tf.quantization.fake_quant_with_min_max_args

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

tf.quantization.fake_quant_with_min_max_args(
    inputs, min=-6, max=6, num_bits=8, narrow_range=False, name=None
)

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)

  • InvalidArgumentError:
    • If num_bits are outside of range [2, 16].
    • If min >= max.
  • ValueError: If inputs are of any other type than float32.

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.