tensorflow::ops::FakeQuantWithMinMaxArgs

Fake-quantize the 'inputs' tensor, type float to 'outputs' tensor of same type.

Summary

Attributes [min; max] define the clamping range for the inputs data. inputs values are quantized into the quantization range ([0; 2^num_bits - 1] when narrow_range is false and [1; 2^num_bits - 1] when it is true) and then de-quantized and output as floats in [min; max] interval. num_bits is the bitwidth of the quantization; between 2 and 16, inclusive.

Before quantization, min and max values are adjusted with the following logic. It is suggested to have min <= 0 <= max. If 0 is not in the range of values, the behavior can be unexpected: If 0 < min < max: min_adj = 0 and max_adj = max - min. If min < max < 0: min_adj = min - max and max_adj = 0. If min <= 0 <= max: scale = (max - min) / (2^num_bits - 1), min_adj = scale * round(min / scale) and max_adj = max + min_adj - min.

Quantization is called fake since the output is still in floating point.

Arguments:

• scope: A Scope object

Returns:

• Output: The outputs tensor.

Public attributes

Operation operation

::tensorflow::Output outputs

 FakeQuantWithMinMaxArgs(
  const ::tensorflow::Scope & scope,
  ::tensorflow::Input inputs
)

 FakeQuantWithMinMaxArgs(
  const ::tensorflow::Scope & scope,
  ::tensorflow::Input inputs,
  const FakeQuantWithMinMaxArgs::Attrs & attrs
)

::tensorflow::Node * node() const 

 operator::tensorflow::Input() const 

 operator::tensorflow::Output() const 

Attrs Max(
  float x
)

Attrs Min(
  float x
)

Attrs NarrowRange(
  bool x
)

Attrs NumBits(
  int64 x
)