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.