TensorFlow 2 version |
Fake-quantize the 'inputs' tensor of type float via global float scalars min
tf.quantization.fake_quant_with_min_max_vars(
inputs, min, max, num_bits=8, narrow_range=False, name=None
)
and max
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 .
|