tf.compat.v1.keras.experimental.export_saved_model

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Exports a tf.keras.Model as a Tensorflow SavedModel.

tf.compat.v1.keras.experimental.export_saved_model(
    model, saved_model_path, custom_objects=None, as_text=False,
    input_signature=None, serving_only=False
)

Note that at this time, subclassed models can only be saved using serving_only=True.

The exported SavedModel is a standalone serialization of Tensorflow objects, and is supported by TF language APIs and the Tensorflow Serving system. To load the model, use the function tf.keras.experimental.load_from_saved_model.

The SavedModel contains:

1. a checkpoint containing the model weights.
2. a SavedModel proto containing the Tensorflow backend graph. Separate graphs are saved for prediction (serving), train, and evaluation. If the model has not been compiled, then only the graph computing predictions will be exported.
3. the model's json config. If the model is subclassed, this will only be included if the model's get_config() method is overwritten.

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()