def __do_validation(self):
"""Do early-stopping validation."""
if self.ectr >= self.valid_start:
self.vctr += 1
# Compute validation loss
self.model.set_dropout(False)
cur_loss = self.model.val_loss()
self.model.set_dropout(True)
# Add val_loss
self.valid_metrics['loss'].append(cur_loss)
# Print validation loss
self.__print("Validation %2d - LOSS = %.3f (PPL: %.3f)" % (self.vctr, cur_loss, np.exp(cur_loss)))
#############################
# Are we doing beam search? #
#############################
if self.beam_metrics:
beam_results = None
# Save beam search results?
f_valid_out = None
if self.valid_save_hyp:
f_valid_out = "{0}.{1:03d}".format(self.valid_save_prefix, self.vctr)
self.__print('Calling beam-search process')
beam_time = time.time()
beam_results = self.model.run_beam_search(beam_size=self.beam_size,
n_jobs=self.njobs,
metric=self.beam_metrics,
f_valid_out=f_valid_out)
beam_time = time.time() - beam_time
self.__print('Beam-search ended, took %.5f minutes.' % (beam_time / 60.))
if beam_results:
# beam_results: {name: (metric_str, metric_float)}
# names are as defined in metrics/*.py like BLEU, METEOR
# but we use lowercase names in conf files.
self.__send_stats(self.vctr, **beam_results)
for name, (metric_str, metric_value) in beam_results.items():
self.__print("Validation %2d - %s" % (self.vctr, metric_str))
self.valid_metrics[name.lower()].append(metric_value)
else:
self.__print('Skipping this validation since nmt-translate probably failed.')
# Return back to training loop since nmt-translate did not run correctly.
# This will allow to fix your model's build_sampler while training continues.
return
# Is this the best evaluation based on early-stop metric?
if is_last_best(self.early_metric, self.valid_metrics[self.early_metric], self.patience_delta):
self.__save_best_model()
self.early_bad = 0
else:
self.early_bad += 1
self.__print("Early stopping patience: %d validation left" % (self.patience - self.early_bad))
self.__dump_val_summary()
def __do_validation(self):
"""Do early-stopping validation."""
if self.ectr >= self.valid_start:
self.vctr += 1
# Compute validation loss
# self.model.set_dropout(False)
cur_loss = self.model.val_loss()
# self.model.set_dropout(True)
# Add val_loss
self.valid_metrics['loss'].append(cur_loss)
# Print validation loss
self.__print("Validation %2d - ACC = %.3f (LOSS: %.3f)" %
(self.vctr, 1.0 - cur_loss, cur_loss))
f_valid_out = None
if self.valid_save_hyp:
f_valid_out = "{0}.{1:03d}".format(self.valid_save_prefix,
self.vctr)
if is_last_best('loss', self.valid_metrics['loss']):
if self.valid_save_hyp:
# Create a link towards best hypothesis file
force_symlink(f_valid_out,
'%s.BEST' % self.valid_save_prefix,
relative=True)
self.__save_best_model()
self.early_bad = 0
else:
self.early_bad += 1
self.__print("Early stopping patience: %d validation left" %
(self.patience - self.early_bad))
self.__dump_val_summary()
# Khoa: Set the initial accuracy for Discriminator in GAN
if cur_loss < (1 - self.max_acc):
self.__print(
"Reach maximum accuracy %.3f : Current Accuracy = %.3f " %
(self.max_acc, 1 - cur_loss))
return False
else:
return True
return True
def __do_validation(self, min_loss):
"""Do early-stopping validation."""
if self.ectr >= self.valid_start:
self.vctr += 1
# Compute validation loss
self.model.set_dropout(False)
cur_loss = self.model.val_loss()
self.model.set_dropout(True)
# Add val_loss
self.valid_metrics['loss'].append(cur_loss)
# Print validation loss
self.__print("Validation %2d - LOSS = %.3f (PPL: %.3f)" %
(self.vctr, cur_loss, np.exp(cur_loss)))
f_valid_out = None
if self.valid_save_hyp:
f_valid_out = "{0}.{1:03d}".format(self.valid_save_prefix,
self.vctr)
if is_last_best('loss', self.valid_metrics['loss']):
if self.valid_save_hyp:
# Create a link towards best hypothesis file
force_symlink(f_valid_out,
'%s.BEST' % self.valid_save_prefix,
relative=True)
self.__save_best_model()
self.early_bad = 0
else:
self.early_bad += 1
self.__print("Early stopping patience: %d validation left" %
(self.patience - self.early_bad))
self.__dump_val_summary()
if cur_loss < min_loss:
return False
def __do_validation(self):
"""Do early-stopping validation."""
if self.ectr >= self.valid_start:
self.vctr += 1
# Compute validation loss
self.model.set_dropout(False)
cur_loss = self.model.val_loss()
self.model.set_dropout(True)
# Add val_loss
self.valid_metrics['loss'].append(cur_loss)
# Print validation loss
self.__print("Validation %2d - LOSS = %.3f (PPL: %.3f)" %
(self.vctr, cur_loss, np.exp(cur_loss)))
#############################
# Are we doing beam search? #
#############################
if self.beam_metrics:
beam_results = None
# Save beam search results?
f_valid_out = None
if self.valid_save_hyp:
f_valid_out = "{0}.{1:03d}".format(self.valid_save_prefix,
self.vctr)
self.__print('Calling beam-search process')
beam_time = time.time()
beam_results = self.model.run_beam_search(
beam_size=self.beam_size,
n_jobs=self.njobs,
metric=self.beam_metrics,
f_valid_out=f_valid_out)
beam_time = time.time() - beam_time
self.__print('Beam-search ended, took %.5f minutes.' %
(beam_time / 60.))
if beam_results:
# beam_results: {name: (metric_str, metric_float)}
# names are as defined in metrics/*.py like BLEU, METEOR
# but we use lowercase names in conf files.
self.__send_stats(self.vctr, **beam_results)
for name, (metric_str,
metric_value) in beam_results.items():
self.__print("Validation %2d - %s" %
(self.vctr, metric_str))
self.valid_metrics[name.lower()].append(metric_value)
else:
self.__print(
'Skipping this validation since nmt-translate probably failed.'
)
# Return back to training loop since nmt-translate did not run correctly.
# This will allow to fix your model's build_sampler while training continues.
return
# Is this the best evaluation based on early-stop metric?
if is_last_best(self.early_metric,
self.valid_metrics[self.early_metric],
self.patience_delta):
self.__save_best_model()
self.early_bad = 0
else:
self.early_bad += 1
self.__print("Early stopping patience: %d validation left" %
(self.patience - self.early_bad))
self.__dump_val_summary()