# tensorflow:: ops:: MatrixSetDiagV3

#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) + offset .

offset is zero except when the alignment of the diagonal is to the right.

offset = max_diag_len - diag_len(d) ; if (`align` in {RIGHT_LEFT, RIGHT_RIGHT}
and `d >= 0`) or
(`align` in {LEFT_RIGHT, RIGHT_RIGHT}
and `d <= 0`)
0                          ; otherwise
where diag_len(d) = min(cols - max(d, 0), rows + min(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(input, 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(input, 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([[[0, 9, 1],  # Diagonal shape: (2, 4, 3)
[6, 5, 8],
[1, 2, 3],
[4, 5, 0]],
[[0, 1, 2],
[5, 6, 4],
[6, 1, 2],
[3, 4, 0]]])
tf.matrix_set_diag(input, diagonals, k = (-1, 2))
==> [[[1, 6, 9, 7],  # Output shape: (2, 3, 4)
[4, 2, 5, 1],
[7, 5, 3, 8]],
[[6, 5, 1, 7],
[3, 1, 6, 2],
[7, 4, 2, 4]]]

# LEFT_RIGHT alignment.
diagonals = np.array([[[9, 1, 0],  # Diagonal shape: (2, 4, 3)
[6, 5, 8],
[1, 2, 3],
[0, 4, 5]],
[[1, 2, 0],
[5, 6, 4],
[6, 1, 2],
[0, 3, 4]]])
tf.matrix_set_diag(input, diagonals, k = (-1, 2), align="LEFT_RIGHT")
==> [[[1, 6, 9, 7],  # Output shape: (2, 3, 4)
[4, 2, 5, 1],
[7, 5, 3, 8]],
[[6, 5, 1, 7],
[3, 1, 6, 2],
[7, 4, 2, 4]]]

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] .

Optional attributes (see Attrs ):

• align: Some diagonals are shorter than max_diag_len and need to be padded. align is a string specifying how superdiagonals and subdiagonals should be aligned, respectively. There are four possible alignments: "RIGHT_LEFT" (default), "LEFT_RIGHT", "LEFT_LEFT", and "RIGHT_RIGHT". "RIGHT_LEFT" aligns superdiagonals to the right (left-pads the row) and subdiagonals to the left (right-pads the row). It is the packing format LAPACK uses. cuSPARSE uses "LEFT_RIGHT", which is the opposite alignment.

Returns:

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

### Constructors and Destructors

MatrixSetDiagV3 (const :: tensorflow::Scope & scope, :: tensorflow::Input input, :: tensorflow::Input diagonal, :: tensorflow::Input k)
MatrixSetDiagV3 (const :: tensorflow::Scope & scope, :: tensorflow::Input input, :: tensorflow::Input diagonal, :: tensorflow::Input k, const MatrixSetDiagV3::Attrs & attrs)

operation
output

### Public functions

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

### Public static functions

Align (StringPiece x)

### Structs

tensorflow:: ops:: MatrixSetDiagV3:: Attrs

Optional attribute setters for MatrixSetDiagV3 .

## Public attributes

### operation

Operation operation

### output

::tensorflow::Output output

## Public functions

### MatrixSetDiagV3

MatrixSetDiagV3(
const ::tensorflow::Scope & scope,
::tensorflow::Input input,
::tensorflow::Input diagonal,
::tensorflow::Input k
)

### MatrixSetDiagV3

MatrixSetDiagV3(
const ::tensorflow::Scope & scope,
::tensorflow::Input input,
::tensorflow::Input diagonal,
::tensorflow::Input k,
const MatrixSetDiagV3::Attrs & attrs
)

### node

::tensorflow::Node * node() const

### operator::tensorflow::Input

operator::tensorflow::Input() const

### operator::tensorflow::Output

operator::tensorflow::Output() const

## Public static functions

### Align

Attrs Align(
StringPiece 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" }]