def model_to_estimator(keras_model=None,
keras_model_path=None,
custom_objects=None,
model_dir=None,
config=None):
"""Constructs an `Estimator` instance from given keras model.
For usage example, please see
@{$guide/estimators$creating_estimators_from_keras_models}.
Args:
keras_model: A compiled Keras model object. This argument is mutually
exclusive with `keras_model_path`.
keras_model_path: Path to a compiled Keras model saved on disk, in HDF5
format, which can be generated with the `save()` method of a Keras model.
This argument is mutually exclusive with `keras_model`.
custom_objects: Dictionary for custom objects.
model_dir: Directory to save `Estimator` model parameters, graph, summary
files for TensorBoard, etc.
config: `RunConfig` to config `Estimator`.
Returns:
An Estimator from given keras model.
Raises:
ValueError: if neither keras_model nor keras_model_path was given.
ValueError: if both keras_model and keras_model_path was given.
ValueError: if the keras_model_path is a GCS URI.
ValueError: if keras_model has not been compiled.
"""
if not (keras_model or keras_model_path):
raise ValueError(
'Either `keras_model` or `keras_model_path` needs to be provided.')
if keras_model and keras_model_path:
raise ValueError(
'Please specity either `keras_model` or `keras_model_path`, '
'but not both.')
if not keras_model:
if keras_model_path.startswith(
'gs://') or 'storage.googleapis.com' in keras_model_path:
raise ValueError(
'%s is not a local path. Please copy the model locally first.'
% keras_model_path)
logging.info('Loading models from %s', keras_model_path)
keras_model = models.load_model(keras_model_path)
else:
logging.info('Using the Keras model provided.')
keras_model = keras_model
if not hasattr(keras_model, 'optimizer') or not keras_model.optimizer:
raise ValueError('The given keras model has not been compiled yet. '
'Please compile the model with `model.compile()` '
'before calling `model_to_estimator()`.')
config = estimator_lib.maybe_overwrite_model_dir_and_session_config(
config, model_dir)
keras_model_fn = _create_keras_model_fn(keras_model, custom_objects)
if _any_weight_initialized(keras_model):
# Warn if config passed to estimator tries to update GPUOptions. If a
# session has already been created, the GPUOptions passed to the first
# session sticks.
if config.session_config.HasField('gpu_options'):
logging.warning(
'The Keras backend session has already been set. '
'The _session_config passed to model_to_estimator will not be used.'
)
else:
# Pass the config into keras backend's default session.
sess = session.Session(config=config.session_config)
K.set_session(sess)
warm_start_path = None
if keras_model._is_graph_network:
warm_start_path = _save_first_checkpoint(keras_model, custom_objects,
config)
elif keras_model.built:
logging.warning(
'You are creating an Estimator from a Keras model manually '
'subclassed from `Model`, that was already called on some '
'inputs (and thus already had weights). We are currently '
'unable to preserve the model\'s state (its weights) as '
'part of the estimator in this case. Be warned that the '
'estimator has been created using a freshly initialized '
'version of your model.\n'
'Note that this doesn\'t affect the state of the model '
'instance you passed as `keras_model` argument.')
estimator = estimator_lib.Estimator(keras_model_fn,
config=config,
warm_start_from=warm_start_path)
return estimator
def model_to_estimator(keras_model=None,
keras_model_path=None,
custom_objects=None,
model_dir=None,
config=None):
"""Constructs an `Estimator` instance from given keras model.
For usage example, please see:
[Creating estimators from Keras
Models](https://tensorflow.org/guide/estimators#model_to_estimator).
Args:
keras_model: A compiled Keras model object. This argument is mutually
exclusive with `keras_model_path`.
keras_model_path: Path to a compiled Keras model saved on disk, in HDF5
format, which can be generated with the `save()` method of a Keras model.
This argument is mutually exclusive with `keras_model`.
custom_objects: Dictionary for custom objects.
model_dir: Directory to save `Estimator` model parameters, graph, summary
files for TensorBoard, etc.
config: `RunConfig` to config `Estimator`.
Returns:
An Estimator from given keras model.
Raises:
ValueError: if neither keras_model nor keras_model_path was given.
ValueError: if both keras_model and keras_model_path was given.
ValueError: if the keras_model_path is a GCS URI.
ValueError: if keras_model has not been compiled.
"""
if not (keras_model or keras_model_path):
raise ValueError(
'Either `keras_model` or `keras_model_path` needs to be provided.')
if keras_model and keras_model_path:
raise ValueError(
'Please specity either `keras_model` or `keras_model_path`, '
'but not both.')
if not keras_model:
if keras_model_path.startswith(
'gs://') or 'storage.googleapis.com' in keras_model_path:
raise ValueError(
'%s is not a local path. Please copy the model locally first.' %
keras_model_path)
logging.info('Loading models from %s', keras_model_path)
keras_model = models.load_model(keras_model_path)
else:
logging.info('Using the Keras model provided.')
keras_model = keras_model
if not hasattr(keras_model, 'optimizer') or not keras_model.optimizer:
raise ValueError(
'The given keras model has not been compiled yet. '
'Please compile the model with `model.compile()` '
'before calling `model_to_estimator()`.')
config = estimator_lib.maybe_overwrite_model_dir_and_session_config(config,
model_dir)
keras_model_fn = _create_keras_model_fn(keras_model, custom_objects)
if _any_weight_initialized(keras_model):
# Warn if config passed to estimator tries to update GPUOptions. If a
# session has already been created, the GPUOptions passed to the first
# session sticks.
if config.session_config.HasField('gpu_options'):
logging.warning(
'The Keras backend session has already been set. '
'The _session_config passed to model_to_estimator will not be used.')
else:
# Pass the config into keras backend's default session.
sess = session.Session(config=config.session_config)
K.set_session(sess)
warm_start_path = None
if keras_model._is_graph_network:
warm_start_path = _save_first_checkpoint(keras_model, custom_objects,
config)
elif keras_model.built:
logging.warning('You are creating an Estimator from a Keras model manually '
'subclassed from `Model`, that was already called on some '
'inputs (and thus already had weights). We are currently '
'unable to preserve the model\'s state (its weights) as '
'part of the estimator in this case. Be warned that the '
'estimator has been created using a freshly initialized '
'version of your model.\n'
'Note that this doesn\'t affect the state of the model '
'instance you passed as `keras_model` argument.')
estimator = estimator_lib.Estimator(keras_model_fn,
config=config,
warm_start_from=warm_start_path)
return estimator
