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tensorflow::ops::Dequantize
#include <array_ops.h>
Dequantize the 'input' tensor into a float Tensor.
Summary
[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::max() - numeric_limits::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(input) - lowest_quantized;
result = range_min + ((input - numeric_limits::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::min() +
(narrow_range ? 1 : 0);
const int max_expected_T = std::numeric_limits::max();
const float max_expected_T = std::numeric_limits::max();
const float scale_factor =
(std::numeric_limits::min() == 0) ? (max_range / max_expected_T)
: std::max(min_range / min_expected_T,
max_range / max_expected_T);
Arguments:
- scope: A Scope object
- min_range: The minimum scalar value possibly produced for the input.
- max_range: The maximum scalar value possibly produced for the input.
Returns:
Public attributes
Public functions
node
::tensorflow::Node * node() const
operator::tensorflow::Input() const
operator::tensorflow::Output
operator::tensorflow::Output() const
Public static functions
Axis
Attrs Axis(
int64 x
)
Mode
Attrs Mode(
StringPiece x
)
NarrowRange
Attrs NarrowRange(
bool x
)
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Last updated 2020-04-20 UTC.
[null,null,["Last updated 2020-04-20 UTC."],[],[],null,["# tensorflow::ops::Dequantize Class Reference\n\ntensorflow::ops::Dequantize\n===========================\n\n`#include \u003carray_ops.h\u003e`\n\n[Dequantize](/versions/r2.1/api_docs/cc/class/tensorflow/ops/dequantize#classtensorflow_1_1ops_1_1_dequantize) the 'input' tensor into a float [Tensor](/versions/r2.1/api_docs/cc/class/tensorflow/tensor#classtensorflow_1_1_tensor).\n\nSummary\n-------\n\n\\[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.\n\nIn 'MIN_COMBINED' mode, each value of the tensor will undergo the following:\n\n\u003cbr /\u003e\n\n```transact-sql\nif T == qint8: in[i] += (range(T) + 1)/ 2.0\nout[i] = min_range + (in[i]* (max_range - min_range) / range(T))\n```\nhere `range(T) = numeric_limits`::max() - numeric_limits::min()\n\n\u003cbr /\u003e\n\n\n*MIN_COMBINED Mode Example*\n\nIf the input comes from a [QuantizedRelu6](/versions/r2.1/api_docs/cc/class/tensorflow/ops/quantized-relu6#classtensorflow_1_1ops_1_1_quantized_relu6), the output type is quint8 (range of 0-255) but the possible range of [QuantizedRelu6](/versions/r2.1/api_docs/cc/class/tensorflow/ops/quantized-relu6#classtensorflow_1_1ops_1_1_quantized_relu6) is 0-6. The min_range and max_range values are therefore 0.0 and 6.0. [Dequantize](/versions/r2.1/api_docs/cc/class/tensorflow/ops/dequantize#classtensorflow_1_1ops_1_1_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.\n\nIf the mode is 'MIN_FIRST', then this approach is used:\n\n\n```gdscript\nnum_discrete_values = 1 \u003c\u003c (# of bits in T)\nrange_adjust = num_discrete_values / (num_discrete_values - 1)\nrange = (range_max - range_min) * range_adjust\nrange_scale = range / num_discrete_values\nconst double offset_input = static_cast(input) - lowest_quantized;\nresult = range_min + ((input - numeric_limits::min()) * range_scale)\n```\n\n\u003cbr /\u003e\n\nIf 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).\n\nThe scaling_factor is determined from `min_range`, `max_range`, and `narrow_range` in a way that is compatible with `QuantizeAndDequantize{V2|V3}` and [QuantizeV2](/versions/r2.1/api_docs/cc/class/tensorflow/ops/quantize-v2#classtensorflow_1_1ops_1_1_quantize_v2), using the following algorithm:\n\n\n````gdscript\n \n \n const int min_expected_T = std::numeric_limits::min() +\n (narrow_range ? 1 : 0);\n const int max_expected_T = std::numeric_limits::max();\n const float max_expected_T = std::numeric_limits::max();\n \n \n \n```gdscript\n const float scale_factor =\n (std::numeric_limits::min() == 0) ? (max_range / max_expected_T)\n : std::max(min_range / min_expected_T,\n max_range / max_expected_T);\n```\n\n \n Arguments:\n \n- scope: A /versions/r2.1/api_docs/cc/class/tensorflow/scope#classtensorflow_1_1_scope object\n\n \n- min_range: The minimum scalar value possibly produced for the input.\n\n \n- max_range: The maximum scalar value possibly produced for the input.\n\n \n\n Returns:\n \n- /versions/r2.1/api_docs/cc/class/tensorflow/output#classtensorflow_1_1_output: The output tensor. \n\n \n \n \n \n \n### Constructors and Destructors\n\n\n \n \n \n \n #classtensorflow_1_1ops_1_1_dequantize_1ace6411557abc00c6e59649720be7d579`(const ::`/versions/r2.1/api_docs/cc/class/tensorflow/scope#classtensorflow_1_1_scope` & scope, ::`/versions/r2.1/api_docs/cc/class/tensorflow/input#classtensorflow_1_1_input` input, ::`/versions/r2.1/api_docs/cc/class/tensorflow/input#classtensorflow_1_1_input` min_range, ::`/versions/r2.1/api_docs/cc/class/tensorflow/input#classtensorflow_1_1_input` max_range)`\n \n\n \n \n \n \n #classtensorflow_1_1ops_1_1_dequantize_1afb71f46f9e4fc4922578ecd9116ad9b1`(const ::`/versions/r2.1/api_docs/cc/class/tensorflow/scope#classtensorflow_1_1_scope` & scope, ::`/versions/r2.1/api_docs/cc/class/tensorflow/input#classtensorflow_1_1_input` input, ::`/versions/r2.1/api_docs/cc/class/tensorflow/input#classtensorflow_1_1_input` min_range, ::`/versions/r2.1/api_docs/cc/class/tensorflow/input#classtensorflow_1_1_input` max_range, const `/versions/r2.1/api_docs/cc/struct/tensorflow/ops/dequantize/attrs#structtensorflow_1_1ops_1_1_dequantize_1_1_attrs` & attrs)`\n \n\n \n \n \n \n \n \n \n### Public attributes\n\n\n \n \n \n \n #classtensorflow_1_1ops_1_1_dequantize_1a917ce29fbec6ef49406db9a374bde9aa\n \n \n \n /versions/r2.1/api_docs/cc/class/tensorflow/operation#classtensorflow_1_1_operation\n \n \n \n \n \n #classtensorflow_1_1ops_1_1_dequantize_1a5c4618ae3d058bcd8547217612f8f41e\n \n \n \n `::`/versions/r2.1/api_docs/cc/class/tensorflow/output#classtensorflow_1_1_output\n \n \n \n \n \n \n \n \n### Public functions\n\n\n \n \n \n \n #classtensorflow_1_1ops_1_1_dequantize_1a4bdeb613e4b88880638a67528cbd01f0`() const `\n \n \n \n `::tensorflow::Node *`\n \n \n \n \n \n #classtensorflow_1_1ops_1_1_dequantize_1ab1b62ee39a382d6e124eb62156c05525`() const `\n \n \n \n `\n `\n`\n `\n \n \n \n #classtensorflow_1_1ops_1_1_dequantize_1ae01ee2df9b62f7729848ca15ed70e8fc`() const `\n \n \n \n `\n `\n`\n `\n \n \n \n \n \n \n### Public static functions\n\n\n \n \n \n \n #classtensorflow_1_1ops_1_1_dequantize_1ac0b7d9ea267e2c8719f63ff4434b5250`(int64 x)`\n \n \n \n /versions/r2.1/api_docs/cc/struct/tensorflow/ops/dequantize/attrs#structtensorflow_1_1ops_1_1_dequantize_1_1_attrs\n \n \n \n \n \n #classtensorflow_1_1ops_1_1_dequantize_1ac9873b34c5c0eb36296e0fe726644fc9`(StringPiece x)`\n \n \n \n /versions/r2.1/api_docs/cc/struct/tensorflow/ops/dequantize/attrs#structtensorflow_1_1ops_1_1_dequantize_1_1_attrs\n \n \n \n \n \n #classtensorflow_1_1ops_1_1_dequantize_1a4409107547aae6b42715813687850b35`(bool x)`\n \n \n \n /versions/r2.1/api_docs/cc/struct/tensorflow/ops/dequantize/attrs#structtensorflow_1_1ops_1_1_dequantize_1_1_attrs\n \n \n \n \n \n \n \n \n### Structs\n\n\n \n \n \n \n /versions/r2.1/api_docs/cc/struct/tensorflow/ops/dequantize/attrs\n \n \n Optional attribute setters for /versions/r2.1/api_docs/cc/class/tensorflow/ops/dequantize#classtensorflow_1_1ops_1_1_dequantize. \n\n \n \n \n Public attributes\n \n \n### operation\n\n\n \n\n\n```text\nOperation operation\n```\n\n \n\n \n \n \n### output\n\n\n \n\n\n```text\n::tensorflow::Output output\n```\n\n \n\n \n Public functions\n \n \n### Dequantize\n\n\n \n\n\n```gdscript\n Dequantize(\n const ::tensorflow::Scope & scope,\n ::tensorflow::Input input,\n ::tensorflow::Input min_range,\n ::tensorflow::Input max_range\n)\n```\n\n \n\n \n \n \n### Dequantize\n\n\n \n\n\n```gdscript\n Dequantize(\n const ::tensorflow::Scope & scope,\n ::tensorflow::Input input,\n ::tensorflow::Input min_range,\n ::tensorflow::Input max_range,\n const Dequantize::Attrs & attrs\n)\n```\n\n \n\n \n \n \n### node\n\n\n \n\n\n```gdscript\n::tensorflow::Node * node() const \n```\n\n \n\n \n \n \n### operator::tensorflow::Input\n\n\n \n\n\n```gdscript\n operator::tensorflow::Input() const \n```\n\n \n\n \n \n \n### operator::tensorflow::Output\n\n\n \n\n\n```gdscript\n operator::tensorflow::Output() const \n```\n\n \n\n \n Public static functions\n \n \n### Axis\n\n\n \n\n\n```text\nAttrs Axis(\n int64 x\n)\n```\n\n \n\n \n \n \n### Mode\n\n\n \n\n\n```text\nAttrs Mode(\n StringPiece x\n)\n```\n\n \n\n \n \n \n### NarrowRange\n\n\n \n\n\n```text\nAttrs NarrowRange(\n bool x\n)\n```\n\n \n\n \n\n \n\n \n````"]]