tf.raw_ops.UniformDequantize

Perform dequantization on the quantized Tensor input.

Given quantized input which was quantized using scales and zero_points, performs dequantization using the formula: dequantized_data = (quantized_data - zero_point) * scale.

input A Tensor. Must be one of the following types: qint8, qint32. Must be a Tensor of Tin.
scales A Tensor of type float32. The float value(s) used as scale(s) when quantizing original data that input represents. 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) used as zero_point(s) when quantizing original data that input represents. Same shape condition as scales.
Tout A tf.DType from: tf.float32. The type of output Tensor. A tf.DType from: tf.qint8, tf.qint32
quantization_min_val An int. The quantization min value that was used when input was quantized. 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 that was used when input was quantized. 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.