Webtorch.tanh(input, *, out=None) → Tensor Returns a new tensor with the hyperbolic tangent of the elements of input. \text {out}_ {i} = \tanh (\text {input}_ {i}) outi = tanh(inputi) … WebJul 29, 2024 · diag = torch.tensor ( [11,22,33,44]) off_diag = torch.tensor ( [ [12,13,14], [21,23,24], [31,32,34], [41,42,43]]) matrix = _merge_on_and_off_diagonal (diag, off_diag) """ returns torch.tensor ( [ [11,12,13,14], [21,22,23,24], [31,32,33,34], [41,42,43,44]]) """ diag = torch.tensor ( [ [11,22,33,44], [11,22,33,44]]) off_diag = torch.tensor ( [ [ …
pytorch_sparse/diag.py at master · rusty1s/pytorch_sparse
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torch — PyTorch 2.0 documentation
Webtorch.eye¶ torch. eye (n, m = None, *, out = None, dtype = None, layout = torch.strided, device = None, requires_grad = False) → Tensor ¶ Returns a 2-D tensor with ones on the diagonal and zeros elsewhere. Parameters:. n – the number of rows. m (int, optional) – the number of columns with default being n. Keyword Arguments:. out (Tensor, optional) – … WebJul 7, 2024 · and want to extract the diagonal of each matrix in that batch to get diag_T = [ [0.9527, 0.6147], [0.0672, 0.4532], [0.0992, 0.0925]] Is there some torch.diag () function that also works for batches? 1 Like LeviViana (Levi Viana) July 7, 2024, 8:24pm #2 Maybe not the best solution, but it is vectorized: WebPyTorch - torch.diag_embed 创建张量,其某些二维平面的对角线(由dim1和dim2指定)被填充输入。 torch.diag_embed torch.diag_embed (input, offset=0, dim1=-2, dim2=-1) → Tensor 创建一个张量,其特定2D平面(由 dim1 和 dim2 指定)的对角线由 input 填充。 为了便于创建成批的对角矩阵,默认情况下选择由返回张量的最后两个维度形成的2D平面 … the paul center