1) loading model topology from json (this will eventually come
from metagraph).
2) loading model weights from checkpoint.
Example:
importtensorflowastf# 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.