addr_(beta=1, alpha=1, vec1, vec2) → Tensor addr()的in-place运算形式 apply_(callable) → Tensor 将函数callable作用于tensor中每一个元素,并将每个元素用callable函数返回值替代。 !注意:该函数只能在CPU tensor中使用,并且不应该用在有较高性能要求的代码块。 asin
torch.cat(inputs, dimension=0): 在给定维度上对输入的张量序列seq进行连接操作。 inputs (sequence of Tensors) dimension (int optional) - 沿着此维连接张量序列 torch.chunk(tensor, chunks, dim=0): 在给定维度上将输入张量进行分块 tensors(Tensors) - 待分场的输入张量 chunks (int) - 分块的个数...
具体来说,对于src中的每个值,其在张量self中的输出索引由其dimension != dim处的索引值和其对应在张量index中dimension= dim处的值所组成(这句话比较绕,看公式和下面的例子可以帮助理解)。 Writes all values from the tensor src into self at the indices specified in the index tensor. For each value in...
因此tensor.new_tensor(x) 等效于 x.clone().detach();tensor.new_tensor(x,requires_grad=True)等效于x.clone().detach().requires_grad_(True)。建议使用clone()和 detach()。 2new_full(size,fill_value,dtype=None,device=None,requires_grad=False)→ Tensor 返回size大小的值为fill_value的tensor。默...
new_zeros(size, dtype=None, device=None, requires_grad=False) → Tensor is_cuda device grad T abs() → Tensor abs_() → Tensor acos() → Tensor acos_() → Tensor add(value) → Tensor add_(value) → Tensor add_(value=1, other) -> Tensor ...
add_(value) add()的in-place运算形式 addbmm(beta=1, mat, alpha=1, batch1, batch2) → Tensor 请查看torch.addbmm() addbmm_(beta=1, mat, alpha=1, batch1, batch2) → Tensor addbmm()的in-place运算形式 addcdiv(value=1, tensor1, tensor2) → Tensor ...
torch.add(input, other, out=None) Adds the scalar other to each element of the input input and returns a new resulting tensor. out=input+other\text{out} = \text{input} + \text{other} out=input+other If input is of type FloatTensor or DoubleTensor, other must be a real number, othe...
问如何在一些维度中管理可变长度的torch.Tensor？EN当前我们的混合的参数都需要传递，如果都需要全部传递...
tensors along a new dimension.print(x)print(torch.stack((x,x),1));print(torch.stack([x,x],dim=2)#torch.t(input,out=None) → Tensor 转置print(torch.t(x))#torch.FloatTensor of size 3x5#print(torch.t(torch.Tensor(1,2,3,4))) #RuntimeError: t() expects a 2D tensor, but ...
Python objects that represent dimension are created using the dims operator.1 import torch from torchdim import dims batch, channel, width, height = dims(4) The existing implemention of Named Tensors in PyTorch, or JAX's xmap use strings to name dimensions. We call these dimensions first clas...