def compute_test_loss():
log.info("computing test loss")
test_loss = compute_loss_all(encdec, test_data, eos_idx, mb_size, gpu=gpu,
reverse_src=reverse_src, reverse_tgt=reverse_tgt,
use_chainerx=use_chainerx)
log.info("test loss: %f" % test_loss)
return test_loss
def compute_valid_loss():
log.info("computing valid loss")
dev_loss = compute_loss_all(encdec, valid_data, eos_idx, mb_size, gpu=gpu,
reverse_src=reverse_src, reverse_tgt=reverse_tgt,
use_chainerx=use_chainerx)
log.info("valid loss: %f" % dev_loss)
return dev_loss
def __call__(self, trainer):
encdec = trainer.updater.get_optimizer("main").target
log.info("computing %s" % self.observation_name)
dev_loss = compute_loss_all(encdec, self.data, self.eos_idx, self.mb_size,
gpu=self.gpu,
reverse_src=self.reverse_src, reverse_tgt=self.reverse_tgt,
use_chainerx=self.use_chainerx)
log.info("%s: %f (current best: %r)" % (self.observation_name, dev_loss, self.best_loss))
chainer.reporter.report({self.observation_name: dev_loss})
if self.best_loss is None or self.best_loss > dev_loss:
log.info("loss (%s) improvement: %r -> %r" % (self.observation_name,
self.best_loss, dev_loss))
self.best_loss = dev_loss
if self.save_best_model_to is not None:
log.info("saving best loss (%s) model to %s" % (self.observation_name, self.save_best_model_to,))
serializers.save_npz(self.save_best_model_to, encdec)
if self.config_training is not None:
config_session = self.config_training.copy(readonly=False)
config_session.add_section("model_parameters", keep_at_bottom="metadata")
config_session["model_parameters"]["filename"] = self.save_best_model_to
config_session["model_parameters"]["type"] = "model"
config_session["model_parameters"]["description"] = "best_loss"
config_session["model_parameters"]["infos"] = argument_parsing_tools.OrderedNamespace()
config_session["model_parameters"]["infos"]["loss"] = float(dev_loss)
config_session["model_parameters"]["infos"]["iteration"] = trainer.updater.iteration
config_session.set_metadata_modified_time()
config_session.save_to(self.save_best_model_to + ".config")
def compute_dev_loss():
log.info("computing dev loss")
dev_loss = compute_loss_all(encdec, dev_data, eos_idx, mb_size, gpu=gpu,
reverse_src=reverse_src, reverse_tgt=reverse_tgt)
log.info("dev loss: %f" % dev_loss)
return dev_loss