tensorflow/python/keras/saving/save.py - platform/external/tensorflow - Git at Google

 # Copyright 2019 The TensorFlow Authors. All Rights Reserved.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.
 # You may obtain a copy of the License at
 #
 #     http://www.apache.org/licenses/LICENSE-2.0
 #
 # Unless required by applicable law or agreed to in writing, software
 # distributed under the License is distributed on an "AS IS" BASIS,
 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 # See the License for the specific language governing permissions and
 # limitations under the License.
 # ==============================================================================
 """Keras model saving code."""

 from __future__ import absolute_import
 from __future__ import division
 from __future__ import print_function

 import six

 from tensorflow.python import tf2
 from tensorflow.python.keras.saving import hdf5_format
 from tensorflow.python.keras.saving import saving_utils
 from tensorflow.python.keras.saving.saved_model import load as saved_model_load
 from tensorflow.python.keras.saving.saved_model import load_context
 from tensorflow.python.keras.saving.saved_model import save as saved_model_save
 from tensorflow.python.keras.utils import generic_utils
 from tensorflow.python.keras.utils.io_utils import path_to_string
 from tensorflow.python.saved_model import loader_impl
 from tensorflow.python.util import keras_deps
 from tensorflow.python.util.tf_export import keras_export

 # pylint: disable=g-import-not-at-top
 try:
   import h5py
 except ImportError:
   h5py = None
 # pylint: enable=g-import-not-at-top


 @keras_export('keras.models.save_model')
 def save_model(model,
                filepath,
                overwrite=True,
                include_optimizer=True,
                save_format=None,
                signatures=None,
                options=None,
                save_traces=True):
   # pylint: disable=line-too-long
   """Saves a model as a TensorFlow SavedModel or HDF5 file.

   See the [Serialization and Saving guide](https://keras.io/guides/serialization_and_saving/)
   for details.

   Usage:

   >>> model = tf.keras.Sequential([
   ...     tf.keras.layers.Dense(5, input_shape=(3,)),
   ...     tf.keras.layers.Softmax()])
   >>> model.save('/tmp/model')
   >>> loaded_model = tf.keras.models.load_model('/tmp/model')
   >>> x = tf.random.uniform((10, 3))
   >>> assert np.allclose(model.predict(x), loaded_model.predict(x))

   The SavedModel and HDF5 file contains:

   - the model's configuration (topology)
   - the model's weights
   - the model's optimizer's state (if any)

   Thus models can be reinstantiated in the exact same state, without any of the
   code used for model definition or training.

   Note that the model weights may have different scoped names after being
   loaded. Scoped names include the model/layer names, such as
   `"dense_1/kernel:0"`. It is recommended that you use the layer properties to
   access specific variables, e.g. `model.get_layer("dense_1").kernel`.

   __SavedModel serialization format__

   Keras SavedModel uses `tf.saved_model.save` to save the model and all
   trackable objects attached to the model (e.g. layers and variables). The model
   config, weights, and optimizer are saved in the SavedModel. Additionally, for
   every Keras layer attached to the model, the SavedModel stores:

     * the config and metadata -- e.g. name, dtype, trainable status
     * traced call and loss functions, which are stored as TensorFlow subgraphs.

   The traced functions allow the SavedModel format to save and load custom
   layers without the original class definition.

   You can choose to not save the traced functions by disabling the `save_traces`
   option. This will decrease the time it takes to save the model and the
   amount of disk space occupied by the output SavedModel. If you enable this
   option, then you _must_ provide all custom class definitions when loading
   the model. See the `custom_objects` argument in `tf.keras.models.load_model`.

   Args:
       model: Keras model instance to be saved.
       filepath: One of the following:
         - String or `pathlib.Path` object, path where to save the model
         - `h5py.File` object where to save the model
       overwrite: Whether we should overwrite any existing model at the target
         location, or instead ask the user with a manual prompt.
       include_optimizer: If True, save optimizer's state together.
       save_format: Either 'tf' or 'h5', indicating whether to save the model
         to Tensorflow SavedModel or HDF5. Defaults to 'tf' in TF 2.X, and 'h5'
         in TF 1.X.
       signatures: Signatures to save with the SavedModel. Applicable to the 'tf'
         format only. Please see the `signatures` argument in
         `tf.saved_model.save` for details.
       options: (only applies to SavedModel format) `tf.saved_model.SaveOptions`
         object that specifies options for saving to SavedModel.
       save_traces: (only applies to SavedModel format) When enabled, the
         SavedModel will store the function traces for each layer. This
         can be disabled, so that only the configs of each layer are stored.
         Defaults to `True`. Disabling this will decrease serialization time and
         reduce file size, but it requires that all custom layers/models
         implement a `get_config()` method.

   Raises:
       ImportError: If save format is hdf5, and h5py is not available.
   """
   # pylint: enable=line-too-long
   from tensorflow.python.keras.engine import sequential  # pylint: disable=g-import-not-at-top

   default_format = 'tf' if tf2.enabled() else 'h5'
   save_format = save_format or default_format

   filepath = path_to_string(filepath)

   if (save_format == 'h5' or
       (h5py is not None and isinstance(filepath, h5py.File)) or
       saving_utils.is_hdf5_filepath(filepath)):
     # TODO(b/130258301): add utility method for detecting model type.
     if (not model._is_graph_network and  # pylint:disable=protected-access
         not isinstance(model, sequential.Sequential)):
       raise NotImplementedError(
           'Saving the model to HDF5 format requires the model to be a '
           'Functional model or a Sequential model. It does not work for '
           'subclassed models, because such models are defined via the body of '
           'a Python method, which isn\'t safely serializable. Consider saving '
           'to the Tensorflow SavedModel format (by setting save_format="tf") '
           'or using `save_weights`.')
     hdf5_format.save_model_to_hdf5(
         model, filepath, overwrite, include_optimizer)
   else:
     with generic_utils.SharedObjectSavingScope():
       saved_model_save.save(model, filepath, overwrite, include_optimizer,
                             signatures, options, save_traces)


 @keras_export('keras.models.load_model')
 def load_model(filepath, custom_objects=None, compile=True, options=None):  # pylint: disable=redefined-builtin
   """Loads a model saved via `model.save()`.

   Usage:

   >>> model = tf.keras.Sequential([
   ...     tf.keras.layers.Dense(5, input_shape=(3,)),
   ...     tf.keras.layers.Softmax()])
   >>> model.save('/tmp/model')
   >>> loaded_model = tf.keras.models.load_model('/tmp/model')
   >>> x = tf.random.uniform((10, 3))
   >>> assert np.allclose(model.predict(x), loaded_model.predict(x))

   Note that the model weights may have different scoped names after being
   loaded. Scoped names include the model/layer names, such as
   `"dense_1/kernel:0"`. It is recommended that you use the layer properties to
   access specific variables, e.g. `model.get_layer("dense_1").kernel`.

   Args:
       filepath: One of the following:
           - String or `pathlib.Path` object, path to the saved model
           - `h5py.File` object from which to load the model
       custom_objects: Optional dictionary mapping names
           (strings) to custom classes or functions to be
           considered during deserialization.
       compile: Boolean, whether to compile the model
           after loading.
       options: Optional `tf.saved_model.LoadOptions` object that specifies
         options for loading from SavedModel.

   Returns:
       A Keras model instance. If the original model was compiled, and saved with
       the optimizer, then the returned model will be compiled. Otherwise, the
       model will be left uncompiled. In the case that an uncompiled model is
       returned, a warning is displayed if the `compile` argument is set to
       `True`.

   Raises:
       ImportError: if loading from an hdf5 file and h5py is not available.
       IOError: In case of an invalid savefile.
   """
   with generic_utils.SharedObjectLoadingScope():
     with generic_utils.CustomObjectScope(custom_objects or {}):
       with load_context.load_context(options):
         if (h5py is not None and
             (isinstance(filepath, h5py.File) or h5py.is_hdf5(filepath))):
           return hdf5_format.load_model_from_hdf5(filepath, custom_objects,
                                                   compile)

         filepath = path_to_string(filepath)
         if isinstance(filepath, six.string_types):
           loader_impl.parse_saved_model(filepath)
           return saved_model_load.load(filepath, compile, options)

   raise IOError(
       'Unable to load model. Filepath is not an hdf5 file (or h5py is not '
       'available) or SavedModel.')

 # Inject the load_model function to keras_deps to remove the dependency
 # from TFLite to Keras.
 keras_deps.register_load_model_function(load_model)
	# Copyright 2019 The TensorFlow Authors. All Rights Reserved.
	#
	# Licensed under the Apache License, Version 2.0 (the "License");
	# you may not use this file except in compliance with the License.
	# You may obtain a copy of the License at
	#
	# http://www.apache.org/licenses/LICENSE-2.0
	#
	# Unless required by applicable law or agreed to in writing, software
	# distributed under the License is distributed on an "AS IS" BASIS,
	# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
	# See the License for the specific language governing permissions and
	# limitations under the License.
	# ==============================================================================
	"""Keras model saving code."""

	from __future__ import absolute_import
	from __future__ import division
	from __future__ import print_function

	import six

	from tensorflow.python import tf2
	from tensorflow.python.keras.saving import hdf5_format
	from tensorflow.python.keras.saving import saving_utils
	from tensorflow.python.keras.saving.saved_model import load as saved_model_load
	from tensorflow.python.keras.saving.saved_model import load_context
	from tensorflow.python.keras.saving.saved_model import save as saved_model_save
	from tensorflow.python.keras.utils import generic_utils
	from tensorflow.python.keras.utils.io_utils import path_to_string
	from tensorflow.python.saved_model import loader_impl
	from tensorflow.python.util import keras_deps
	from tensorflow.python.util.tf_export import keras_export

	# pylint: disable=g-import-not-at-top
	try:
	import h5py
	except ImportError:
	h5py = None
	# pylint: enable=g-import-not-at-top


	@keras_export('keras.models.save_model')
	def save_model(model,
	filepath,
	overwrite=True,
	include_optimizer=True,
	save_format=None,
	signatures=None,
	options=None,
	save_traces=True):
	# pylint: disable=line-too-long
	"""Saves a model as a TensorFlow SavedModel or HDF5 file.

	See the [Serialization and Saving guide](https://keras.io/guides/serialization_and_saving/)
	for details.

	Usage:

	>>> model = tf.keras.Sequential([
	... tf.keras.layers.Dense(5, input_shape=(3,)),
	... tf.keras.layers.Softmax()])
	>>> model.save('/tmp/model')
	>>> loaded_model = tf.keras.models.load_model('/tmp/model')
	>>> x = tf.random.uniform((10, 3))
	>>> assert np.allclose(model.predict(x), loaded_model.predict(x))

	The SavedModel and HDF5 file contains:

	- the model's configuration (topology)
	- the model's weights
	- the model's optimizer's state (if any)

	Thus models can be reinstantiated in the exact same state, without any of the
	code used for model definition or training.

	Note that the model weights may have different scoped names after being
	loaded. Scoped names include the model/layer names, such as
	`"dense_1/kernel:0"`. It is recommended that you use the layer properties to
	access specific variables, e.g. `model.get_layer("dense_1").kernel`.

	__SavedModel serialization format__

	Keras SavedModel uses `tf.saved_model.save` to save the model and all
	trackable objects attached to the model (e.g. layers and variables). The model
	config, weights, and optimizer are saved in the SavedModel. Additionally, for
	every Keras layer attached to the model, the SavedModel stores:

	* the config and metadata -- e.g. name, dtype, trainable status
	* traced call and loss functions, which are stored as TensorFlow subgraphs.

	The traced functions allow the SavedModel format to save and load custom
	layers without the original class definition.

	You can choose to not save the traced functions by disabling the `save_traces`
	option. This will decrease the time it takes to save the model and the
	amount of disk space occupied by the output SavedModel. If you enable this
	option, then you _must_ provide all custom class definitions when loading
	the model. See the `custom_objects` argument in `tf.keras.models.load_model`.

	Args:
	model: Keras model instance to be saved.
	filepath: One of the following:
	- String or `pathlib.Path` object, path where to save the model
	- `h5py.File` object where to save the model
	overwrite: Whether we should overwrite any existing model at the target
	location, or instead ask the user with a manual prompt.
	include_optimizer: If True, save optimizer's state together.
	save_format: Either 'tf' or 'h5', indicating whether to save the model
	to Tensorflow SavedModel or HDF5. Defaults to 'tf' in TF 2.X, and 'h5'
	in TF 1.X.
	signatures: Signatures to save with the SavedModel. Applicable to the 'tf'
	format only. Please see the `signatures` argument in
	`tf.saved_model.save` for details.
	options: (only applies to SavedModel format) `tf.saved_model.SaveOptions`
	object that specifies options for saving to SavedModel.
	save_traces: (only applies to SavedModel format) When enabled, the
	SavedModel will store the function traces for each layer. This
	can be disabled, so that only the configs of each layer are stored.
	Defaults to `True`. Disabling this will decrease serialization time and
	reduce file size, but it requires that all custom layers/models
	implement a `get_config()` method.

	Raises:
	ImportError: If save format is hdf5, and h5py is not available.
	"""
	# pylint: enable=line-too-long
	from tensorflow.python.keras.engine import sequential # pylint: disable=g-import-not-at-top

	default_format = 'tf' if tf2.enabled() else 'h5'
	save_format = save_format or default_format

	filepath = path_to_string(filepath)

	if (save_format == 'h5' or
	(h5py is not None and isinstance(filepath, h5py.File)) or
	saving_utils.is_hdf5_filepath(filepath)):
	# TODO(b/130258301): add utility method for detecting model type.
	if (not model._is_graph_network and # pylint:disable=protected-access
	not isinstance(model, sequential.Sequential)):
	raise NotImplementedError(
	'Saving the model to HDF5 format requires the model to be a '
	'Functional model or a Sequential model. It does not work for '
	'subclassed models, because such models are defined via the body of '
	'a Python method, which isn\'t safely serializable. Consider saving '
	'to the Tensorflow SavedModel format (by setting save_format="tf") '
	'or using `save_weights`.')
	hdf5_format.save_model_to_hdf5(
	model, filepath, overwrite, include_optimizer)
	else:
	with generic_utils.SharedObjectSavingScope():
	saved_model_save.save(model, filepath, overwrite, include_optimizer,
	signatures, options, save_traces)


	@keras_export('keras.models.load_model')
	def load_model(filepath, custom_objects=None, compile=True, options=None): # pylint: disable=redefined-builtin
	"""Loads a model saved via `model.save()`.

	Usage:

	>>> model = tf.keras.Sequential([
	... tf.keras.layers.Dense(5, input_shape=(3,)),
	... tf.keras.layers.Softmax()])
	>>> model.save('/tmp/model')
	>>> loaded_model = tf.keras.models.load_model('/tmp/model')
	>>> x = tf.random.uniform((10, 3))
	>>> assert np.allclose(model.predict(x), loaded_model.predict(x))

	Note that the model weights may have different scoped names after being
	loaded. Scoped names include the model/layer names, such as
	`"dense_1/kernel:0"`. It is recommended that you use the layer properties to
	access specific variables, e.g. `model.get_layer("dense_1").kernel`.

	Args:
	filepath: One of the following:
	- String or `pathlib.Path` object, path to the saved model
	- `h5py.File` object from which to load the model
	custom_objects: Optional dictionary mapping names
	(strings) to custom classes or functions to be
	considered during deserialization.
	compile: Boolean, whether to compile the model
	after loading.
	options: Optional `tf.saved_model.LoadOptions` object that specifies
	options for loading from SavedModel.

	Returns:
	A Keras model instance. If the original model was compiled, and saved with
	the optimizer, then the returned model will be compiled. Otherwise, the
	model will be left uncompiled. In the case that an uncompiled model is
	returned, a warning is displayed if the `compile` argument is set to
	`True`.

	Raises:
	ImportError: if loading from an hdf5 file and h5py is not available.
	IOError: In case of an invalid savefile.
	"""
	with generic_utils.SharedObjectLoadingScope():
	with generic_utils.CustomObjectScope(custom_objects or {}):
	with load_context.load_context(options):
	if (h5py is not None and
	(isinstance(filepath, h5py.File) or h5py.is_hdf5(filepath))):
	return hdf5_format.load_model_from_hdf5(filepath, custom_objects,
	compile)

	filepath = path_to_string(filepath)
	if isinstance(filepath, six.string_types):
	loader_impl.parse_saved_model(filepath)
	return saved_model_load.load(filepath, compile, options)

	raise IOError(
	'Unable to load model. Filepath is not an hdf5 file (or h5py is not '
	'available) or SavedModel.')

	# Inject the load_model function to keras_deps to remove the dependency
	# from TFLite to Keras.
	keras_deps.register_load_model_function(load_model)