dgl.sparse.SparseMatrix.softmax¶
-
SparseMatrix.
softmax
() → dgl.sparse.sparse_matrix.SparseMatrix¶ Applies row-wise softmax to the non-zero elements of the sparse matrix.
Equivalently, applies softmax to the non-zero elements of the sparse matrix along the column (
dim=1
) dimension.If
input.val
takes shape(nnz, D)
, then the output matrixoutput
andoutput.val
take the same shape asinput
andinput.val
.output.val[:, i]
is calculated based oninput.val[:, i]
.- Parameters
input (SparseMatrix) – The input sparse matrix
- Returns
The output sparse matrix
- Return type
Examples
Case1: matrix with values of shape (nnz)
>>> indices = torch.tensor([[0, 0, 1, 2], [1, 2, 2, 0]]) >>> nnz = len(row) >>> val = torch.arange(nnz).float() >>> A = dglsp.spmatrix(indices, val) >>> dglsp.softmax(A) SparseMatrix(indices=tensor([[0, 0, 1, 2], [1, 2, 2, 0]]), values=tensor([0.2689, 0.7311, 1.0000, 1.0000]), shape=(3, 3), nnz=4)
Case2: matrix with values of shape (nnz, D)
>>> indices = torch.tensor([[0, 0, 1, 2], [1, 2, 2, 0]]) >>> val = torch.tensor([[0., 7.], [1., 3.], [2., 2.], [3., 1.]]) >>> A = dglsp.spmatrix(indices, val) >>> dglsp.softmax(A) SparseMatrix(indices=tensor([[0, 0, 1, 2], [1, 2, 2, 0]]), values=tensor([[0.2689, 0.9820], [0.7311, 0.0180], [1.0000, 1.0000], [1.0000, 1.0000]]), shape=(3, 3), nnz=4, val_size=(2,))