110 lines
3.6 KiB
Python
110 lines
3.6 KiB
Python
# Copyright 2018 The TensorFlow Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# ==============================================================================
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"""Functions that save the model's config into different formats."""
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# isort: off
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from tensorflow.python.util.tf_export import keras_export
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@keras_export("keras.models.model_from_config")
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def model_from_config(config, custom_objects=None):
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"""Instantiates a Keras model from its config.
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Usage:
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```
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# for a Functional API model
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tf.keras.Model().from_config(model.get_config())
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# for a Sequential model
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tf.keras.Sequential().from_config(model.get_config())
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```
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Args:
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config: Configuration dictionary.
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custom_objects: Optional dictionary mapping names
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(strings) to custom classes or functions to be
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considered during deserialization.
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Returns:
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A Keras model instance (uncompiled).
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Raises:
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TypeError: if `config` is not a dictionary.
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"""
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if isinstance(config, list):
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raise TypeError(
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"`model_from_config` expects a dictionary, not a list. "
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f"Received: config={config}. Did you meant to use "
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"`Sequential.from_config(config)`?"
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)
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from keras.layers import deserialize
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return deserialize(config, custom_objects=custom_objects)
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@keras_export("keras.models.model_from_yaml")
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def model_from_yaml(yaml_string, custom_objects=None):
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"""Parses a yaml model configuration file and returns a model instance.
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Note: Since TF 2.6, this method is no longer supported and will raise a
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RuntimeError.
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Args:
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yaml_string: YAML string or open file encoding a model configuration.
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custom_objects: Optional dictionary mapping names
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(strings) to custom classes or functions to be
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considered during deserialization.
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Returns:
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A Keras model instance (uncompiled).
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Raises:
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RuntimeError: announces that the method poses a security risk
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"""
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raise RuntimeError(
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"Method `model_from_yaml()` has been removed due to security risk of "
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"arbitrary code execution. Please use `Model.to_json()` and "
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"`model_from_json()` instead."
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)
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@keras_export("keras.models.model_from_json")
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def model_from_json(json_string, custom_objects=None):
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"""Parses a JSON model configuration string and returns a model instance.
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Usage:
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>>> model = tf.keras.Sequential([
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... tf.keras.layers.Dense(5, input_shape=(3,)),
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... tf.keras.layers.Softmax()])
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>>> config = model.to_json()
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>>> loaded_model = tf.keras.models.model_from_json(config)
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Args:
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json_string: JSON string encoding a model configuration.
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custom_objects: Optional dictionary mapping names
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(strings) to custom classes or functions to be
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considered during deserialization.
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Returns:
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A Keras model instance (uncompiled).
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"""
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from keras.layers import (
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deserialize_from_json,
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)
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return deserialize_from_json(json_string, custom_objects=custom_objects)
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