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DCRNN
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logging and refactor

This commit is contained in:
Chintan Shah 2019-10-06 14:34:58 -04:00
parent 036e552bf6
commit b6a2b3fe8e
1 changed files with 1 additions and 1 deletions
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model/pytorch/dcrnn_supervisor.py
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@ -129,6 +129,7 @@ class DCRNNSupervisor:
self.dcrnn_model = self.dcrnn_model.train() self.dcrnn_model = self.dcrnn_model.train()
self._logger.info('Start training ...') self._logger.info('Start training ...')
self._logger.info("num_batches:".format(self._data['train_loader'].num_batch))
for epoch_num in range(epochs): for epoch_num in range(epochs):
train_iterator = self._data['train_loader'].get_iterator() train_iterator = self._data['train_loader'].get_iterator()
losses = [] losses = []
@ -154,7 +155,6 @@ class DCRNNSupervisor:
torch.nn.utils.clip_grad_norm_(self.dcrnn_model.parameters(), self.max_grad_norm) torch.nn.utils.clip_grad_norm_(self.dcrnn_model.parameters(), self.max_grad_norm)
optimizer.step() optimizer.step()
self._logger.info("finished one batch in {:.1f}s".format(time.time() - start_time))
lr_scheduler.step() lr_scheduler.step()