def __init__(self, ensemble_size, in_features, out_features):
super().__init__()
self.num_nets = ensemble_size
self.in_features = in_features
self.out_features = out_features
self.lin0_w, self.lin0_b = get_affine_params(ensemble_size,
in_features, 200)
self.lin1_w, self.lin1_b = get_affine_params(ensemble_size, 200, 200)
self.lin2_w, self.lin2_b = get_affine_params(ensemble_size, 200, 200)
self.lin3_w, self.lin3_b = get_affine_params(ensemble_size, 200,
out_features)
self.inputs_mu = nn.Parameter(torch.zeros(in_features),
requires_grad=False)
self.inputs_sigma = nn.Parameter(torch.zeros(in_features),
requires_grad=False)
self.max_logvar = nn.Parameter(
torch.ones(1, out_features // 2, dtype=torch.float32) / 2.0)
self.min_logvar = nn.Parameter(
-torch.ones(1, out_features // 2, dtype=torch.float32) * 10.0)
def __init__(self, ensemble_size, in_features, out_features, hidden_size,
num_layers, weight_decays):
super().__init__()
self.num_nets = ensemble_size
self.in_features = in_features
self.out_features = out_features
self.num_layers = num_layers
self.weight_decays = weight_decays
self.linear_layers = nn.ParameterList()
self.linear_layers.extend(
get_affine_params(ensemble_size, in_features, hidden_size))
for i in range(num_layers - 2):
self.linear_layers.extend(
get_affine_params(ensemble_size, hidden_size, hidden_size))
self.linear_layers.extend(
get_affine_params(ensemble_size, hidden_size, out_features))
self.inputs_mu = nn.Parameter(torch.zeros(1, in_features),
requires_grad=False)
self.inputs_sigma = nn.Parameter(torch.zeros(1, in_features),
requires_grad=False)
self.max_logvar = nn.Parameter(
torch.ones(1, out_features // 2, dtype=torch.float32) / 2.0)
self.min_logvar = nn.Parameter(
-torch.ones(1, out_features // 2, dtype=torch.float32) * 10.0)
