tf.quantization.fake_quant_with_min_max_args  |  TensorFlow v2.16.1 (original) (raw)

tf.quantization.fake_quant_with_min_max_args

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

View aliases

Compat aliases for migration

SeeMigration guide for more details.

tf.compat.v1.fake_quant_with_min_max_args, tf.compat.v1.quantization.fake_quant_with_min_max_args

tf.quantization.fake_quant_with_min_max_args(
    inputs: Annotated[Any, _atypes.Float32],
    min: float = -6,
    max: float = 6,
    num_bits: int = 8,
    narrow_range: bool = False,
    name=None
) -> Annotated[Any, _atypes.Float32]

Quantization is called fake since the output is still in floating point. The API converts inputs into values within the range [min and max] and returns as output.

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.

Examples

inp = tf.constant ([10.03, -10.23, 3])
out = tf.quantization.fake_quant_with_min_max_args(inp, min=-5, max=5,
                                                   num_bits=16)
print(out)

#  Output:
#  tf.Tensor([ 4.9999237 -5.0000763  3.0000763], shape=(3,), dtype=float32)