wassname/pytorch-ts
1
0
Fork 0
mirror of https://github.com/wassname/pytorch-ts.git synced 2026-07-10 06:28:10 +08:00
Code Issues Packages Projects Releases Wiki Activity

put back scaling to flow

Browse Source
This commit is contained in:
Dr. Kashif Rasul
2020-01-15 13:12:23 +01:00
parent 9938121a42
commit b5ef5f90c2
1 changed files with 7 additions and 9 deletions
Download Patch File Download Diff File Expand all files Collapse all files
pts/model/tempflow/tempflow_network.py
+7 -9
View File
@@ -278,9 +278,7 @@ class TempFlowTrainingNetwork(nn.Module):
return outputs, states, scale, lags_scaled, inputs
def distr_args(
self, rnn_outputs: torch.Tensor, scale: torch.Tensor,
):
def distr_args(self, rnn_outputs: torch.Tensor):
"""
Returns the distribution of DeepVAR with respect to the RNN outputs.
@@ -298,7 +296,7 @@ class TempFlowTrainingNetwork(nn.Module):
distr_args
Distribution arguments
"""
distr_args, = self.proj_dist_args(rnn_outputs)
(distr_args,) = self.proj_dist_args(rnn_outputs)
# # compute likelihood of target given the predicted parameters
# distr = self.distr_output.distribution(distr_args, scale=scale)
@@ -380,7 +378,8 @@ class TempFlowTrainingNetwork(nn.Module):
# assert_shape(target, (-1, seq_len, self.target_dim))
distr_args = self.distr_args(rnn_outputs=rnn_outputs, scale=scale)
distr_args = self.distr_args(rnn_outputs=rnn_outputs)
self.flow.scale = scale
# we sum the last axis to have the same shape for all likelihoods
# (batch_size, subseq_length, 1)
@@ -432,7 +431,7 @@ class TempFlowPredictionNetwork(TempFlowTrainingNetwork):
target_dimension_indicator: torch.Tensor,
time_feat: torch.Tensor,
scale: torch.Tensor,
begin_states: Union[List[torch.Tensor], torch.Tensor]
begin_states: Union[List[torch.Tensor], torch.Tensor],
) -> torch.Tensor:
"""
Computes sample paths by unrolling the RNN starting with a initial
@@ -495,9 +494,8 @@ class TempFlowPredictionNetwork(TempFlowTrainingNetwork):
unroll_length=1,
)
distr_args = self.distr_args(
rnn_outputs=rnn_outputs, scale=repeated_scale,
)
distr_args = self.distr_args(rnn_outputs=rnn_outputs,)
self.flow.scale = repeated_scale
# (batch_size, 1, target_dim)
new_samples = self.flow.sample(cond=distr_args)