EVOLUTION-MANAGER

Edit File: model_config.py

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

import json

from tensorflow.python.util.tf_export import keras_export

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

@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())
  ```

Arguments:
      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 tensorflow.python.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.

Usage:

>>> model = tf.keras.Sequential([
  ...     tf.keras.layers.Dense(5, input_shape=(3,)),
  ...     tf.keras.layers.Softmax()])
  >>> try:
  ...   import yaml
  ...   config = model.to_yaml()
  ...   loaded_model = tf.keras.models.model_from_yaml(config)
  ... except ImportError:
  ...   pass

Arguments:
      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:
      ImportError: if yaml module is not found.
  """
  if yaml is None:
    raise ImportError('Requires yaml module installed (`pip install pyyaml`).')
  # The method unsafe_load only exists in PyYAML 5.x+, so which branch of the
  # try block is covered by tests depends on the installed version of PyYAML.
  try:
    # PyYAML 5.x+
    config = yaml.unsafe_load(yaml_string)
  except AttributeError:
    config = yaml.load(yaml_string)
  from tensorflow.python.keras.layers import deserialize  # pylint: disable=g-import-not-at-top
  return deserialize(config, custom_objects=custom_objects)

@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)

Arguments:
      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.loads(json_string)
  from tensorflow.python.keras.layers import deserialize  # pylint: disable=g-import-not-at-top
  return deserialize(config, custom_objects=custom_objects)