val_loss = evaluate(val_data)
msglogger.info('-' * 89)
msglogger.info(
'| end of epoch {:3d} | time: {:5.2f}s | valid loss {:5.3f} | '
'valid ppl {:8.2f}'.format(epoch, (time.time() - epoch_start_time),
val_loss, math.exp(val_loss)))
msglogger.info('-' * 89)
distiller.log_weights_sparsity(model,
epoch,
loggers=[tflogger, pylogger])
stats = ('Peformance/Validation/',
OrderedDict([('Loss', val_loss),
('Perplexity', math.exp(val_loss))]))
tflogger.log_training_progress(stats, epoch, 0, total=1, freq=1)
with open(args.save, 'wb') as f:
torch.save(model, f)
# Save the model if the validation loss is the best we've seen so far.
if val_loss < best_val_loss:
with open(args.save + ".best", 'wb') as f:
torch.save(model, f)
best_val_loss = val_loss
lr_scheduler.step(val_loss)
if compression_scheduler:
compression_scheduler.on_epoch_end(epoch)
except KeyboardInterrupt:
val_loss = evaluate(val_data)
msglogger.info('-' * 89)
msglogger.info(
'| end of epoch {:3d} | time: {:5.2f}s | valid loss {:5.3f} | '
'valid ppl {:8.2f}'.format(epoch, (time.time() - epoch_start_time),
val_loss, math.exp(val_loss)))
msglogger.info('-' * 89)
distiller.log_weights_sparsity(model,
epoch,
loggers=[tflogger, pylogger])
stats = ('Performance/Validation/',
OrderedDict([('Loss', val_loss),
('Perplexity', math.exp(val_loss))]))
tflogger.log_training_progress(stats, epoch, None)
with open(args.save, 'wb') as f:
torch.save(model, f)
# Save the model if the validation loss is the best we've seen so far.
if val_loss < best_val_loss:
with open(args.save + ".best", 'wb') as f:
torch.save(model, f)
best_val_loss = val_loss
lr_scheduler.step(val_loss)
if compression_scheduler:
compression_scheduler.on_epoch_end(epoch)
except KeyboardInterrupt: