def __init__(
self,
n_conditionals,
n_inputs,
n_hiddens,
n_mades,
n_components=10,
activation="relu",
batch_norm=True,
input_order="sequential",
mode="sequential",
alpha=0.1,
):
super(ConditionalMixtureMaskedAutoregressiveFlow, self).__init__(n_conditionals, n_inputs)
# save input arguments
self.n_conditionals = n_conditionals
self.n_inputs = n_inputs
self.n_hiddens = n_hiddens
self.n_mades = n_mades
self.activation = activation
self.batch_norm = batch_norm
self.mode = mode
self.alpha = alpha
self.n_components = n_components
# Dtype and GPU / CPU management
self.to_args = None
self.to_kwargs = None
# Build MADEs
self.mades = nn.ModuleList()
for i in range(n_mades - 1):
made = ConditionalGaussianMADE(
n_conditionals, n_inputs, n_hiddens, activation=activation, input_order=input_order, mode=mode
)
self.mades.append(made)
if not (isinstance(input_order, str) and input_order != "random"):
input_order = made.input_order[::-1]
# Last MADE MoG
self.made_mog = ConditionalMixtureMADE(
n_conditionals,
n_inputs,
n_hiddens,
n_components=n_components,
activation=activation,
input_order=input_order,
mode=mode,
)
# Batch normalizatino
self.bns = None
if self.batch_norm:
self.bns = nn.ModuleList()
for i in range(n_mades):
bn = BatchNorm(n_inputs, alpha=self.alpha)
self.bns.append(bn)
def __init__(self,
n_inputs,
n_hiddens,
n_mades,
activation='relu',
batch_norm=True,
input_order='sequential',
mode='sequential',
alpha=0.1):
super(MaskedAutoregressiveFlow, self).__init__(n_inputs)
# save input arguments
self.n_inputs = n_inputs
self.n_hiddens = n_hiddens
self.n_mades = n_mades
self.activation = activation
self.batch_norm = batch_norm
self.mode = mode
self.alpha = alpha
# Dtype and GPU / CPU management
self.to_args = None
self.to_kwargs = None
# Build MADEs
self.mades = nn.ModuleList()
for i in range(n_mades):
made = GaussianMADE(n_inputs,
n_hiddens,
activation=activation,
input_order=input_order,
mode=mode)
self.mades.append(made)
if not (isinstance(input_order, str) and input_order == 'random'):
input_order = made.input_order[::-1]
# Batch normalization
self.bns = None
if self.batch_norm:
self.bns = nn.ModuleList()
for i in range(n_mades):
bn = BatchNorm(n_inputs, alpha=self.alpha)
self.bns.append(bn)