|View source on GitHub|
Quantizes then dequantizes a tensor. (deprecated)
Compat aliases for migration
See Migration guide for more details.
tf.quantization.quantize_and_dequantize( input, input_min, input_max, signed_input=True, num_bits=8, range_given=False, round_mode='HALF_TO_EVEN', name=None, narrow_range=False, axis=None )
| || A |
| ||If range_given=True, the minimum input value, that needs to be represented in the quantized representation. If axis is specified, this should be a vector of minimum values for each slice along axis.|
| ||If range_given=True, the maximum input value that needs to be represented in the quantized representation. If axis is specified, this should be a vector of maximum values for each slice along axis.|
| ||True if the quantization is signed or unsigned.|
| ||The bitwidth of the quantization.|
| || If true use |
| ||Rounding mode when rounding from float values to quantized ones. one of ['HALF_TO_EVEN', 'HALF_UP']|
| ||Optional name for the operation.|
| ||If true, then the absolute value of the quantized minimum value is the same as the quantized maximum value, instead of 1 greater. i.e. for 8 bit quantization, the minimum value is -127 instead of -128.|
| ||Integer. If specified, refers to a dimension of the input tensor, such that quantization will be per slice along that dimension.|
| A |
© 2020 The TensorFlow Authors. All rights reserved.
Licensed under the Creative Commons Attribution License 3.0.
Code samples licensed under the Apache 2.0 License.