tf.lite.Interpreter

TensorFlow 1 version

View source on GitHub

Enum defining the optimizations to apply when generating tflite graphs.

View aliases

Compat aliases for migration

See Migration guide for more details.

tf.compat.v1.lite.Optimize

Some optimizations may come at the cost of accuracy.

DEFAULT Default optimization strategy.

Converter will do its best to improve size and latency based on the
information provided.
Enhanced optimizations are gained by providing a representative_dataset.
This is recommended, and is currently equivalent to the modes below.
Currently, weights will be quantized and if representative_dataset is
provided, activations for quantizable operations will also be quantized.

OPTIMIZE_FOR_SIZE Deprecated. Does the same as DEFAULT.

OPTIMIZE_FOR_LATENCY Deprecated. Does the same as DEFAULT.

Class Variables
DEFAULT	`tf.lite.Optimize`
OPTIMIZE_FOR_LATENCY	`tf.lite.Optimize`
OPTIMIZE_FOR_SIZE	`tf.lite.Optimize`

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 / Interpreter

View aliases

Class Variables