# tensorflow:: ops:: MatrixSetDiagV2

``` #include <array_ops.h> ```

Returns a batched matrix tensor with new batched diagonal values.

## Summary

Given ``` input ``` and ``` diagonal ``` , this operation returns a tensor with the same shape and values as ``` input ``` , except for the specified diagonals of the innermost matrices. These will be overwritten by the values in ``` diagonal ``` .

``` input ``` has ``` r+1 ``` dimensions ``` [I, J, ..., L, M, N] ``` . When ``` k ``` is scalar or ``` k[0] == k[1] ``` , ``` diagonal ``` has ``` r ``` dimensions ``` [I, J, ..., L, max_diag_len] ``` . Otherwise, it has ``` r+1 ``` dimensions ``` [I, J, ..., L, num_diags, max_diag_len] ``` . ``` num_diags ``` is the number of diagonals, ``` num_diags = k[1] - k[0] + 1 ``` . ``` max_diag_len ``` is the longest diagonal in the range ``` [k[0], k[1]] ``` , ``` max_diag_len = min(M + min(k[1], 0), N + min(-k[0], 0)) ```

The output is a tensor of rank ``` k+1 ``` with dimensions ``` [I, J, ..., L, M, N] ``` . If ``` k ``` is scalar or ``` k[0] == k[1] ``` :

```output[i, j, ..., l, m, n]
= diagonal[i, j, ..., l, n-max(k[1], 0)] ; if n - m == k[1]
input[i, j, ..., l, m, n]              ; otherwise
```

Otherwise,

```output[i, j, ..., l, m, n]
= diagonal[i, j, ..., l, diag_index, index_in_diag] ; if k[0] <= d <= k[1]
input[i, j, ..., l, m, n]                         ; otherwise
```
where ``` d = n - m ``` , ``` diag_index = k[1] - d ``` , and ``` index_in_diag = n - max(d, 0) ``` .

For example:

```# The main diagonal.
input = np.array([[[7, 7, 7, 7],              # Input shape: (2, 3, 4)
[7, 7, 7, 7],
[7, 7, 7, 7]],
[[7, 7, 7, 7],
[7, 7, 7, 7],
[7, 7, 7, 7]]])
diagonal = np.array([[1, 2, 3],               # Diagonal shape: (2, 3)
[4, 5, 6]])
tf.matrix_set_diag(diagonal) ==> [[[1, 7, 7, 7],  # Output shape: (2, 3, 4)
[7, 2, 7, 7],
[7, 7, 3, 7]],
[[4, 7, 7, 7],
[7, 5, 7, 7],
[7, 7, 6, 7]]]```

```# A superdiagonal (per batch).
tf.matrix_set_diag(diagonal, k = 1)
==> [[[7, 1, 7, 7],  # Output shape: (2, 3, 4)
[7, 7, 2, 7],
[7, 7, 7, 3]],
[[7, 4, 7, 7],
[7, 7, 5, 7],
[7, 7, 7, 6]]]```

```# A band of diagonals.
diagonals = np.array([[[1, 2, 3],  # Diagonal shape: (2, 2, 3)
[4, 5, 0]],
[[6, 1, 2],
[3, 4, 0]]])
tf.matrix_set_diag(diagonals, k = (-1, 0))
==> [[[1, 7, 7, 7],  # Output shape: (2, 3, 4)
[4, 2, 7, 7],
[0, 5, 3, 7]],
[[6, 7, 7, 7],
[3, 1, 7, 7],
[7, 4, 2, 7]]]```

Args:

• scope: A Scope object
• input: Rank ``` r+1 ``` , where ``` r >= 1 ``` .
• diagonal: Rank ``` r ``` when ``` k ``` is an integer or ``` k[0] == k[1] ``` . Otherwise, it has rank ``` r+1 ``` . ``` k >= 1 ``` .
• k: Diagonal offset(s). Positive value means superdiagonal, 0 refers to the main diagonal, and negative value means subdiagonals. ``` k ``` can be a single integer (for a single diagonal) or a pair of integers specifying the low and high ends of a matrix band. ``` k[0] ``` must not be larger than ``` k[1] ``` .

Returns:

• ``` Output ``` : Rank ``` r+1 ``` , with ``` output.shape = input.shape ``` .

### Constructors and Destructors

``` MatrixSetDiagV2 (const :: tensorflow::Scope & scope, :: tensorflow::Input input, :: tensorflow::Input diagonal, :: tensorflow::Input k) ```

### Public attributes

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

### Public functions

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

## Public attributes

### operation

`Operation operation`

### output

`::tensorflow::Output output`

## Public functions

### MatrixSetDiagV2

``` MatrixSetDiagV2(
const ::tensorflow::Scope & scope,
::tensorflow::Input input,
::tensorflow::Input diagonal,
::tensorflow::Input k
)```

### node

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

### operator::tensorflow::Input

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

### operator::tensorflow::Output

` operator::tensorflow::Output() const `
[{ "type": "thumb-down", "id": "missingTheInformationINeed", "label":"Falta la información que necesito" },{ "type": "thumb-down", "id": "tooComplicatedTooManySteps", "label":"Muy complicado o demasiados pasos" },{ "type": "thumb-down", "id": "outOfDate", "label":"Desactualizado" },{ "type": "thumb-down", "id": "samplesCodeIssue", "label":"Samples / code issue" },{ "type": "thumb-down", "id": "otherDown", "label":"Otro" }]
[{ "type": "thumb-up", "id": "easyToUnderstand", "label":"Fácil de comprender" },{ "type": "thumb-up", "id": "solvedMyProblem", "label":"Resolvió mi problema" },{ "type": "thumb-up", "id": "otherUp", "label":"Otro" }]