tff.learning.models.save

Serializes model as a TensorFlow SavedModel to path.

tff.learning.models.save(
    model: tff.learning.models.VariableModel,
    path: str,
    input_type=None
) -> None

The resulting SavedModel will contain the default serving signature, which can be used with the TFLite converter to create a TFLite flatbuffer for inference.

Note: The model returned by tff.learning.models.load will not be the same Python type as the saved model. If the model serialized using this method is a subclass of tff.learning.models.VariableModel, that subclass is not returned. All method behavior is retained, but the Python type does not cross serialization boundaries. The return type of metric_finalizers will be an OrderedDict of str to tff.tensorflow.computation (annotated TFF computations) which could be different from that of the model before serialization.

Args
model	The tff.learning.models.VariableModel to save.
path	The str directory path to serialize the model to.
input_type	An optional structure of tf.TensorSpecs representing the expected input of model.predict_on_batch, to override reading from model.input_spec. Typically this will be similar to model.input_spec, with any example labels removed. If None, default to model.input_spec['x'] if the input_spec is a mapping, otherwise default to model.input_spec[0].