tensor=torch.rand(3,4)print(f"Shape of tensor: {tensor.shape}")print(f"Datatype of tensor: {tensor.dtype}")print(f"Device tensor is stored on: {tensor.device}")---Shapeoftensor:torch.Size([3,4])Datatypeoftensor:torch.float32Devicetensorisstoredon:cpu 2 张量上的操作 这里(https://...
shape is a tuple of tensor dimensions. In the functions below, it determines the dimensionality of the output tensor. shape是一个元组,代表tensor的维度 shape = (2,3,) rand_tensor = torch.rand(shape) ones_tensor = torch.ones(shape) zeros_tensor = torch.zeros(shape) print(f"Random ...
Returns a tensor with the same data and number of elements asinput(返回与输入具有相同数据和元素数量的张量)but with the specified shape(但是具有指定形状). When possible, the returned tensor will be a view ofinput(如果可能,返回的张量将是输入的视图,也就是说原本的tensor并没有被改变,如果想要改变...
The shape of the tensor isdefinedby the variable argument:attr:`size`.Args:size (int...): a sequence of integers defining the shape of the output tensor. Can be a variable number of argumentsora collection like a listortuple. Keywordargs:generator (:class:`torch.Generator`, optional): a...
Returns a new tensor with the arccosine of the elements of input. outi=cos−1(inputi)\text{out}_{i} = \cos^{-1}(\text{input}_{i}) outi=cos−1(inputi) Parameters input (Tensor)– the input tensor. out (Tensor, optional)– the output tensor. ...
torch.nonzero(input, *, out=None, as_tuple=False) → LongTensor or tuple of LongTensors torch.reshape(input, shape) → Tensor torch.split(tensor, split_size_or_sections, dim=0)[source] torch.squeeze(input, dim=None, out=None) → Tensor ...
tensor=torch.rand(3,4) print(f"Shape of tensor:{tensor.shape}") # 这条语句等效于 print("Shape of tensor:{}".format(tensor.shape))
mask: torch.Tensor, bidirectional: bool = False)-> torch.Tensor:""" Given the output from a ``Seq2SeqEncoder``, with shape ``(batch_size, sequence_length, encoding_dim)``, this method returns the final hidden state for each element of the batch, ...
Copies the elements of tensor into the original tensor by selecting the indices in the order given in index. The shape of tensor must exactly match the elements indexed or an error will be thrown.> x 0.8020 0.7246 0.1204 0.3419 0.4385 0.0369 0.4158 0.0985 0.3024 0.8186 0.2746 0.9362 0.2546 ...
torch.compileraises an error thatValueError: Cannot view a tensor with shape torch.Size([1, 32, 32, 32]) and strides (32768, 1, 1024, 32) as a tensor with shape (16, 2048)! By contrast, if we turn ontrainingof the model, which disables thefuse_binary, they will both succeed ...