def __prepare_model(self, train_mode=False):
"""Prepare utilities for decoding."""
hparams = registry.hparams(self.params.hparams_set)
hparams.problem = self.problem
hparams.problem_hparams = self.problem.get_hparams(hparams)
if self.params.hparams:
tf.logging.info("Overriding hparams in %s with %s",
self.params.hparams_set,
self.params.hparams)
hparams = hparams.parse(self.params.hparams)
trainer_run_config = g2p_trainer_utils.create_run_config(hparams,
self.params)
if train_mode:
exp_fn = g2p_trainer_utils.create_experiment_fn(self.params, self.problem)
self.exp = exp_fn(trainer_run_config, hparams)
decode_hp = decoding.decode_hparams(self.params.decode_hparams)
estimator = trainer_lib.create_estimator(
self.params.model_name,
hparams,
trainer_run_config,
decode_hparams=decode_hp,
use_tpu=False)
return estimator, decode_hp, hparams
def __prepare_model(self, train_mode=False):
"""Prepare utilities for decoding."""
hparams = registry.hparams(self.params.hparams_set)
hparams.problem = self.problem
hparams.problem_hparams = self.problem.get_hparams(hparams)
if self.params.hparams:
tf.logging.info("Overriding hparams in %s with %s",
self.params.hparams_set,
self.params.hparams)
hparams = hparams.parse(self.params.hparams)
trainer_run_config = g2p_trainer_utils.create_run_config(hparams,
self.params)
if train_mode:
exp_fn = g2p_trainer_utils.create_experiment_fn(self.params, self.problem)
self.exp = exp_fn(trainer_run_config, hparams)
decode_hp = decoding.decode_hparams(self.params.decode_hparams)
estimator = trainer_lib.create_estimator(
self.params.model_name,
hparams,
trainer_run_config,
decode_hparams=decode_hp,
use_tpu=False)
return estimator, decode_hp, hparams
def __prepare_model(self):
"""Prepare utilities for decoding."""
hparams = trainer_lib.create_hparams(
hparams_set=self.params.hparams_set,
hparams_overrides_str=self.params.hparams)
trainer_run_config = g2p_trainer_utils.create_run_config(hparams,
self.params)
exp_fn = g2p_trainer_utils.create_experiment_fn(self.params, self.problem)
self.exp = exp_fn(trainer_run_config, hparams)
decode_hp = decoding.decode_hparams(self.params.decode_hparams)
decode_hp.add_hparam("shards", self.params.decode_shards)
decode_hp.add_hparam("shard_id", self.params.worker_id)
estimator = trainer_lib.create_estimator(
self.params.model_name,
hparams,
trainer_run_config,
decode_hparams=decode_hp,
use_tpu=False)
return estimator, decode_hp, hparams