tf.compat.v1.python

TensorFlow 1 version

Public API for tf.quantization namespace.

Functions

dequantize(...): Dequantize the 'input' tensor into a float or bfloat16 Tensor.

fake_quant_with_min_max_args(...): Fake-quantize the 'inputs' tensor, type float to 'outputs' tensor of same type.

fake_quant_with_min_max_args_gradient(...): Compute gradients for a FakeQuantWithMinMaxArgs operation.

fake_quant_with_min_max_vars(...): Fake-quantize the 'inputs' tensor of type float via global float scalars

fake_quant_with_min_max_vars_gradient(...): Compute gradients for a FakeQuantWithMinMaxVars operation.

fake_quant_with_min_max_vars_per_channel(...): Fake-quantize the 'inputs' tensor of type float via per-channel floats

fake_quant_with_min_max_vars_per_channel_gradient(...): Compute gradients for a FakeQuantWithMinMaxVarsPerChannel operation.

quantize(...): Quantize the 'input' tensor of type float to 'output' tensor of type 'T'.

quantize_and_dequantize(...): Quantizes then dequantizes a tensor. (deprecated)

quantize_and_dequantize_v2(...): Quantizes then dequantizes a tensor.

quantized_concat(...): Concatenates quantized tensors along one dimension.

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

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tf.compat / v1 / v1.python_io.Overview

Functions