tf.quantization.dequantize     View source on GitHub    Dequantize the 'input' tensor into a float or bfloat16 Tensor.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.dequantize, tf.compat.v1.quantization.dequantize  
tf.quantization.dequantize(
    input, min_range, max_range, mode='MIN_COMBINED', name=None,
    axis=None, narrow_range=False, dtype=tf.dtypes.float32
)
 [min_range, max_range] are scalar floats that specify the range for the output. The 'mode' attribute controls exactly which calculations are used to convert the float values to their quantized equivalents. In 'MIN_COMBINED' mode, each value of the tensor will undergo the following: if T == qint8: in[i] += (range(T) + 1)/ 2.0
out[i] = min_range + (in[i]* (max_range - min_range) / range(T))
 here range(T) = numeric_limits<T>::max() - numeric_limits<T>::min() MIN_COMBINED Mode Example If the input comes from a QuantizedRelu6, the output type is quint8 (range of 0-255) but the possible range of QuantizedRelu6 is 0-6. The min_range and max_range values are therefore 0.0 and 6.0. Dequantize on quint8 will take each value, cast to float, and multiply by 6 / 255. Note that if quantizedtype is qint8, the operation will additionally add each value by 128 prior to casting. If the mode is 'MIN_FIRST', then this approach is used: num_discrete_values = 1 << (# of bits in T)
range_adjust = num_discrete_values / (num_discrete_values - 1)
range = (range_max - range_min) * range_adjust
range_scale = range / num_discrete_values
const double offset_input = static_cast<double>(input) - lowest_quantized;
result = range_min + ((input - numeric_limits<T>::min()) * range_scale)
 If the mode is SCALED, dequantization is performed by multiplying each input value by a scaling_factor. (Thus an input of 0 always maps to 0.0). The scaling_factor is determined from min_range, max_range, and narrow_range in a way that is compatible with QuantizeAndDequantize{V2|V3} and QuantizeV2, using the following algorithm: 
const int min_expected_T = std::numeric_limits<T>::min() +
  (narrow_range ? 1 : 0);
const int max_expected_T = std::numeric_limits<T>::max();
const float max_expected_T = std::numeric_limits<float>::max();

const float scale_factor =
  (std::numeric_limits<T>::min() == 0) ? (max_range / max_expected_T)
                                       : std::max(min_range / min_expected_T,
                                                  max_range / max_expected_T);

 


 Args
  input   A Tensor. Must be one of the following types: qint8, quint8, qint32, qint16, quint16.  
  min_range   A Tensor of type float32. The minimum scalar value possibly produced for the input.  
  max_range   A Tensor of type float32. The maximum scalar value possibly produced for the input.  
  mode   An optional string from: "MIN_COMBINED", "MIN_FIRST", "SCALED". Defaults to "MIN_COMBINED".  
  narrow_range   An optional bool. Defaults to False.  
  axis   An optional int. Defaults to -1.  
  dtype   An optional tf.DType from: tf.bfloat16, tf.float32. Defaults to tf.float32. Type of the output tensor. Currently Dequantize supports float and bfloat16. If 'dtype' is 'bfloat16', it only supports 'MIN_COMBINED' mode.  
  name   A name for the operation (optional).   
 


 Returns   A Tensor of type dtype.