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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]]

Arguments:

• 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)

operation
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":"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" }]