Fake-quantize the 'inputs' tensor of type float and one of the shapes: [d],
tf.raw_ops.FakeQuantWithMinMaxVarsPerChannel(
inputs, min, max, num_bits=8, narrow_range=False, name=None
)
[b, d] [b, h, w, d] via per-channel floats min and max of shape [d]
to 'outputs' tensor of same shape as inputs.
[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.
Args | |
|---|---|
inputs
|
A Tensor of type float32.
|
min
|
A Tensor of type float32.
|
max
|
A Tensor of type float32.
|
num_bits
|
An optional int. Defaults to 8.
|
narrow_range
|
An optional bool. Defaults to False.
|
name
|
A name for the operation (optional). |
Returns | |
|---|---|
A Tensor of type float32.
|