torch.stack (tensors, dim = 0) : 在一个新的维度拼接tensor, tensors : sequence of tensors torch.cat (tensors, dim=0) : 在一个已有的维度拼接tensor torch.topk (input, k , dim=None, largest=True) : 在某个维度上返回前k大(小)的元素,返回一个tuple (values,indices) torch.clamp (input...
>>>importtorch>>>a=torch.arange(0,16).view(4,4)>>>atensor([[0,1,2,3],[4,5,6,7],[8,9,10,11],[12,13,14,15]])>>>index=torch.tensor([[0,1,2,3]])# 选取对角线元素>>>torch.gather(a,0,index)tensor([[0,5,10,15]]) output值定义如下: # 按照 index = tensor([[0,...
index(LongTensor) – the indices of elements to scatter, can be either empty or the same size of src. When empty, the operation returns identity src(Tensor) – the source element(s) to scatter, incase value is not specified value(float) – the source element(s) to scatter, incase src ...
fill_diagonal_(fill_value, wrap=False) → Tensor digamma() → Tensor digamma_() → Tensor dim() → int dist(other, p=2) → Tensor div(value) → Tensor div_(value) → Tensor dot(tensor2) → Tensor double() → Tensor eig(eigenvectors=False) -> (Tensor, Tensor) element_size() →...
_tensor_str._str(self) def backward(self, gradient=None, retain_graph=None, create_graph=False): r"""Computes the gradient of current tensor w.r.t. graph leaves. The graph is differentiated using the chain rule. If the tensor is non-scalar (i.e. its data has more than one element...
Tensor的接口设计与numpy类似,从接口的角度讲,对Tensor的操作可分为两类:(1)torch.function,如:torch.save等。(2)tensor.function,如:tensor.view等。为方便使用,对tensor的大部分操作同时支持这两种接口,如:torch.sum(a,b)与a.sum(b)功能等价;从存储的角度讲,对Tensor的操作又可分为两类:(1)不会修改自身...
tensor.size # Shapeofthe tensor. Itisa subclassofPython tuple tensor.dim # Numberofdimensions. 数据类型转换 # Set default tensor type. Float in PyTorch is much faster than double. torch.set_default_tensor_type(torch.FloatTensor) # Type convertions. ...
torch.Tensor是默认的tensor类型(torch.FlaotTensor)的简称。张量可以从Python的list或序列构成:>>> torch.FloatTensor([[1, 2, 3], [4, 5, 6]]) 1 2 3 4 5 6 [torch.FloatTensor of size 2x3]可以通过指定它的大小来构建一个空的张量:>>> torch.IntTensor(2, 4).zero_() 0 0 0 0 ...
torch.float32>>>torch.set_default_tensor_type(torch.DoubleTensor)>>>torch.tensor([1.2,3]).dtype # anewfloatingpoint tensor torch.float64 torch.numel(input) → int Returns the total number of elements in theinputtensor. Parameters input(Tensor) – the input tensor. ...
# Matrix multiplication: (m*n) * (n*p) -> (m*p).result= torch.mm(tensor1, tensor2)# Batch matrix multiplication: (b*m*n) * (b*n*p) -> (b*m*p).result= torch.bmm(tensor1, tensor2)# Element-wise multiplication.result= tensor1 * tensor2 ...