def _python_properties_internal(self):
"""Returns dictionary of all python properties."""
# TODO(kathywu): Add support for metrics serialization.
# TODO(kathywu): Synchronize with the keras spec (go/keras-json-spec) once
# the python config serialization has caught up.
metadata = dict(
class_name=generic_utils.get_registered_name(type(self.obj)),
name=self.obj.name,
trainable=self.obj.trainable,
expects_training_arg=self.obj._expects_training_arg, # pylint: disable=protected-access
dtype=policy.serialize(self.obj._dtype_policy), # pylint: disable=protected-access
batch_input_shape=getattr(self.obj, '_batch_input_shape', None),
stateful=self.obj.stateful,
must_restore_from_config=self.obj._must_restore_from_config, # pylint: disable=protected-access
)
metadata.update(get_config(self.obj))
if self.obj.input_spec is not None:
# Layer's input_spec has already been type-checked in the property setter.
metadata['input_spec'] = tf.nest.map_structure(
lambda x: generic_utils.serialize_keras_object(x)
if x else None, self.obj.input_spec)
if (self.obj.activity_regularizer is not None
and hasattr(self.obj.activity_regularizer, 'get_config')):
metadata[
'activity_regularizer'] = generic_utils.serialize_keras_object(
self.obj.activity_regularizer)
if self.obj._build_input_shape is not None: # pylint: disable=protected-access
metadata['build_input_shape'] = self.obj._build_input_shape # pylint: disable=protected-access
return metadata
def model_metadata(model, include_optimizer=True, require_config=True):
"""Returns a dictionary containing the model metadata."""
from keras import (
__version__ as keras_version,
) # pylint: disable=g-import-not-at-top
from keras.optimizers.optimizer_v2 import (
optimizer_v2,
) # pylint: disable=g-import-not-at-top
model_config = {"class_name": model.__class__.__name__}
try:
model_config["config"] = model.get_config()
except NotImplementedError as e:
if require_config:
raise e
metadata = dict(
keras_version=str(keras_version),
backend=backend.backend(),
model_config=model_config,
)
if model.optimizer and include_optimizer:
if isinstance(model.optimizer, optimizer_v1.TFOptimizer):
logging.warning(
"TensorFlow optimizers do not "
"make it possible to access "
"optimizer attributes or optimizer state "
"after instantiation. "
"As a result, we cannot save the optimizer "
"as part of the model save file. "
"You will have to compile your model again after loading it. "
"Prefer using a Keras optimizer instead "
"(see keras.io/optimizers)."
)
elif model._compile_was_called: # pylint: disable=protected-access
training_config = model._get_compile_args(
user_metrics=False
) # pylint: disable=protected-access
training_config.pop("optimizer", None) # Handled separately.
metadata["training_config"] = _serialize_nested_config(
training_config
)
if isinstance(model.optimizer, optimizer_v2.RestoredOptimizer):
raise NotImplementedError(
"Optimizers loaded from a SavedModel cannot be saved. "
"If you are calling `model.save` or `tf.keras.models.save_model`, "
"please set the `include_optimizer` option to `False`. For "
"`tf.saved_model.save`, delete the optimizer from the model."
)
else:
optimizer_config = {
"class_name": generic_utils.get_registered_name(
model.optimizer.__class__
),
"config": model.optimizer.get_config(),
}
metadata["training_config"]["optimizer_config"] = optimizer_config
return metadata
def _python_properties_internal(self):
metadata = dict(class_name=generic_utils.get_registered_name(
type(self.obj)),
name=self.obj.name,
dtype=self.obj.dtype)
metadata.update(layer_serialization.get_serialized(self.obj))
if self.obj._build_input_shape is not None: # pylint: disable=protected-access
metadata['build_input_shape'] = self.obj._build_input_shape # pylint: disable=protected-access
return metadata
def _python_properties_internal(self):
metadata = dict(
class_name=generic_utils.get_registered_name(type(self.obj)),
name=self.obj.name,
dtype=self.obj.dtype,
)
metadata.update(layer_serialization.get_serialized(self.obj))
if self.obj._build_input_shape is not None:
metadata["build_input_shape"] = self.obj._build_input_shape
return metadata
def model_metadata(model, include_optimizer=True, require_config=True):
"""Returns a dictionary containing the model metadata."""
from keras import __version__ as keras_version # pylint: disable=g-import-not-at-top
from keras.optimizer_v2 import optimizer_v2 # pylint: disable=g-import-not-at-top
model_config = {'class_name': model.__class__.__name__}
try:
model_config['config'] = model.get_config()
except NotImplementedError as e:
if require_config:
raise e
metadata = dict(keras_version=str(keras_version),
backend=K.backend(),
model_config=model_config)
if model.optimizer and include_optimizer:
if isinstance(model.optimizer, optimizer_v1.TFOptimizer):
logging.warning(
'TensorFlow optimizers do not '
'make it possible to access '
'optimizer attributes or optimizer state '
'after instantiation. '
'As a result, we cannot save the optimizer '
'as part of the model save file. '
'You will have to compile your model again after loading it. '
'Prefer using a Keras optimizer instead '
'(see keras.io/optimizers).')
elif model._compile_was_called: # pylint: disable=protected-access
training_config = model._get_compile_args(user_metrics=False) # pylint: disable=protected-access
training_config.pop('optimizer', None) # Handled separately.
metadata['training_config'] = _serialize_nested_config(
training_config)
if isinstance(model.optimizer, optimizer_v2.RestoredOptimizer):
raise NotImplementedError(
'As of now, Optimizers loaded from SavedModel cannot be saved. '
'If you\'re calling `model.save` or `tf.keras.models.save_model`,'
' please set the `include_optimizer` option to `False`. For '
'`tf.saved_model.save`, delete the optimizer from the model.'
)
else:
optimizer_config = {
'class_name':
generic_utils.get_registered_name(
model.optimizer.__class__),
'config':
model.optimizer.get_config()
}
metadata['training_config']['optimizer_config'] = optimizer_config
return metadata
def _get_object_registered_name(obj):
if isinstance(obj, types.FunctionType):
return generic_utils.get_registered_name(obj)
else:
return generic_utils.get_registered_name(obj.__class__)