annotate=args.annotate,
device_idxs=device_idx,
gpu_memory_limit=args.gpu_memory_limit)
if len(args.retrain_model) != 0:
trainer.retrain_model(retrain_model=args.retrain_model,
processed_steps=args.processed_steps)
try:
trainer.run()
print('Logging stats to file ... ')
stats.log_to_file('*' * 80)
except KeyboardInterrupt:
print('Keyboard interrupted. Logging stats to file ... ')
finally:
stats.train_stats_to_file()
stats.valid_stats_to_file()
if args.eval_type == 'acc':
sorted_values = sorted(stats.valid_acc.items(),
key=lambda d: d[1],
reverse=True)
elif args.eval_type == 'xent':
sorted_values = sorted(stats.valid_loss.items(), key=lambda d: d[1])
else:
sorted_values = sorted(stats.valid_bleu.items(),
key=lambda d: d[1],
reverse=True)
stats.log_to_file('Model performances (%s): ' % args.eval_type)
print('Model performances (%s): ' % args.eval_type)
for (step, value) in sorted_values:
