tf.raw_ops.UniformQuantize

Perform quantization on Tensor input.

tf.raw_ops.UniformQuantize(
    input,
    scales,
    zero_points,
    Tout,
    quantization_min_val,
    quantization_max_val,
    quantization_axis=-1,
    name=None
)

Given input, scales and zero_points, performs quantization using the formula: quantized_data = floor(input_data * (1.0f / scale) + 0.5f) + zero_point

input A Tensor. Must be one of the following types: float32. Must be a Tensor of Tin.
scales A Tensor of type float32. The float value(s) to use as scale(s) to quantize input. Must be a scalar Tensor if quantization_axis is -1 (per-tensor quantization), otherwise 1D Tensor of size (input.dim_size(quantization_axis),) (per-axis quantization).
zero_points A Tensor of type int32. The int32 value(s) to use as zero_point(s) to quantize input. Same shape condition as scales.
Tout A tf.DType from: tf.qint8, tf.qint32. The type of output Tensor. A tf.DType from: tf.float32
quantization_min_val An int. The quantization min value to quantize input. The purpose of this attribute is typically (but not limited to) to indicate narrow range, where this is set to: (Tin lowest) + 1 if narrow range, and (Tin lowest) otherwise. For example, if Tin is qint8, this is set to -127 if narrow range quantized or -128 if not.
quantization_max_val An int. The quantization max value to quantize input. The purpose of this attribute is typically (but not limited to) indicate narrow range, where this is set to: (Tout max) for both narrow range and not narrow range. For example, if Tin is qint8, this is set to 127.
quantization_axis An optional int. Defaults to -1. Indicates the dimension index of the tensor where per-axis quantization is applied for the slices along that dimension. If set to -1 (default), this indicates per-tensor quantization. Otherwise, it must be set within range [0, input.dims()).
name A name for the operation (optional).

A Tensor of type Tout.