# tf.raw_ops.FakeQuantWithMinMaxVarsPerChannel

<br />

Fake-quantize the 'inputs' tensor of type float via per-channel floats

#### View aliases


**Compat aliases for migration**

See
[Migration guide](https://www.tensorflow.org/guide/migrate) for
more details.

[`tf.compat.v1.raw_ops.FakeQuantWithMinMaxVarsPerChannel`](https://www.tensorflow.org/api_docs/python/tf/raw_ops/FakeQuantWithMinMaxVarsPerChannel)

<br />

    tf.raw_ops.FakeQuantWithMinMaxVarsPerChannel(
        inputs, min, max, num_bits=8, narrow_range=False, name=None
    )

Fake-quantize the `inputs` tensor of type float per-channel 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`.

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`.

This operation has a gradient and thus allows for training `min` and `max`
values.

<br />

<br />

<br />

| ## 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). |

<br />

<br />

<br />

<br />

| ## Returns ||
|---|---|
| A `Tensor` of type `float32`. ||

<br />