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# tensorflow::ops::Dequantize

`#include <array_ops.h>`

Dequantize the 'input' tensor into a float or bfloat16 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);
```

Args:

• 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.

Optional attributes (see `Attrs`):

• dtype: Type of the output tensor. Currently Dequantize supports float and bfloat16. If 'dtype' is 'bfloat16', it only supports 'MIN_COMBINED' mode.

Returns:

• `Output`: The output tensor.

### Constructors and Destructors

`Dequantize(const ::tensorflow::Scope & scope, ::tensorflow::Input input, ::tensorflow::Input min_range, ::tensorflow::Input max_range)`
`Dequantize(const ::tensorflow::Scope & scope, ::tensorflow::Input input, ::tensorflow::Input min_range, ::tensorflow::Input max_range, const Dequantize::Attrs & attrs)`

### Public attributes

`operation`
`Operation`
`output`
`::tensorflow::Output`

### Public functions

`node() const `
`::tensorflow::Node *`
`operator::tensorflow::Input() const `
`operator::tensorflow::Output() const `

### Public static functions

`Axis(int64 x)`
`Attrs`
`Dtype(DataType x)`
`Attrs`
`Mode(StringPiece x)`
`Attrs`
`NarrowRange(bool x)`
`Attrs`

### Structs

tensorflow::ops::Dequantize::Attrs

Optional attribute setters for Dequantize.

## Public attributes

### operation

`Operation operation`

### output

`::tensorflow::Output output`

## Public functions

### Dequantize

``` Dequantize(
const ::tensorflow::Scope & scope,
::tensorflow::Input input,
::tensorflow::Input min_range,
::tensorflow::Input max_range
)```

### Dequantize

``` Dequantize(
const ::tensorflow::Scope & scope,
::tensorflow::Input input,
::tensorflow::Input min_range,
::tensorflow::Input max_range,
const Dequantize::Attrs & attrs
)```

### node

`::tensorflow::Node * node() const `

### operator::tensorflow::Input

` operator::tensorflow::Input() const `

### operator::tensorflow::Output

` operator::tensorflow::Output() const `

## Public static functions

### Axis

```Attrs Axis(
int64 x
)```

### Dtype

```Attrs Dtype(
DataType x
)```

### Mode

```Attrs Mode(
StringPiece x
)```

### NarrowRange

```Attrs NarrowRange(
bool x
)```
[{ "type": "thumb-down", "id": "missingTheInformationINeed", "label":"Missing the information I need" },{ "type": "thumb-down", "id": "tooComplicatedTooManySteps", "label":"Too complicated / too many steps" },{ "type": "thumb-down", "id": "outOfDate", "label":"Out of date" },{ "type": "thumb-down", "id": "samplesCodeIssue", "label":"Samples / code issue" },{ "type": "thumb-down", "id": "otherDown", "label":"Other" }]
[{ "type": "thumb-up", "id": "easyToUnderstand", "label":"Easy to understand" },{ "type": "thumb-up", "id": "solvedMyProblem", "label":"Solved my problem" },{ "type": "thumb-up", "id": "otherUp", "label":"Other" }]