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DCRNN
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logging and refactor

This commit is contained in:
Chintan Shah 2019-10-06 14:38:44 -04:00
parent b6a2b3fe8e
commit 02c4681ad9
1 changed files with 2 additions and 2 deletions
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model/pytorch/dcrnn_supervisor.py
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@ -129,7 +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)) 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 = []
@ -144,7 +144,7 @@ class DCRNNSupervisor:
output = self.dcrnn_model(x, y, batches_seen) output = self.dcrnn_model(x, y, batches_seen)
loss = self._compute_loss(y, output, criterion) loss = self._compute_loss(y, output, criterion)
self._logger.info(loss.item()) self._logger.debug(loss.item())
losses.append(loss.item()) losses.append(loss.item())