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tensorflow:: ops:: MatrixDiagV2

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

Returns a batched diagonal tensor with given batched diagonal values.

Summary

Returns a tensor with the contents in ``` diagonal ``` as ``` k[0] ``` -th to ``` k[1] ``` -th diagonals of a matrix, with everything else padded with ``` padding ``` . ``` num_rows ``` and ``` num_cols ``` specify the dimension of the innermost matrix of the output. If both are not specified, the op assumes the innermost matrix is square and infers its size from ``` k ``` and the innermost dimension of ``` diagonal ``` . If only one of them is specified, the op assumes the unspecified value is the smallest possible based on other criteria.

Let ``` diagonal ``` have ``` r ``` dimensions ``` [I, J, ..., L, M, N] ``` . The output tensor has rank ``` r+1 ``` with shape ``` [I, J, ..., L, M, num_rows, num_cols] ``` when only one diagonal is given ( ``` k ``` is an integer or ``` k[0] == k[1] ``` ). Otherwise, it has rank ``` r ``` with shape ``` [I, J, ..., L, num_rows, num_cols] ``` .

The second innermost dimension of ``` diagonal ``` has double meaning. When ``` k ``` is scalar or ``` k[0] == k[1] ``` , ``` M ``` is part of the batch size [I, J, ..., M], and the output tensor is:

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

Otherwise, ``` M ``` is treated as the number of diagonals for the matrix in the same batch ( ``` M = k[1]-k[0]+1 ``` ), and the output tensor is:

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

For example:

```# The main diagonal.
diagonal = np.array([[1, 2, 3, 4],            # Input shape: (2, 4)
[5, 6, 7, 8]])
tf.matrix_diag(diagonal) ==> [[[1, 0, 0, 0],  # Output shape: (2, 4, 4)
[0, 2, 0, 0],
[0, 0, 3, 0],
[0, 0, 0, 4]],
[[5, 0, 0, 0],
[0, 6, 0, 0],
[0, 0, 7, 0],
[0, 0, 0, 8]]]```

```# A superdiagonal (per batch).
diagonal = np.array([[1, 2, 3],  # Input shape: (2, 3)
[4, 5, 6]])
tf.matrix_diag(diagonal, k = 1)
==> [[[0, 1, 0, 0],  # Output shape: (2, 4, 4)
[0, 0, 2, 0],
[0, 0, 0, 3],
[0, 0, 0, 0]],
[[0, 4, 0, 0],
[0, 0, 5, 0],
[0, 0, 0, 6],
[0, 0, 0, 0]]]```

```# A band of diagonals.
diagonals = np.array([[[1, 2, 3],  # Input shape: (2, 2, 3)
[4, 5, 0]],
[[6, 7, 9],
[9, 1, 0]]])
tf.matrix_diag(diagonals, k = (-1, 0))
==> [[[1, 0, 0],  # Output shape: (2, 3, 3)
[4, 2, 0],
[0, 5, 3]],
[[6, 0, 0],
[9, 7, 0],
[0, 1, 9]]]```

```# Rectangular matrix.
diagonal = np.array([1, 2])  # Input shape: (2)
tf.matrix_diag(diagonal, k = -1, num_rows = 3, num_cols = 4)
==> [[0, 0, 0, 0],  # Output shape: (3, 4)
[1, 0, 0, 0],
[0, 2, 0, 0]]```

```# Rectangular matrix with inferred num_cols and padding_value = 9.
tf.matrix_diag(diagonal, k = -1, num_rows = 3, padding_value = 9)
==> [[9, 9],  # Output shape: (3, 2)
[1, 9],
[9, 2]]
```

Args:

• scope: A Scope object
• diagonal: Rank ``` r ``` , where ``` r >= 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] ``` .
• num_rows: The number of rows of the output matrix. If it is not provided, the op assumes the output matrix is a square matrix and infers the matrix size from k and the innermost dimension of ``` diagonal ``` .
• num_cols: The number of columns of the output matrix. If it is not provided, the op assumes the output matrix is a square matrix and infers the matrix size from k and the innermost dimension of ``` diagonal ``` .
• padding_value: The number to fill the area outside the specified diagonal band with. Default is 0.

Returns:

• ``` Output ``` : Has rank ``` r+1 ``` when ``` k ``` is an integer or ``` k[0] == k[1] ``` , rank ``` r ``` otherwise.

Constructors and Destructors

``` MatrixDiagV2 (const :: tensorflow::Scope & scope, :: tensorflow::Input diagonal, :: tensorflow::Input k, :: tensorflow::Input num_rows, :: tensorflow::Input num_cols, :: tensorflow::Input padding_value) ```

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

MatrixDiagV2

``` MatrixDiagV2(
const ::tensorflow::Scope & scope,
::tensorflow::Input diagonal,
::tensorflow::Input k,
::tensorflow::Input num_rows,
::tensorflow::Input num_cols,
)```

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" }]