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the decoder of the DCRNNModel in model/pytorch/dcrnn_model.py seems to be fed with its own output when use_curriculum_learning is set to False:
for t in range(self.decoder_model.horizon):
decoder_output, decoder_hidden_state = self.decoder_model(decoder_input,
decoder_hidden_state)
decoder_input = decoder_output
outputs.append(decoder_output)
if self.training and self.use_curriculum_learning:
c = np.random.uniform(0, 1)
if c < self._compute_sampling_threshold(batches_seen):
decoder_input = labels[t]
However, I think it should be fed with ground truth labels, isn't it?
cfr. DCRNN paper, page 4 1st line
The text was updated successfully, but these errors were encountered:
the decoder of the
DCRNNModel
inmodel/pytorch/dcrnn_model.py
seems to be fed with its own output whenuse_curriculum_learning
is set toFalse
:However, I think it should be fed with ground truth labels, isn't it?
cfr. DCRNN paper, page 4 1st line
The text was updated successfully, but these errors were encountered: