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。默认情况下,返回的Tensor与此张量具有相同的torch.dtype和torch.device。 >>> tensor = torch.ones((2,), dtype=torc
torch.unsqueeze(tensor, dim)函数 功能: 返回一个新的tensor,新tensor增加了一个纬度,新的纬度的大小是1。 参数 tensor: 在哪个tensor上增加纬度 dim:在哪个纬度之前增加一个纬度 示例: target1: 在第一个纬度之前增加一个纬度(原来是[4],增加以后变为[1, 4]) target2: 在第二个纬度之前(也就是第一个...
argmax(dim=None, keepdim=False) → LongTensor argmin(dim=None, keepdim=False) → LongTensor argsort(dim=-1, descending=False) → LongTensor asin() → Tensor asin_() → Tensor as_strided(size, stride, storage_offset=0) → Tensor atan() → Tensor atan2(other) → Tensor atan2_(other...
Tensor的三角函数 Tensor中其他的数学函数 Tensor中统计学相关的函数(维度,对于二维数据:dim=0 按列,dim=1 按行,默认 dim=1) Tensor的torch.distributions(分布函数) Tensor中的随机抽样 Tensor中的范数运算 Tensor中的矩阵分解 微信公众号:数学建模与人工智能 QInzhengk/Math-Model-and-Machine-Learning (github.co...
If keepdim is True, the output tensors are of the same size as input except in the dimension dim where they are of size 1. Otherwise, dim is squeezed (see torch.squeeze()), resulting in the output tensors having 1 fewer dimension than input. ...
If keepdim is True, the output tensors are of the same size as input except in the dimension dim where they are of size 1. Otherwise, dim is squeezed (see torch.squeeze()), resulting in the output tensors having 1 fewer dimension than input. ...
[4, 0.0, 0.0,-0.4]]))print(non_zero)#torch.LongTensor of size 4x2#0 0#1 0#1 1#2 0#2 2#3 0#torch.split(tensor, split_size, dim=0) 与前面torch.chunk类似print(torch.split(x,2))#(#0 0 0#0 0 0#[torch.FloatTensor of size 2x3]#,#1.00000e-42 *#0.0000 0.0000 0.0000#0.0000...
Returns a namedtuple(values, indices)wherevaluesis the minimum value of each row of theinputtensor in the given dimensiondim. Andindicesis the index location of each minimum value found (argmin). IfkeepdimisTrue, the output tensors are of the same size asinputexcept in the dimensiondimwhere...
Named tensors with first-class dimensions can accomplish the same goal, but using PyTorch's existing operator set. Automatically batching Code (vmap, xmap) The implicit batching of Rule #1 means it is easy to created batched versions of existing PyTorch code. Simply bind a dim to the dimensio...
Here, torch-mlir/lib/Conversion/TorchToTMTensor/TorchToTMTensor.cpp Line 1532 in 34f6948 op, "unimplemented: only constant dim value is supported"); the TorchToTMTensor lowering checks to make sure that dim is a TorchConstantInt. However...