def test_with_empty_config(self):
keras_model, _, _, _, _ = get_resource_for_simple_model(
model_type='sequential', is_evaluate=True)
keras_model.compile(
loss='categorical_crossentropy',
optimizer='rmsprop',
metrics=['mse', keras.metrics.categorical_accuracy])
with self.test_session():
est_keras = keras_lib.model_to_estimator(
keras_model=keras_model,
model_dir=self._base_dir,
config=run_config_lib.RunConfig())
self.assertEqual(run_config_lib.get_default_session_config(),
est_keras._session_config)
self.assertEqual(est_keras._session_config,
est_keras._config.session_config)
self.assertEqual(self._base_dir, est_keras._config.model_dir)
self.assertEqual(self._base_dir, est_keras._model_dir)
with self.test_session():
est_keras = keras_lib.model_to_estimator(keras_model=keras_model,
model_dir=self._base_dir,
config=None)
self.assertEqual(run_config_lib.get_default_session_config(),
est_keras._session_config)
self.assertEqual(est_keras._session_config,
est_keras._config.session_config)
self.assertEqual(self._base_dir, est_keras._config.model_dir)
self.assertEqual(self._base_dir, est_keras._model_dir)
def test_with_empty_config(self):
keras_model, _, _, _, _ = get_resource_for_simple_model(
model_type='sequential', is_evaluate=True)
keras_model.compile(
loss='categorical_crossentropy',
optimizer='rmsprop',
metrics=['mse', keras.metrics.categorical_accuracy])
with self.test_session():
est_keras = keras_lib.model_to_estimator(
keras_model=keras_model, model_dir=self._base_dir,
config=run_config_lib.RunConfig())
self.assertEqual(run_config_lib.get_default_session_config(),
est_keras._session_config)
self.assertEqual(est_keras._session_config,
est_keras._config.session_config)
self.assertEqual(self._base_dir, est_keras._config.model_dir)
self.assertEqual(self._base_dir, est_keras._model_dir)
with self.test_session():
est_keras = keras_lib.model_to_estimator(
keras_model=keras_model, model_dir=self._base_dir,
config=None)
self.assertEqual(run_config_lib.get_default_session_config(),
est_keras._session_config)
self.assertEqual(est_keras._session_config,
est_keras._config.session_config)
self.assertEqual(self._base_dir, est_keras._config.model_dir)
self.assertEqual(self._base_dir, est_keras._model_dir)
def _maybe_overwrite_model_dir_and_session_config(config, model_dir):
"""Overwrite estimator config by `model_dir` and `session_config` if needed.
Args:
config: Original estimator config.
model_dir: Estimator model checkpoint directory.
Returns:
Overwritten estimator config.
Raises:
ValueError: Model directory inconsistent between `model_dir` and `config`.
"""
default_session_config = run_config_lib.get_default_session_config()
if isinstance(config, dict):
config = RunConfig(**config)
elif config is None:
config = RunConfig(session_config=default_session_config)
if config.session_config is None:
config = RunConfig.replace(config, session_config=default_session_config)
if model_dir is not None:
if (getattr(config, 'model_dir', None) is not None and
config.model_dir != model_dir):
raise ValueError(
"`model_dir` are set both in constructor and `RunConfig`, but with "
"different values. In constructor: '{}', in `RunConfig`: "
"'{}' ".format(model_dir, config.model_dir))
config = RunConfig.replace(config, model_dir=model_dir)
elif getattr(config, 'model_dir', None) is None:
model_dir = tempfile.mkdtemp()
config = RunConfig.replace(config, model_dir=model_dir)
return config