|TensorFlow 1 version||View source on GitHub|
Saves a model as a TensorFlow SavedModel or HDF5 file.
Compat aliases for migration
See Migration guide for more details.
tf.keras.models.save_model( model, filepath, overwrite=True, include_optimizer=True, save_format=None, signatures=None, options=None )
model = tf.keras.Sequential([
loaded_model = tf.keras.models.load_model('/tmp/model')
x = tf.random.uniform((10, 3))
assert np.allclose(model.predict(x), loaded_model.predict(x))
The saved model contains:
- the model's configuration (topology) - the model's weights - the model's optimizer's state (if any)
Thus the saved model can be reinstantiated in the exact same state, without any of the code used for model definition or training.
Note that the model weights may have different scoped names after being
loaded. Scoped names include the model/layer names, such as
. It is recommended that you use the layer properties to
access specific variables, e.g.model.get_layer("dense_1").kernel`.
The SavedModel serialization path uses
tf.saved_model.save to save the model
and all trackable objects attached to the model (e.g. layers and variables).
@tf.function-decorated methods are also saved. Additional trackable objects
and functions are added to the SavedModel to allow the model to be
loaded back as a Keras Model object.
||Keras model instance to be saved.|
One of the following:
||Whether we should overwrite any existing model at the target location, or instead ask the user with a manual prompt.|
||If True, save optimizer's state together.|
||Either 'tf' or 'h5', indicating whether to save the model to Tensorflow SavedModel or HDF5. Defaults to 'tf' in TF 2.X, and 'h5' in TF 1.X.|
Signatures to save with the SavedModel. Applicable to the 'tf'
format only. Please see the
||If save format is hdf5, and h5py is not available.|