tf.quantization.fake_quant_with_min_max_vars_per_channel

Fake-quantize the 'inputs' tensor of type float via per-channel floats

tf.quantization.fake_quant_with_min_max_vars_per_channel(
    inputs: Annotated[Any, _atypes.Float32],
    min: Annotated[Any, _atypes.Float32],
    max: Annotated[Any, _atypes.Float32],
    num_bits: int = 8,
    narrow_range: bool = False,
    name=None
) -> Annotated[Any, _atypes.Float32]

Fake-quantize the inputs tensor of type float per-channel and one of the shapes: [d], [b, d] [b, h, w, d] via per-channel floats min and max of shape [d] to outputs tensor of same shape as inputs.

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.

This operation has a gradient and thus allows for training min and max values.