tf.fake_quant_with_min_max_vars(inputs, min, max, name=None)

tf.fake_quant_with_min_max_vars(inputs, min, max, name=None)

See the guide: Tensor Transformations > Fake quantization

Fake-quantize the 'inputs' tensor of type float and shape [b, h, w, d] via

global float scalars min and max to 'outputs' tensor of same shape as inputs.

[min; max] is the clamping range for the 'inputs' data. 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.