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Conditional MADE cannot estimate first dimension of input #85

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Conditional MADE cannot estimate first dimension of input #85

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18 changes: 11 additions & 7 deletions nflows/nn/nde/made.py
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Original file line number Diff line number Diff line change
Expand Up @@ -227,11 +227,12 @@ def __init__(
raise ValueError("Residual blocks can't be used with random masks.")
super().__init__()

self.output_multiplier = output_multiplier
# Initial layer.
self.initial_layer = MaskedLinear(
in_degrees=_get_input_degrees(features),
in_degrees=_get_input_degrees(features+1),
out_features=hidden_features,
autoregressive_features=features,
autoregressive_features=features+1,
random_mask=random_mask,
is_output=False,
)
Expand All @@ -250,7 +251,7 @@ def __init__(
blocks.append(
block_constructor(
in_degrees=prev_out_degrees,
autoregressive_features=features,
autoregressive_features=features+1,
context_features=context_features,
random_mask=random_mask,
activation=activation,
Expand All @@ -265,20 +266,23 @@ def __init__(
# Final layer.
self.final_layer = MaskedLinear(
in_degrees=prev_out_degrees,
out_features=features * output_multiplier,
autoregressive_features=features,
out_features=(features+1) * output_multiplier,
autoregressive_features=(features+1),
random_mask=random_mask,
is_output=True,
)

def forward(self, inputs, context=None):
temps = self.initial_layer(inputs)
# add dummy input to ensure all dims conditioned on context.
dummy_input = torch.zeros((inputs.shape[:-1]+(1,)))
concat_input = torch.cat((dummy_input,inputs),dim=-1)
temps = self.initial_layer(concat_input)
if context is not None:
temps += self.context_layer(context)
for block in self.blocks:
temps = block(temps, context)
outputs = self.final_layer(temps)
return outputs
return outputs[...,self.output_multiplier:] # remove dummy input


class MixtureOfGaussiansMADE(MADE):
Expand Down