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DCRNN
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logging and refactor

This commit is contained in:
Chintan Shah 2019-10-06 15:15:11 -04:00
parent 55a087ac9f
commit ec5d9555a5
1 changed files with 3 additions and 3 deletions
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6
model/pytorch/dcrnn_supervisor.py
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@ -163,15 +163,15 @@ class DCRNNSupervisor:
if epoch_num % log_every == 0:
message = 'Epoch [{}/{}] ({}) train_mae: {:.4f}, val_mae: {:.4f}, lr: {:.6f}' \
'{:.1f}s'.format(epoch_num, epochs, batches_seen,
np.mean(losses), val_loss, lr_scheduler.get_lr(),
np.mean(losses), val_loss, lr_scheduler.get_lr()[0],
(end_time - start_time))
self._logger.info(message)
if epoch_num % test_every_n_epochs == 0:
test_loss = self.evaluate(dataset='test')
message = 'Epoch [{}/{}] ({}) train_mae: {:.4f}, test_mae: {:.4f}, lr: {:.6f} ' \
message = 'Epoch [{}/{}] ({}) train_mae: {:.4f}, test_mae: {:.4f}, lr: {:.6f}, ' \
'{:.1f}s'.format(epoch_num, epochs, batches_seen,
np.mean(losses), test_loss, lr_scheduler.get_lr(),
np.mean(losses), test_loss, lr_scheduler.get_lr()[0],
(end_time - start_time))
self._logger.info(message)