print('-' * 89)
print('| end of epoch {:3d} | time: {:5.2f}s | valid loss {:5.2f} | '
'valid ppl {:8.2f}'.format(epoch,
(time.time() - epoch_start_time),
val_loss, math.exp(val_loss)))
print('-' * 89)
# Save the model if the validation loss is the best we've seen so far.
if not best_val_loss or val_loss < best_val_loss:
with open(args.logdir + "/" + args.save, 'wb') as f:
torch.save(model, f)
best_val_loss = val_loss
else:
# Anneal the learning rate if no improvement has been seen in the validation dataset.
lr /= 4.0
if args.opt_method == "YF":
optimizer.set_lr_factor(optimizer.get_lr_factor() / 4.0)
else:
for group in optimizer.param_groups:
group['lr'] /= 4.0
if args.opt_method == "YF":
mu_list.append(optimizer._mu)
lr_list.append(optimizer._lr)
with open(args.logdir+"/loss.txt", "wb") as f:
np.savetxt(f, np.array(train_loss_list) )
with open(args.logdir+"/val_loss.txt", "wb") as f:
np.savetxt(f, np.array(val_loss_list) )
with open(args.logdir+"/lr.txt", "wb") as f:
np.savetxt(f, np.array(lr_list) )
with open(args.logdir+"/mu.txt", "wb") as f:
np.savetxt(f, np.array(mu_list) )
val_loss = evaluate()
print('-' * 89)
print('| end of epoch {:3d} | time: {:5.2f}s | valid loss {:5.2f} | '
'valid ppl {:8.2f} |'.format(epoch, (time.time() - epoch_start_time),
val_loss, math.exp(val_loss)))
print('-' * 89)
# Save the model if the validation loss is the best we've seen so far.
if not best_val_loss or val_loss < best_val_loss:
with open(args.save, 'wb') as f:
torch.save(model, f)
best_val_loss = val_loss
lr_decay=1.15
lr /= lr_decay
if args.opt_method == "YF":
optimizer.set_lr_factor(optimizer.get_lr_factor() / lr_decay)
else:
for group in optimizer.param_groups:
group['lr'] /= lr_decay
else:
# Anneal the learning rate if no improvement has been seen in the validation dataset.
#lr /= 3
lr_decay=4
lr /= lr_decay
if args.opt_method == "YF":
optimizer.set_lr_factor(optimizer.get_lr_factor() / lr_decay)
else:
for group in optimizer.param_groups:
group['lr'] /= lr_decay
