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TensorFlow 1 version

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

    inputs, min=-6, max=6, num_bits=8, narrow_range=False, name=None

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.


  • inputs: A Tensor of type float32.
  • min: An optional float. Defaults to -6.
  • max: An optional float. Defaults to 6.
  • num_bits: An optional int. Defaults to 8.
  • narrow_range: An optional bool. Defaults to False.
  • name: A name for the operation (optional).


A Tensor of type float32.