Construct list(tensors) 创建一个包含张量的列表,以及2个张量如下: AI检测代码解析 importtoroch a=[torch.tensor([[0.7,0.3], [0.2,0.8]]), torch.tensor([[0.5,0.9], [0.5,0.5]])] b=torch.tensor([[0.1,0.9], [0.3,0.7]]) c=torch.tensor([[0.1,0.9,0.5], [0.3,0.7,0.0]]) 1. 2. 3...
bitwise_not_() → Tensor bmm(batch2) → Tensor bool() → Tensor byte() → Tensor cauchy_(median=0, sigma=1, *, generator=None) → Tensor ceil() → Tensor ceil_() → Tensor char() → Tensor cholesky(upper=False) → Tensor cholesky_inverse(upper=False) → Tensor cholesky_solve(inpu...
torch.chunk(input, chunks, dim=0)→ List of Tensors Splits a tensor into a specific number of chunks. Last chunk will be smaller if the tensor size along the given dimension dim is not divisible by chunks. Parameters input (Tensor)– the tensor to split chunks (int)– number of ch...
get_attrretrieves a parameter from the module hierarchy.nameis similarly the name the result of the fetch is assigned to.targetis the fully-qualified name of the parameter’s position in the module hierarchy.argsandkwargsare don’t-care ...
,device=None,requires_grad=False,pin_memory=False)data:数据,可以是list,numpydtype:默认是data的数据类型device...:data的梯度grad_fn 创建Tensor的Function,是自动求导的关键requires_grad: 指示是否需要梯度 is_leaf:指示是否是叶子结点(张量)dtype:张量的 ...
torch.Tensor是默认的tensor类型(torch.FlaotTensor)的简称。 一个张量tensor可以从Python的list或序列构建: >>> torch.FloatTensor([[1, 2, 3], [4, 5, 6]]) 1 2 3 4 5 6 [torch.FloatTensor of size 2x3] 一个空张量tensor可以通过规定其大小来构建: ...
Returns a tensor where each row contains num_samples indices sampled from the multinomial probability distribution located in the corresponding row of tensor input. Note The rows of input do not need to sum to one (in which case we use the values as weights), but must be non-negative, fini...
return func(*args, **kwargs) File "/usr/local/python3.7.5/lib/python3.7/site-packages/torch_npu/utils/tensor_methods.py", line 66, in _npu return torch_npu._C.npu(self, *args, **kwargs) RuntimeError: Initialize:/usr1/workspace/FPTA_Daily_open_pytorchv1.11.0-5.0.rc1/CODE/to...
# This computes the matrix multiplication between two tensors. y1, y2, y3 will have the same value# ``tensor.T`` returns the transpose of a tensor#矩阵乘法y1=tensor@tensor.Ty2=tensor.matmul(tensor.T)y3=torch.rand_like(y1)torch.matmul(tensor,tensor.T,out=y3)# This computes the element...
切片 torch.tensor_split(input, indices_or_sections, dim=0) → List of Tensors 是按照索引拆分。相当于你指定了拆分位置的下标; 组合/拼接 torch.cat(tensors, dim=0, ***, out=None) → Tensor 拼接tensor 序列,可以指定dim 组合/拼接 torch.stack(tensors, dim=0, ***, out=None) → Tensor ...