|TensorFlow 2 version||View source on GitHub|
Saves a model as a TensorFlow SavedModel or HDF5 file.
tf.keras.models.save_model( model, filepath, overwrite=True, include_optimizer=True, save_format=None, signatures=None )
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.
SavedModel serialization (not yet added)
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.
model: Keras model instance to be saved.
filepath: One of the following:
- String, path where to save the model
h5py.Fileobject where to save the model
overwrite: Whether we should overwrite any existing model at the target location, or instead ask the user with a manual prompt.
include_optimizer: If True, save optimizer's state together.
save_format: 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: Signatures to save with the SavedModel. Applicable to the 'tf' format only. Please see the
ImportError: If save format is hdf5, and h5py is not available.