Aliases:
tf.fake_quant_with_min_max_vars_per_channel
tf.quantization.fake_quant_with_min_max_vars_per_channel
tf.quantization.fake_quant_with_min_max_vars_per_channel(
inputs,
min,
max,
num_bits=8,
narrow_range=False,
name=None
)
Defined in generated file: tensorflow/python/ops/gen_array_ops.py
.
Fake-quantize the 'inputs' tensor of type float 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
.
[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.
This operation has a gradient and thus allows for training min
and max
values.
Args:
inputs
: ATensor
of typefloat32
.min
: ATensor
of typefloat32
.max
: ATensor
of typefloat32
.num_bits
: An optionalint
. Defaults to8
.narrow_range
: An optionalbool
. Defaults toFalse
.name
: A name for the operation (optional).
Returns:
A Tensor
of type float32
.