# tf.where

Return the elements where `condition` is `True` (multiplexing `x` and `y`).

This operator has two modes: in one mode both `x` and `y` are provided, in another mode neither are provided. `condition` is always expected to be a `tf.Tensor` of type `bool`.

#### Retrieving indices of `True` elements

If `x` and `y` are not provided (both are None):

`tf.where` will return the indices of `condition` that are `True`, in the form of a 2-D tensor with shape (n, d). (Where n is the number of matching indices in `condition`, and d is the number of dimensions in `condition`).

Indices are output in row-major order.

````tf.where([True, False, False, True])`
`<tf.Tensor: shape=(2, 1), dtype=int64, numpy=`
`array([[0],`
`       [3]])>`
```
````tf.where([[True, False], [False, True]])`
`<tf.Tensor: shape=(2, 2), dtype=int64, numpy=`
`array([[0, 0],`
`       [1, 1]])>`
```
````tf.where([[[True, False], [False, True], [True, True]]])`
`<tf.Tensor: shape=(4, 3), dtype=int64, numpy=`
`array([[0, 0, 0],`
`       [0, 1, 1],`
`       [0, 2, 0],`
`       [0, 2, 1]])>`
```

#### Multiplexing between `x` and `y`

If `x` and `y` are provided (both have non-None values):

`tf.where` will choose an output shape from the shapes of `condition`, `x`, and `y` that all three shapes are broadcastable to.

The `condition` tensor acts as a mask that chooses whether the corresponding element / row in the output should be taken from `x` (if the elemment in `condition is True) or`y` (if it is false).

````tf.where([True, False, False, True], [1,2,3,4], [100,200,300,400])`
`<tf.Tensor: shape=(4,), dtype=int32, numpy=array([  1, 200, 300,   4],`
`dtype=int32)>`
`tf.where([True, False, False, True], [1,2,3,4], [100])`
`<tf.Tensor: shape=(4,), dtype=int32, numpy=array([  1, 100, 100,   4],`
`dtype=int32)>`
`tf.where([True, False, False, True], [1,2,3,4], 100)`
`<tf.Tensor: shape=(4,), dtype=int32, numpy=array([  1, 100, 100,   4],`
`dtype=int32)>`
`tf.where([True, False, False, True], 1, 100)`
`<tf.Tensor: shape=(4,), dtype=int32, numpy=array([  1, 100, 100,   1],`
`dtype=int32)>`
```
````tf.where(True, [1,2,3,4], 100)`
`<tf.Tensor: shape=(4,), dtype=int32, numpy=array([1, 2, 3, 4],`
`dtype=int32)>`
`tf.where(False, [1,2,3,4], 100)`
`<tf.Tensor: shape=(4,), dtype=int32, numpy=array([100, 100, 100, 100],`
`dtype=int32)>`
```

`condition` A `tf.Tensor` of type `bool`
`x` If provided, a Tensor which is of the same type as `y`, and has a shape broadcastable with `condition` and `y`.
`y` If provided, a Tensor which is of the same type as `y`, and has a shape broadcastable with `condition` and `x`.
`name` A name of the operation (optional).

If `x` and `y` are provided: A `Tensor` with the same type as `x` and `y`, and shape that is broadcast from `condition`, `x`, and `y`. Otherwise, a `Tensor` with shape `(num_true, dim_size(condition))`.

`ValueError` When exactly one of `x` or `y` is non-None, or the shapes are not all broadcastable.

[{ "type": "thumb-down", "id": "missingTheInformationINeed", "label":"Brak potrzebnych mi informacji" },{ "type": "thumb-down", "id": "tooComplicatedTooManySteps", "label":"Zbyt skomplikowane / zbyt wiele czynności do wykonania" },{ "type": "thumb-down", "id": "outOfDate", "label":"Nieaktualne treści" },{ "type": "thumb-down", "id": "samplesCodeIssue", "label":"Samples / code issue" },{ "type": "thumb-down", "id": "otherDown", "label":"Inne" }]
[{ "type": "thumb-up", "id": "easyToUnderstand", "label":"Łatwo zrozumieć" },{ "type": "thumb-up", "id": "solvedMyProblem", "label":"Rozwiązało to mój problem" },{ "type": "thumb-up", "id": "otherUp", "label":"Inne" }]