Returns the batched diagonal part of a batched tensor.
tf.raw_ops.MatrixDiagPartV2( input, k, padding_value, name=None ) Returns a tensor with the k[0]-th to k[1]-th diagonals of the batched input.
Assume input has r dimensions [I, J, ..., L, M, N]. Let max_diag_len be the maximum length among all diagonals to be extracted, max_diag_len = min(M + min(k[1], 0), N + min(-k[0], 0)) Let num_diags be the number of diagonals to extract, num_diags = k[1] - k[0] + 1.
If num_diags == 1, the output tensor is of rank r - 1 with shape [I, J, ..., L, max_diag_len] and values:
diagonal[i, j, ..., l, n] = input[i, j, ..., l, n+y, n+x] ; if 0 <= n+y < M and 0 <= n+x < N, padding_value ; otherwise. where y = max(-k[1], 0), x = max(k[1], 0).
Otherwise, the output tensor has rank r with dimensions [I, J, ..., L, num_diags, max_diag_len] with values:
diagonal[i, j, ..., l, m, n] = input[i, j, ..., l, n+y, n+x] ; if 0 <= n+y < M and 0 <= n+x < N, padding_value ; otherwise. where d = k[1] - m, y = max(-d, 0), and x = max(d, 0).
The input must be at least a matrix.
For example:
input = np.array([[[1, 2, 3, 4], # Input shape: (2, 3, 4) [5, 6, 7, 8], [9, 8, 7, 6]], [[5, 4, 3, 2], [1, 2, 3, 4], [5, 6, 7, 8]]]) # A main diagonal from each batch. tf.matrix_diag_part(input) ==> [[1, 6, 7], # Output shape: (2, 3) [5, 2, 7]] # A superdiagonal from each batch. tf.matrix_diag_part(input, k = 1) ==> [[2, 7, 6], # Output shape: (2, 3) [4, 3, 8]] # A tridiagonal band from each batch. tf.matrix_diag_part(input, k = (-1, 1)) ==> [[[2, 7, 6], # Output shape: (2, 3, 3) [1, 6, 7], [5, 8, 0]], [[4, 3, 8], [5, 2, 7], [1, 6, 0]]] # Padding value = 9 tf.matrix_diag_part(input, k = (1, 3), padding_value = 9) ==> [[[4, 9, 9], # Output shape: (2, 3, 3) [3, 8, 9], [2, 7, 6]], [[2, 9, 9], [3, 4, 9], [4, 3, 8]]] Returns | |
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A Tensor. Has the same type as input. |