Converts a TensorFlow model into TensorFlow Lite model.

Used in the notebooks

Used in the guide Used in the tutorials

Example usage:

# Converting a SavedModel to a TensorFlow Lite model.
converter = tf.lite.TFLiteConverter.from_saved_model(saved_model_dir)
tflite_model = converter.convert()

# Converting a tf.Keras model to a TensorFlow Lite model.
converter = tf.lite.TFLiteConverter.from_keras_model(model)
tflite_model = converter.convert()

# Converting ConcreteFunctions to a TensorFlow Lite model.
converter = tf.lite.TFLiteConverter.from_concrete_functions([func])
tflite_model = converter.convert()

funcs List of TensorFlow ConcreteFunctions. The list should not contain duplicate elements.
trackable_obj tf.AutoTrackable object associated with funcs. A reference to this object needs to be maintained so that Variables do not get garbage collected since functions have a weak reference to Variables. This is only required when the tf.AutoTrackable object is not maintained by the user (e.g. from_saved_model).

optimizations Experimental flag, subject to change. Set of optimizations to apply. e.g {tf.lite.Optimize.DEFAULT}. (default None, must be None or a set of values of type tf.lite.Optimize)
representative_dataset A generator function used for integer quantization where each generated sample has the same order, type and shape as the inputs to the model. Usually, this is a small subset of a few hundred samples randomly chosen, in no particular order, from the training or evaluation dataset. This is an optional attribute, but required for full integer quantization, i.e, if tf.int8 is the only supported type in target_spec.supported_types. Refer to tf.lite.RepresentativeDataset. (default None)
target_spec Experimental flag, subject to change. Specifications of target device, including supported ops set, supported types and a set of user's defined TensorFlow operators required in