def test_nested_shared_object_saving_scopes(self):
my_obj = MaybeSharedObject()
with generic_utils.SharedObjectSavingScope() as scope_1:
scope_1.create_config({}, my_obj)
with generic_utils.SharedObjectSavingScope() as scope_2:
# Nesting saving scopes should return the original scope and should
# not clear any objects we're tracking.
self.assertIs(scope_1, scope_2)
self.assertIsNotNone(scope_2.get_config(my_obj))
self.assertIsNotNone(scope_1.get_config(my_obj))
self.assertIsNone(generic_utils._shared_object_saving_scope())
def test_shared_object_saving_scope_single_object_doesnt_export_id(self):
with generic_utils.SharedObjectSavingScope() as scope:
single_object = MaybeSharedObject()
self.assertIsNone(scope.get_config(single_object))
single_object_config = scope.create_config({}, single_object)
self.assertIsNotNone(single_object_config)
self.assertNotIn(generic_utils.SHARED_OBJECT_KEY,
single_object_config)
def test_shared_object_saving_scope_shared_object_exports_id(self):
with generic_utils.SharedObjectSavingScope() as scope:
shared_object = MaybeSharedObject()
self.assertIsNone(scope.get_config(shared_object))
scope.create_config({}, shared_object)
first_object_config = scope.get_config(shared_object)
second_object_config = scope.get_config(shared_object)
self.assertIn(generic_utils.SHARED_OBJECT_KEY, first_object_config)
self.assertIn(generic_utils.SHARED_OBJECT_KEY,
second_object_config)
self.assertIs(first_object_config, second_object_config)
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 the user has not already called fit or built the underlying metrics, we
# should do that before saving to ensure the metric names have all
# appropriate name transformations applied.
saving_utils.try_build_compiled_arguments(model)
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)
