Module keras.saving.model_config
Functions that save the model's config into different formats.
Expand source code
# Copyright 2018 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.
# ==============================================================================
# pylint: disable=protected-access
"""Functions that save the model's config into different formats."""
from keras.saving.saved_model import json_utils
from tensorflow.python.util.tf_export import keras_export
@keras_export('keras.models.model_from_config')
def model_from_config(config, custom_objects=None):
"""Instantiates a Keras model from its config.
Usage:
```
# for a Functional API model
tf.keras.Model().from_config(model.get_config())
# for a Sequential model
tf.keras.Sequential().from_config(model.get_config())
```
Args:
config: Configuration dictionary.
custom_objects: Optional dictionary mapping names
(strings) to custom classes or functions to be
considered during deserialization.
Returns:
A Keras model instance (uncompiled).
Raises:
TypeError: if `config` is not a dictionary.
"""
if isinstance(config, list):
raise TypeError('`model_from_config` expects a dictionary, not a list. '
'Maybe you meant to use '
'`Sequential.from_config(config)`?')
from keras.layers import deserialize # pylint: disable=g-import-not-at-top
return deserialize(config, custom_objects=custom_objects)
@keras_export('keras.models.model_from_yaml')
def model_from_yaml(yaml_string, custom_objects=None):
"""Parses a yaml model configuration file and returns a model instance.
Note: Since TF 2.6, this method is no longer supported and will raise a
RuntimeError.
Args:
yaml_string: YAML string or open file encoding a model configuration.
custom_objects: Optional dictionary mapping names
(strings) to custom classes or functions to be
considered during deserialization.
Returns:
A Keras model instance (uncompiled).
Raises:
RuntimeError: announces that the method poses a security risk
"""
raise RuntimeError(
'Method `model_from_yaml()` has been removed due to security risk of '
'arbitrary code execution. Please use `Model.to_json()` and '
'`model_from_json()` instead.'
)
@keras_export('keras.models.model_from_json')
def model_from_json(json_string, custom_objects=None):
"""Parses a JSON model configuration string and returns a model instance.
Usage:
>>> model = tf.keras.Sequential([
... tf.keras.layers.Dense(5, input_shape=(3,)),
... tf.keras.layers.Softmax()])
>>> config = model.to_json()
>>> loaded_model = tf.keras.models.model_from_json(config)
Args:
json_string: JSON string encoding a model configuration.
custom_objects: Optional dictionary mapping names
(strings) to custom classes or functions to be
considered during deserialization.
Returns:
A Keras model instance (uncompiled).
"""
config = json_utils.decode(json_string)
from keras.layers import deserialize # pylint: disable=g-import-not-at-top
return deserialize(config, custom_objects=custom_objects)
Functions
def model_from_config(config, custom_objects=None)
-
Instantiates a Keras model from its config.
Usage:
# for a Functional API model tf.keras.Model().from_config(model.get_config()) # for a Sequential model tf.keras.Sequential().from_config(model.get_config())
Args
config
- Configuration dictionary.
custom_objects
- Optional dictionary mapping names (strings) to custom classes or functions to be considered during deserialization.
Returns
A Keras model instance (uncompiled).
Raises
TypeError
- if
config
is not a dictionary.
Expand source code
@keras_export('keras.models.model_from_config') def model_from_config(config, custom_objects=None): """Instantiates a Keras model from its config. Usage: ``` # for a Functional API model tf.keras.Model().from_config(model.get_config()) # for a Sequential model tf.keras.Sequential().from_config(model.get_config()) ``` Args: config: Configuration dictionary. custom_objects: Optional dictionary mapping names (strings) to custom classes or functions to be considered during deserialization. Returns: A Keras model instance (uncompiled). Raises: TypeError: if `config` is not a dictionary. """ if isinstance(config, list): raise TypeError('`model_from_config` expects a dictionary, not a list. ' 'Maybe you meant to use ' '`Sequential.from_config(config)`?') from keras.layers import deserialize # pylint: disable=g-import-not-at-top return deserialize(config, custom_objects=custom_objects)
def model_from_json(json_string, custom_objects=None)
-
Parses a JSON model configuration string and returns a model instance.
Usage:
>>> model = tf.keras.Sequential([ ... tf.keras.layers.Dense(5, input_shape=(3,)), ... tf.keras.layers.Softmax()]) >>> config = model.to_json() >>> loaded_model = tf.keras.models.model_from_json(config)
Args
json_string
- JSON string encoding a model configuration.
custom_objects
- Optional dictionary mapping names (strings) to custom classes or functions to be considered during deserialization.
Returns
A Keras model instance (uncompiled).
Expand source code
@keras_export('keras.models.model_from_json') def model_from_json(json_string, custom_objects=None): """Parses a JSON model configuration string and returns a model instance. Usage: >>> model = tf.keras.Sequential([ ... tf.keras.layers.Dense(5, input_shape=(3,)), ... tf.keras.layers.Softmax()]) >>> config = model.to_json() >>> loaded_model = tf.keras.models.model_from_json(config) Args: json_string: JSON string encoding a model configuration. custom_objects: Optional dictionary mapping names (strings) to custom classes or functions to be considered during deserialization. Returns: A Keras model instance (uncompiled). """ config = json_utils.decode(json_string) from keras.layers import deserialize # pylint: disable=g-import-not-at-top return deserialize(config, custom_objects=custom_objects)
def model_from_yaml(yaml_string, custom_objects=None)
-
Parses a yaml model configuration file and returns a model instance.
Note: Since TF 2.6, this method is no longer supported and will raise a RuntimeError.
Args
yaml_string
- YAML string or open file encoding a model configuration.
custom_objects
- Optional dictionary mapping names (strings) to custom classes or functions to be considered during deserialization.
Returns
A Keras model instance (uncompiled).
Raises
RuntimeError
- announces that the method poses a security risk
Expand source code
@keras_export('keras.models.model_from_yaml') def model_from_yaml(yaml_string, custom_objects=None): """Parses a yaml model configuration file and returns a model instance. Note: Since TF 2.6, this method is no longer supported and will raise a RuntimeError. Args: yaml_string: YAML string or open file encoding a model configuration. custom_objects: Optional dictionary mapping names (strings) to custom classes or functions to be considered during deserialization. Returns: A Keras model instance (uncompiled). Raises: RuntimeError: announces that the method poses a security risk """ raise RuntimeError( 'Method `model_from_yaml()` has been removed due to security risk of ' 'arbitrary code execution. Please use `Model.to_json()` and ' '`model_from_json()` instead.' )