def _prepare(self):
""" Prepares for evaluation.
Builds the model with reuse=True, mode=EVAL and preprocesses
data file(s).
Furthermore, if the decay_type of optimizer is "loss_decay", creates
the controller variables/operations.
"""
features_file = self._dataset["features_file"]
labels_file = self._dataset["labels_file"]
vocab_source = self._dataset["vocab_source"]
vocab_target = self._dataset["vocab_target"]
text_inputter = ParallelTextInputter(
LineReader(data=features_file,
preprocessing_fn=lambda x: vocab_source.convert_to_idlist(x)),
LineReader(data=labels_file,
preprocessing_fn=lambda x: vocab_target.convert_to_idlist(x)),
vocab_source.pad_id,
vocab_target.pad_id,
batch_size=self._batch_size,
batch_tokens_size=None,
shuffle_every_epoch=None,
bucketing=True)
estimator_spec = model_fn(
model_configs=self._model_configs,
mode=ModeKeys.EVAL,
vocab_source=vocab_source,
vocab_target=vocab_target,
name=self._model_name,
reuse=True,
verbose=False)
self._eval_feeding_data = text_inputter.make_feeding_data(
input_fields=estimator_spec.input_fields, in_memory=True)
self._loss_op = estimator_spec.loss
# for learning decay decay
self._half_lr = False
self._start_decay_at = 0
if self._model_configs["optimizer_params"]["optimizer.lr_decay"]["decay_type"] == "loss_decay":
self._half_lr = True
lr_tensor_dict = get_dict_from_collection(Constants.LEARNING_RATE_VAR_NAME)
self._learning_rate = lr_tensor_dict[Constants.LEARNING_RATE_VAR_NAME]
self._max_patience = self._model_configs["optimizer_params"]["optimizer.lr_decay"]["patience"]
self._start_decay_at = self._model_configs["optimizer_params"]["optimizer.lr_decay"]["start_decay_at"]
assert self._start_decay_at >= self._start_at, (
"start_decay_at in optimizer.lr_decay should be no less than start_at in LossMetricSpec.")
self._half_lr_op = lr_tensor_dict[Constants.LR_AUTO_HALF_OP_NAME]
self._bad_count = 0
self._min_loss = 10000.
def _prepare(self):
""" Prepares for evaluation.
Builds the model with reuse=True, mode=EVAL and preprocesses
data file(s).
Furthermore, if the decay_type of optimizer is "loss_decay", creates
the controller variables/operations.
"""
text_inputter = ParallelTextInputter(
dataset=self._dataset,
features_field_name="eval_features_file",
labels_field_name="eval_labels_file",
batch_size=self._batch_size,
batch_tokens_size=None,
shuffle_every_epoch=None,
bucketing=True)
estimator_spec = model_fn(model_configs=self._model_configs,
mode=ModeKeys.EVAL,
dataset=self._dataset,
name=self._model_name,
reuse=True,
verbose=False)
self._eval_feeding_data = text_inputter.make_feeding_data(
input_fields=estimator_spec.input_fields, in_memory=True)
self._loss_op = estimator_spec.loss
# for learning decay decay
self._half_lr = False
self._start_decay_at = 0
if self._model_configs["optimizer_params"]["optimizer.lr_decay"][
"decay_type"] == "loss_decay":
self._half_lr = True
lr_tensor_dict = get_dict_from_collection(
Constants.LEARNING_RATE_VAR_NAME)
self._learning_rate = lr_tensor_dict[
Constants.LEARNING_RATE_VAR_NAME]
self._max_patience = self._model_configs["optimizer_params"][
"optimizer.lr_decay"]["patience"]
self._start_decay_at = self._model_configs["optimizer_params"][
"optimizer.lr_decay"]["start_decay_at"]
assert self._start_decay_at >= self._start_at, (
"start_decay_at in optimizer.lr_decay should be no less than start_at in LossMetricSpec."
)
div_factor = lr_tensor_dict[Constants.LR_ANNEAL_DIV_FACTOR_NAME]
self._half_lr_op = div_factor.assign(div_factor * 2.)
self._bad_count = 0
self._min_loss = 10000.
def _prepare(self):
""" Prepares for evaluation.
Builds the model with reuse=True, mode=EVAL and preprocesses
data file(s).
Furthermore, if the decay_type of optimizer is "loss_decay", creates
the controller variables/operations.
"""
text_inputter = ParallelTextInputter(
dataset=self._dataset,
features_field_name="eval_features_file",
labels_field_name="eval_labels_file",
batch_size=self._batch_size,
batch_tokens_size=None,
shuffle_every_epoch=None,
bucketing=True)
estimator_spec = model_fn(
model_configs=self._model_configs,
mode=ModeKeys.EVAL,
dataset=self._dataset,
name=self._model_name,
reuse=True,
verbose=False)
self._eval_feeding_data = text_inputter.make_feeding_data(
input_fields=estimator_spec.input_fields, in_memory=True)
self._loss_op = estimator_spec.loss
# for learning decay decay
self._half_lr = False
self._start_decay_at = 0
if self._model_configs["optimizer_params"]["optimizer.lr_decay"]["decay_type"] == "loss_decay":
self._half_lr = True
lr_tensor_dict = get_dict_from_collection(Constants.LEARNING_RATE_VAR_NAME)
self._learning_rate = lr_tensor_dict[Constants.LEARNING_RATE_VAR_NAME]
self._max_patience = self._model_configs["optimizer_params"]["optimizer.lr_decay"]["patience"]
self._start_decay_at = self._model_configs["optimizer_params"]["optimizer.lr_decay"]["start_decay_at"]
assert self._start_decay_at >= self._start_at, (
"start_decay_at in optimizer.lr_decay should be no less than start_at in LossMetricSpec.")
div_factor = lr_tensor_dict[Constants.LR_ANNEAL_DIV_FACTOR_NAME]
self._half_lr_op = div_factor.assign(div_factor * 2.)
self._bad_count = 0
self._min_loss = 10000.
