102)
torch.manual_seed(1102)
torch.cuda.manual_seed(1102)
for t in range(N_epoch):
modelu1_output = model_u1(S)