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MatrixSetDiagV2

public final class MatrixSetDiagV2

Returns a batched matrix tensor with new batched diagonal values.

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)`.

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(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(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([[[1, 2, 3],  # Diagonal shape: (2, 2, 3)
                        [4, 5, 0]],
                       [[6, 1, 2],
                        [3, 4, 0]]])
 tf.matrix_set_diag(diagonals, k = (-1, 0))
   ==> [[[1, 7, 7, 7],  # Output shape: (2, 3, 4)
         [4, 2, 7, 7],
         [0, 5, 3, 7]],
        [[6, 7, 7, 7],
         [3, 1, 7, 7],
         [7, 4, 2, 7]]]
 
 

Public Methods

Output<T>
asOutput()
Returns the symbolic handle of a tensor.
static <T> MatrixSetDiagV2<T>
create(Scope scope, Operand<T> input, Operand<T> diagonal, Operand<Integer> k)
Factory method to create a class wrapping a new MatrixSetDiagV2 operation.
Output<T>
output()
Rank `r+1`, with `output.shape = input.shape`.

Inherited Methods

Public Methods

public Output<T> asOutput ()

Returns the symbolic handle of a tensor.

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.

public static MatrixSetDiagV2<T> create (Scope scope, Operand<T> input, Operand<T> diagonal, Operand<Integer> k)

Factory method to create a class wrapping a new MatrixSetDiagV2 operation.

Parameters
scope current scope
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]`.
Returns
  • a new instance of MatrixSetDiagV2

public Output<T> output ()

Rank `r+1`, with `output.shape = input.shape`.