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Implemented lr annealing schedule

This commit is contained in:
Chintan Shah 2019-10-04 21:18:05 -04:00
parent ba880b8230
commit 8d3b1d0d66
1 changed files with 12 additions and 6 deletions
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18
model/pytorch/dcrnn_supervisor.py
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@ -119,6 +119,9 @@ class DCRNNSupervisor:
wait = 0 wait = 0
batches_seen = 0 batches_seen = 0
optimizer = torch.optim.Adam(self.dcrnn_model.parameters(), lr=base_lr) optimizer = torch.optim.Adam(self.dcrnn_model.parameters(), lr=base_lr)
lr_scheduler = torch.optim.lr_scheduler.MultiStepLR(optimizer, milestones=steps,
gamma=lr_decay_ratio)
criterion = torch.nn.L1Loss() # mae loss criterion = torch.nn.L1Loss() # mae loss
self.dcrnn_model = self.dcrnn_model.train() self.dcrnn_model = self.dcrnn_model.train()
@ -149,20 +152,23 @@ class DCRNNSupervisor:
optimizer.step() optimizer.step()
lr_scheduler.step()
val_loss = self.evaluate(dataset='val') val_loss = self.evaluate(dataset='val')
end_time = time.time() end_time = time.time()
if epoch_num % log_every == 0: if epoch_num % log_every == 0:
message = 'Epoch [{}/{}] ({}) train_mae: {:.4f}, val_mae: {:.4f} ' \ message = 'Epoch [{}/{}] ({}) train_mae: {:.4f}, val_mae: {:.4f}, lr: {:.6f}' \
'{:.1f}s'.format(epoch_num, epochs, batches_seen, '{:.1f}s'.format(epoch_num, epochs, batches_seen,
np.mean(losses), val_loss, np.mean(losses), val_loss, lr_scheduler.get_lr(),
(end_time - start_time)) (end_time - start_time))
self._logger.info(message) self._logger.info(message)
if epoch_num % test_every_n_epochs == 0: if epoch_num % test_every_n_epochs == 0:
test_loss = self.evaluate(dataset='test') test_loss = self.evaluate(dataset='test')
message = 'Epoch [{}/{}] ({}) train_mae: {:.4f}, test_mae: {:.4f} ' \ message = 'Epoch [{}/{}] ({}) train_mae: {:.4f}, test_mae: {:.4f}, lr: {:.6f} ' \
'{:.1f}s'.format(epoch_num, epochs, batches_seen, '{:.1f}s'.format(epoch_num, epochs, batches_seen,
np.mean(losses), test_loss, (end_time - start_time)) np.mean(losses), test_loss, lr_scheduler.get_lr(),
(end_time - start_time))
self._logger.info(message) self._logger.info(message)
if val_loss < min_val_loss: if val_loss < min_val_loss:
@ -171,8 +177,8 @@ class DCRNNSupervisor:
if save_model: if save_model:
model_file_name = self.save_model(epoch_num) model_file_name = self.save_model(epoch_num)
self._logger.info( self._logger.info(
'Val loss decrease from {:.4f} to {:.4f}, saving to {}'.format(min_val_loss, val_loss, 'Val loss decrease from {:.4f} to {:.4f}, '
model_file_name)) 'saving to {}'.format(min_val_loss, val_loss, model_file_name))
elif val_loss >= min_val_loss: elif val_loss >= min_val_loss:
wait += 1 wait += 1
if wait == patience: if wait == patience: