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# tf.raw_ops.FakeQuantWithMinMaxVarsPerChannel

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

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.

`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).

A `Tensor` of type `float32`.

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