Quantizes then dequantizes a tensor.
tf.raw_ops.QuantizeAndDequantizeV2( input, input_min, input_max, signed_input=True, num_bits=8, range_given=False, round_mode='HALF_TO_EVEN', narrow_range=False, axis=1, name=None )
This op simulates the precision loss from the quantized forward pass by:
There are different ways to quantize. This version uses only scaling, so 0.0 maps to 0.
From the specified 'num_bits' in the quantized output type, it determines minimum and maximum representable quantized values.
e.g.
If range_given == False, the initial input_min, input_max will be determined automatically as the minimum and maximum values in the input tensor, otherwise the specified values of input_min, input_max are used.
Note: If the input_min, input_max are specified, they do not need to equal the actual minimum and maximum values in the tensor. e.g. in some cases it may be beneficial to specify these values such that the low probability extremes of the input distribution are clipped.
This op determines the maximum scale_factor that would map the initial [input_min, input_max] range to a range that lies within the representable quantized range.
It determines the scale from one of input_min and input_max, then updates the other one to maximize the representable range.
e.g.
After determining the scale_factor and updating the input range, it applies the following to each value in the 'input' tensor.
output = round(clamp(value, input_min, input_max) * scale_factor) / scale_factor.
The above round function rounds the value based on the given round_mode.
Args  

input  A Tensor . Must be one of the following types: bfloat16 , half , float32 , float64 . Tensor to quantize and then dequantize. 
input_min  A Tensor . Must have the same type as input . If range_given == True , this specifies the minimum input value that needs to be represented, otherwise it is determined from the min value of the input tensor. 
input_max  A Tensor . Must have the same type as input . If range_given == True , this specifies the maximum input value that needs to be represented, otherwise it is determined from the max value of the input tensor. 
signed_input  An optional bool . Defaults to True . Whether the quantization is signed or unsigned. (actually this parameter should have been called signed_output ) 
num_bits  An optional int . Defaults to 8 . The bitwidth of the quantization. 
range_given  An optional bool . Defaults to False . Whether the range is given or should be determined from the input tensor. 
round_mode  An optional string from: "HALF_TO_EVEN", "HALF_UP" . Defaults to "HALF_TO_EVEN" . The 'round_mode' attribute controls which rounding tiebreaking algorithm is used when rounding float values to their quantized equivalents. The following rounding modes are currently supported:

narrow_range  An optional bool . Defaults to False . 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. 
axis  An optional int . Defaults to 1 . If specified, this axis is treated as a channel or slice axis, and a separate quantization range is used for each channel or slice along this axis. 
name  A name for the operation (optional). 
Returns  

A Tensor . Has the same type as input . 
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Licensed under the Creative Commons Attribution License 3.0.
Code samples licensed under the Apache 2.0 License.
https://www.tensorflow.org/versions/r2.4/api_docs/python/tf/raw_ops/QuantizeAndDequantizeV2