tf.lite.RepresentativeDataset

Converts a TensorFlow model into TensorFlow Lite model.

tf.lite.TFLiteConverter(
    funcs, trackable_obj=None
)

Used in the notebooks

Used in the guide	Used in the tutorials
Weight clustering in Keras example Pruning in Keras example Quantization aware training in Keras example Quantization aware training comprehensive guide	Post-training integer quantization Post-training dynamic range quantization Retraining an Image Classifier Super resolution with TensorFlow Lite Post-training float16 quantization

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()

Args
`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`).

Attributes
`allow_custom_ops`	Boolean indicating whether to allow custom operations. When False, any unknown operation is an error. When True, custom ops are created for any op that is unknown. The developer needs to provide these to the TensorFlow Lite runtime with a custom resolver. (default False)
`optimizations`	Experimental flag, subject to change. A list of optimizations to apply when converting the model. E.g. `[Optimize.DEFAULT]`
`representative_dataset`	A representative dataset that can be used to generate input and output samples for the model. The converter can use the dataset to evaluate different optimizations. Note that this is an optional attribute but it is necessary if INT8 is the only support builtin ops in target ops.
`target_spec`	Experimental flag, subject to change. Specification of target device.
`inference_input_type`	Data type of the input layer. Note that integer types (tf.int8 and tf.uint8) are currently only supported for post training integer quantization and quantization aware training. (default tf.float32, must be in {tf.float32, tf.int8, tf.uint8})
`inference_output_type`	Data type of the output layer. Note that integer types (tf.int8 and tf.uint8) are currently only supported for post training integer quantization and quantization aware training. (default tf.float32, must be in {tf.float32, tf.int8, tf.uint8})
`experimental_new_converter`	Experimental flag, subject to change. Enables MLIR-based conversion instead of TOCO conversion. (default True)

Methods

`convert`

View source

convert()

Converts a TensorFlow GraphDef based on instance variables.

Returns
The converted data in serialized format.

Raises
`ValueError`	No concrete functions is specified. Multiple concrete functions are specified. Input shape is not specified. Invalid quantization parameters.

`from_concrete_functions`

View source

@classmethod
from_concrete_functions(
    funcs
)

Creates a TFLiteConverter object from ConcreteFunctions.

Args
`funcs`	List of TensorFlow ConcreteFunctions. The list should not contain duplicate elements. Currently converter can only convert a single ConcreteFunction. Converting multiple functions is under development.

Returns
TFLiteConverter object.

Raises
Invalid input type.

`from_keras_model`

View source

@classmethod
from_keras_model(
    model
)

Creates a TFLiteConverter object from a Keras model.

Args
`model`	tf.Keras.Model

Returns
TFLiteConverter object.

`from_saved_model`

View source

@classmethod
from_saved_model(
    saved_model_dir, signature_keys=None, tags=None
)

Creates a TFLiteConverter object from a SavedModel directory.

Args
`saved_model_dir`	SavedModel directory to convert.
`signature_keys`	List of keys identifying SignatureDef containing inputs and outputs. Elements should not be duplicated. By default the `signatures` attribute of the MetaGraphdef is used. (default saved_model.signatures)
`tags`	Set of tags identifying the MetaGraphDef within the SavedModel to analyze. All tags in the tag set must be present. (default set(SERVING))

Returns
TFLiteConverter object.

Raises
Invalid signature keys.

TensorFlow

tf

tf.audio

tf.autograph

tf.bitwise

tf.compat

tf.config

tf.data

tf.debugging

tf.distribute

tf.dtypes

tf.errors

tf.estimator

tf.experimental

tf.feature_column

tf.graph_util

tf.image

tf.initializers

tf.io

tf.keras

tf.linalg

tf.lite

tf.lookup

tf.losses

tf.math

tf.metrics

tf.nest

tf.nn

tf.optimizers

tf.quantization

tf.queue

tf.ragged

tf.random

tf.raw_ops

tf.saved_model

tf.sets

tf.signal

tf.sparse

tf.strings

tf.summary

tf.sysconfig

tf.test

tf.tpu

tf.train

tf.version

tf.xla

tf.lite / RepresentativeDataset

Used in the notebooks

Example usage:

Args

Attributes

Methods

convert

from_concrete_functions

from_keras_model

from_saved_model

`convert`

`from_concrete_functions`

`from_keras_model`

`from_saved_model`