tf.compat.v1.keras.experimental.load_from_saved_model

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Loads a keras Model from a SavedModel created by export_saved_model(). (deprecated)

tf.compat.v1.keras.experimental.load_from_saved_model(
    saved_model_path, custom_objects=None
)

Warning: THIS FUNCTION IS DEPRECATED. It will be removed in a future version. Instructions for updating: The experimental save and load functions have been deprecated. Please switch to tf.keras.models.load_model.

This function reinstantiates model state by: 1) loading model topology from json (this will eventually come from metagraph). 2) loading model weights from checkpoint.

Example:

import tensorflow as tf

# Create a tf.keras model.
model = tf.keras.Sequential()
model.add(tf.keras.layers.Dense(1, input_shape=[10]))
model.summary()

# Save the tf.keras model in the SavedModel format.
path = '/tmp/simple_keras_model'
tf.keras.experimental.export_saved_model(model, path)

# Load the saved keras model back.
new_model = tf.keras.experimental.load_from_saved_model(path)
new_model.summary()

Args:

• saved_model_path: a string specifying the path to an existing SavedModel.
• custom_objects: Optional dictionary mapping names (strings) to custom classes or functions to be considered during deserialization.

Returns:

a keras.Model instance.