def make_group_generator():
# Note that this Variable is NOT going to show up in `net.parameters()` and
# therefore it is implicitly free from the ridge penalty/p(theta) prior.
log_sigma = Variable(torch.log(1e-2 * torch.ones(image_size)),
requires_grad=True)
return NormalNet(mu_net=torch.nn.Linear(group_input_dim, image_size),
sigma_net=Lambda(lambda x, log_sigma: torch.exp(
log_sigma.expand(x.size(0), -1)) + 1e-3,
extra_args=(log_sigma, )))
A = torch.zeros(image_size, image_size)
A[i, :] = 0.5
A = torch.Tensor(A)
data.append(A.view(-1))
temp = torch.stack([data[i] for i in range(len(data))])
X = torch.stack([temp for _ in range(num_samples)])
X += 0.05 * torch.randn(X.size())
X = X.transpose(0, 1)
stddev_multiple = 0.1
inference_net = NormalNet(mu_net=nn.Sequential(nn.Linear(dim_h + dim_h,
dim_z)),
sigma_net=torch.nn.Sequential(
nn.Linear(dim_h + dim_h, dim_z),
Lambda(torch.exp),
Lambda(lambda x: x * stddev_multiple + 1e-3)))
def make_group_generator():
# Note that this Variable is NOT going to show up in `net.parameters()` and
# therefore it is implicitly free from the ridge penalty/p(theta) prior.
log_sigma = Variable(torch.log(1e-2 * torch.ones(image_size)),
requires_grad=True)
return NormalNet(mu_net=torch.nn.Sequential(
torch.nn.Tanh(), torch.nn.Linear(group_input_dim, image_size)),
sigma_net=Lambda(lambda x, log_sigma: torch.exp(
log_sigma.expand(x.size(0), -1)) + 1e-3,
extra_args=(log_sigma, )))
# `inference_net` below. Zero means that the model has no actual connection to
# the output and therefore the standard deviation defaults to the minimum. One
# means that we're learning the real model. This value is flipped to 1 after
# some number of iterations.
stddev_multiple = 0.1
inference_net = NormalNet(
mu_net=torch.nn.Sequential(
# inference_net_base,
torch.nn.Linear(dim_x, dim_z)),
# Fixed standard deviation
# sigma_net=Lambda(lambda x: 1e-3 * Variable(torch.ones(x.size(0), dim_z)))
# Learned constant standard deviation
# sigma_net=Lambda(
# lambda x: torch.exp(inference_net_log_stddev.expand(x.size(0), -1)) + 1e-3
# )
# Learned standard deviation as a function of the input
sigma_net=torch.nn.Sequential(
# inference_net_base,
torch.nn.Linear(dim_x, dim_z),
Lambda(torch.exp),
Lambda(lambda x: x * stddev_multiple + 1e-3)))
def make_group_generator(group_output_dim):
# Note that this Variable is NOT going to show up in `net.parameters()` and
# therefore it is implicitly free from the ridge penalty/p(theta) prior.
log_sigma = Variable(torch.log(1e-2 * torch.ones(group_output_dim).type(
shuffle=True
)
dim_x = metrain.size(2)
num_groups = len(groups)
#group_dims = 196
stddev_multiple = 1
group_dims = [196 for grp in groups]
inference_net = NormalNet(
mu_net=torch.nn.Sequential(
# inference_net_base,
torch.nn.Linear(dim_h + dim_h, dim_z)
),
sigma_net=torch.nn.Sequential(
# inference_net_base,
torch.nn.Linear(dim_h + dim_h, dim_z),
Lambda(torch.exp),
Lambda(lambda x: x * stddev_multiple + 1e-3)
)
)
def make_group_generator(output_dim):
# Note that this Variable is NOT going to show up in `net.parameters()` and
# therefore it is implicitly free from the ridge penalty/p(theta) prior.
log_sigma = Variable(
torch.log(1e-2 * torch.ones(output_dim)),
requires_grad=True
)
return NormalNet(