T
- data type for output()
output@Operator public final class MatrixDiagV2<T> extends PrimitiveOp implements Operand<T>
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]]
operation
Modifier and Type | Method and Description |
---|---|
Output<T> |
asOutput()
Returns the symbolic handle of a tensor.
|
static <T> MatrixDiagV2<T> |
create(Scope scope,
Operand<T> diagonal,
Operand<Integer> k,
Operand<Integer> numRows,
Operand<Integer> numCols,
Operand<T> paddingValue)
Factory method to create a class wrapping a new MatrixDiagV2 operation.
|
Output<T> |
output()
Has rank `r+1` when `k` is an integer or `k[0] == k[1]`, rank `r` otherwise.
|
equals, hashCode, op, toString
public static <T> MatrixDiagV2<T> create(Scope scope, Operand<T> diagonal, Operand<Integer> k, Operand<Integer> numRows, Operand<Integer> numCols, Operand<T> paddingValue)
scope
- current scopediagonal
- 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]`.numRows
- 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`.numCols
- 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`.paddingValue
- The number to fill the area outside the specified diagonal band with.
Default is 0.public Output<T> output()
public Output<T> asOutput()
Operand
Inputs to TensorFlow operations are outputs of another TensorFlow operation. This method is used to obtain a symbolic handle that represents the computation of the input.
asOutput
in interface Operand<T>
OperationBuilder.addInput(Output)
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