def __init__(
self,
features,
hidden_features,
context_features=None,
num_blocks=2,
use_residual_blocks=True,
random_mask=False,
activation=F.relu,
dropout_probability=0.0,
use_batch_norm=False,
):
self.features = features
made = made_module.MADE(
features=features,
hidden_features=hidden_features,
context_features=context_features,
num_blocks=num_blocks,
output_multiplier=self._output_dim_multiplier(),
use_residual_blocks=use_residual_blocks,
random_mask=random_mask,
activation=activation,
dropout_probability=dropout_probability,
use_batch_norm=use_batch_norm,
)
super(MaskedAffineAutoregressiveTransform, self).__init__(made)
def __init__(
self,
features,
hidden_features,
context_features=None,
num_bins=10,
tails=None,
tail_bound=1.0,
num_blocks=2,
use_residual_blocks=True,
random_mask=False,
activation=F.relu,
dropout_probability=0.0,
use_batch_norm=False,
min_bin_width=splines.rational_quadratic.DEFAULT_MIN_BIN_WIDTH,
min_bin_height=splines.rational_quadratic.DEFAULT_MIN_BIN_HEIGHT,
min_derivative=splines.rational_quadratic.DEFAULT_MIN_DERIVATIVE,
):
self.num_bins = num_bins
self.min_bin_width = min_bin_width
self.min_bin_height = min_bin_height
self.min_derivative = min_derivative
self.tails = tails
self.tail_bound = tail_bound
autoregressive_net = made_module.MADE(
features=features,
hidden_features=hidden_features,
context_features=context_features,
num_blocks=num_blocks,
output_multiplier=self._output_dim_multiplier(),
use_residual_blocks=use_residual_blocks,
random_mask=random_mask,
activation=activation,
dropout_probability=dropout_probability,
use_batch_norm=use_batch_norm,
)
super().__init__(autoregressive_net)