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[WIP] Add joint NLL computation to plex, deterministic and heteroscedastic models. #552

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[WIP] Add joint NLL computation to plex, deterministic and heteroscedastic models. #552

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13 changes: 10 additions & 3 deletions edward2/jax/nn/heteroscedastic_lib.py
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Original file line number Diff line number Diff line change
Expand Up @@ -65,6 +65,7 @@ class MCSoftmaxDenseFA(nn.Module):
share_samples_across_batch: bool = False
logits_only: bool = False
return_locs: bool = False
return_unaveraged_logits: bool = False
eps: float = 1e-7
tune_temperature: bool = False
temperature_lower_bound: Optional[float] = None
Expand Down Expand Up @@ -251,9 +252,10 @@ def _compute_mc_samples(self, inputs, scale, num_samples):
# [B, S, dim] -> [B, S, K]
latents = self._compute_loc_param(latents) # pylint: disable=assignment-from-none

samples = jax.nn.softmax(latents / self.get_temperature())
scaled_latents = latents / self.get_temperature()
samples = jax.nn.softmax(scaled_latents)

return jnp.mean(samples, axis=1)
return jnp.mean(samples, axis=1), jax.nn.log_softmax(scaled_latents)

@nn.compact
def __call__(self, inputs, training=True):
Expand All @@ -278,7 +280,8 @@ def __call__(self, inputs, training=True):
else:
total_mc_samples = self.test_mc_samples

probs_mean = self._compute_mc_samples(inputs, scale, total_mc_samples)
probs_mean, unaveraged_logits = self._compute_mc_samples(
inputs, scale, total_mc_samples)

probs_mean = jnp.clip(probs_mean, a_min=self.eps)
log_probs = jnp.log(probs_mean)
Expand All @@ -288,8 +291,12 @@ def __call__(self, inputs, training=True):
logits = self._compute_loc_param(inputs) # pylint: disable=assignment-from-none

if self.logits_only:
if self.return_unaveraged_logits:
return logits, unaveraged_logits
return logits

if self.return_unaveraged_logits:
return logits, log_probs, probs_mean, unaveraged_logits
return logits, log_probs, probs_mean


Expand Down