# tensorflow::ops::MatrixDiagV2 `#include ` 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 output[i, j, ..., l, m, n] ; otherwise ```
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, k[1]-d, n-max(d, 0)] ; if d_lower <= d <= d_upper input[i, j, ..., l, m, n] ; otherwise ``` where `d = n - m`
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 = 9. tf.matrix_diag(diagonal, k = -1, num_rows = 3, padding = 9) ==> [[9, 9], # Output shape: (3, 2) [1, 9], [9, 2]] ```
Arguments: - scope: A [Scope](https://www.tensorflow.org/versions/r1.15/api_docs/cc/class/tensorflow/scope#classtensorflow_1_1_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: - `https://www.tensorflow.org/versions/r1.15/api_docs/cc/class/tensorflow/output#classtensorflow_1_1_output`: Has rank `r+1` when `k` is an integer or `k[0] == k[1]`, rank `r` otherwise.
| ### Constructors and Destructors || |---|---| | `https://www.tensorflow.org/versions/r1.15/api_docs/cc/class/tensorflow/ops/matrix-diag-v2#classtensorflow_1_1ops_1_1_matrix_diag_v2_1a0f4757e436efeaed1ba2bd9690367b7f(const ::https://www.tensorflow.org/versions/r1.15/api_docs/cc/class/tensorflow/scope#classtensorflow_1_1_scope & scope, ::https://www.tensorflow.org/versions/r1.15/api_docs/cc/class/tensorflow/input#classtensorflow_1_1_input diagonal, ::https://www.tensorflow.org/versions/r1.15/api_docs/cc/class/tensorflow/input#classtensorflow_1_1_input k, ::https://www.tensorflow.org/versions/r1.15/api_docs/cc/class/tensorflow/input#classtensorflow_1_1_input num_rows, ::https://www.tensorflow.org/versions/r1.15/api_docs/cc/class/tensorflow/input#classtensorflow_1_1_input num_cols, ::https://www.tensorflow.org/versions/r1.15/api_docs/cc/class/tensorflow/input#classtensorflow_1_1_input padding_value)` || | ### Public attributes || |---|---| | `https://www.tensorflow.org/versions/r1.15/api_docs/cc/class/tensorflow/ops/matrix-diag-v2#classtensorflow_1_1ops_1_1_matrix_diag_v2_1a09bb44acf7362d90e9b70dfaa3ebd8fd` | `https://www.tensorflow.org/versions/r1.15/api_docs/cc/class/tensorflow/operation#classtensorflow_1_1_operation` | | `https://www.tensorflow.org/versions/r1.15/api_docs/cc/class/tensorflow/ops/matrix-diag-v2#classtensorflow_1_1ops_1_1_matrix_diag_v2_1a180f0e2069ba614f9a8bf4593d6b322a` | `::https://www.tensorflow.org/versions/r1.15/api_docs/cc/class/tensorflow/output#classtensorflow_1_1_output` | | ### Public functions || |---|---| | `https://www.tensorflow.org/versions/r1.15/api_docs/cc/class/tensorflow/ops/matrix-diag-v2#classtensorflow_1_1ops_1_1_matrix_diag_v2_1a5faf264fc6c643dfc49d1d5b00828973() const ` | `::tensorflow::Node *` | | `https://www.tensorflow.org/versions/r1.15/api_docs/cc/class/tensorflow/ops/matrix-diag-v2#classtensorflow_1_1ops_1_1_matrix_diag_v2_1a8301ec1ffa503e41034e06b9fc8dfd93() const ` | ` `` ` | | `https://www.tensorflow.org/versions/r1.15/api_docs/cc/class/tensorflow/ops/matrix-diag-v2#classtensorflow_1_1ops_1_1_matrix_diag_v2_1a96f20bc6c061cadeeb30e3986311fc2c() 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, ::tensorflow::Input padding_value ) ``` ### node ``` ::tensorflow::Node * node() const ``` ### operator::tensorflow::Input ``` operator::tensorflow::Input() const ``` ### operator::tensorflow::Output ``` operator::tensorflow::Output() const ```