def get_valid_stats(args, trainer):
stats = metrics.get_smoothed_values('valid')
if 'nll_loss' in stats and 'ppl' not in stats:
stats['ppl'] = utils.get_perplexity(stats['nll_loss'])
stats['num_updates'] = trainer.get_num_updates()
if hasattr(checkpoint_utils.save_checkpoint, 'best'):
key = 'best_{0}'.format(args.best_checkpoint_metric)
best_function = max if args.maximize_best_checkpoint_metric else min
stats[key] = best_function(
checkpoint_utils.save_checkpoint.best,
stats[args.best_checkpoint_metric],
)
return stats
def get_training_stats(stats_key):
stats = metrics.get_smoothed_values(stats_key)
if 'nll_loss' in stats and 'ppl' not in stats:
stats['ppl'] = utils.get_perplexity(stats['nll_loss'])
stats['wall'] = round(metrics.get_meter('default', 'wall').elapsed_time, 0)
return stats