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DCRNN
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logging and refactor

This commit is contained in:
Chintan Shah 2019-10-06 15:22:57 -04:00
parent ec5d9555a5
commit 6331173f44
1 changed files with 3 additions and 2 deletions
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5
model/pytorch/dcrnn_supervisor.py
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@ -161,7 +161,7 @@ class DCRNNSupervisor:
val_loss = self.evaluate(dataset='val')
end_time = time.time()
if epoch_num % log_every == 0:
message = 'Epoch [{}/{}] ({}) train_mae: {:.4f}, val_mae: {:.4f}, lr: {:.6f}' \
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()[0],
(end_time - start_time))
@ -177,12 +177,13 @@ class DCRNNSupervisor:
if val_loss < min_val_loss:
wait = 0
min_val_loss = val_loss
if save_model:
model_file_name = self.save_model(epoch_num)
self._logger.info(
'Val loss decrease from {:.4f} to {:.4f}, '
'saving to {}'.format(min_val_loss, val_loss, model_file_name))
min_val_loss = val_loss
elif val_loss >= min_val_loss:
wait += 1
if wait == patience: