# 需要导入模块: import torch [as 别名]# 或者: from torch importmul[as 别名]def_private_mul(self, other, equation: str):"""Abstractly Multiplies two tensors Args: self: an AdditiveSharingTensor other: another AdditiveSharingTensor equation: a string representation of the equation to be comput...
def _private_mul(self, other, equation: str): """Abstractly Multiplies two tensors Args: self: an AdditiveSharingTensor other: another AdditiveSharingTensor equation: a string representation of the equation to be computed in einstein summation form """ # check to see that operation is either...
Multiply two 1D tensorst1 = torch$tensor(c(1, 2)) t2 = torch$tensor(c(3, 2)) t1 #> tensor([1., 2.]) t2 #> tensor([3., 2.])t1 * t2 #> tensor([3., 4.])t1 = torch$tensor(list( c(1, 2, 3), c(1, 2, 3) )) t2 = torch$tensor(list( c(1, 2), c(1, ...
Multiply elements of tensor2 by the scalar value and add it to tensor1. The number of elements must match, but sizes do not matter.> x = torch.Tensor(2, 2):fill(2) > y = torch.Tensor(4):fill(3) > x:add(2, y) > x 8 8 8 8 [torch.DoubleTensor of size 2x2]...
Performs the element-wise division of tensor1 by tensor2, multiply the result by the scalar value and add it to input. outi=inputi+value×tensor1itensor2i\text{out}_i = \text{input}_i + \text{value} \times \frac{\text{tensor1}_i}{\text{tensor2}_i} outi=inputi+value×...
giving a tensor of shape ``(batch_size, encoding_dim)``. This is not as simple as ``encoder_outputs[:, -1]``, because the sequences could have different lengths. We use the mask (which has shape ``(batch_size, sequence_length)``) to find the final state for each batch ...
For instance, a matrix-multiply will take two input Tensors and produce one output Tensor. Nodes can produce multiple outputs. For instance prim::TupleUnpack splits a tuple into its components, so it has a number of outputs equal to the number of members of the tuple. Though Nodes may ...
torch.sum(input, dim, keepdim=False, dtype=None) → Tensor torch.unique_consecutive(input, return_inverse=False, return_counts=False, dim=None)[source] torch.var() torch.var(input, dim, keepdim=False, unbiased=True, out=None) → Tensor ...
torch.broadcast_tensors Supported torch.index_reduce Partly supported Function is constrained torch.chain_matmul Supported torch.view_as_complex Partly Supported Function is constrained torch.empty_strided Supported torch.cumulative_trapezoid Supported torch.can_cast Supported torch.diagonal_scatter S...
kaldi expects the first cepstral to be weighted sum of factor sqrt(1/num_mel_bins) # this would be the first column in the dct_matrix for torchaudio as it expects a # right multiply (which would be the first column of the kaldi's dct_matrix as kaldi # expects a left mul...