# tf.fake_quant_with_min_max_vars_per_channel(inputs, min, max, name=None)

### tf.fake_quant_with_min_max_vars_per_channel(inputs, min, max, name=None)

See the guide: Tensor Transformations > Fake quantization

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] is the clamping range for the 'inputs' data in the corresponding depth channel. Op divides this range into 255 steps (total of 256 values), then replaces each 'inputs' value with the closest of the quantized step values.

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.
• name: A name for the operation (optional).

#### Returns:

A Tensor of type float32.

Defined in tensorflow/python/ops/gen_array_ops.py.