torch.add(t1, t2)两个张量逐元素相加，等效于 t1 + t2torch.add(torch.tensor([1, 2]), torch.tensor([3, 4])) # 输出 tensor([4, 6])torch.subtract(t1, t2)两个张量逐元素相减，等效于 t1 - t2torch.subtract(torch.tensor([1, 2]), torch.tensor([3
.FloatTensor(3,1,1)>>> (x.add_(y)).size() torch.Size([5,3,4,1])# but:>>> x=torch.FloatTensor(1,3,1)>>> y=torch.FloatTensor(3,1,7)>>> (x.add_(y)).size()RuntimeError:Theexpanded size of the tensor (1) must match the existing size (7) at non-singleton dimension2...
repeat 前面提到过input.expand(*sizes)函数能够实现 input 输入张量中单维度(singleton dimension)上数据的复制操作。「对于非单维度上的复制操作,expand 函数就无能为力了,此时就需要使用input.repeat(*sizes)。」 input.repeat(*sizes)可以对 input 输入张量中的单维度和非单维度进行复制操作,并且会真正的复制数据...
AI代码解释 RuntimeError:The sizeoftensora(3)must match the sizeoftensorb(4)at non-singleton dimension1 报错原因在于b会broadcasting成[1, 4, 64, 32],但与a相乘时,dim=1位置上的3和4不相同,无法进行运算。因此矩阵在进行乘法运算时,要么就相同dim位置上的数值相同,要么就为1,以方便进行broadcasting。
A linear layer expects a 2D tensor as an input, where the first dimension is the batch size and the second dimension is the number of features. You can use the torch.unsqueeze() function to add a singleton dimension to your 1D tensor. For example, if your input tensor has the shape of...
0.9694]]]) # 扩展了第1/3维度的数据 >>> a.expand([1,4,1]) Traceback (most recent call last): File "<pyshell#242>", line 1, in <module> a.expand([1,4,1]) RuntimeError: The expanded size of the tensor (4) must match the existing size (2) at non-singleton dimension 1....
虽然只用后两个乘 ''' RuntimeError Traceback (most recent call last) Input In [46], in <cell line: 2>() 1 b=torch.rand(4,64,32) ---> 2 torch.matmul(a,b).shape RuntimeError: The size of tensor a (3) must match the size of tensor b (4) at non-singleton dimension 1''...
timeError Traceback (most recent call last) <ipython-input-17-72fb34250db7> in <module>() 1 x=torch.empty(5,2,4,1) 2 y=torch.empty(3,1,1) ---> 3 (x+y).size() RuntimeError: The size of tensor a (2) must match the size of tensor b (3) at non-singleton dimension 1...
print(d.shape) # 会报错:RuntimeError: The size of tensor a (1) must match the size of tensor b (2) at non-singleton dimension 0 张量与numpy数组之间的互相转换和共享内存机制 张量与numpy数组之间的互相转换是指可以使用torch.from_numpy()和numpy()函数来实现张量和数组之间的相互转化。例如:...
>>> (x.add_(y)).size() RuntimeError: The expanded size of the tensor (1) must match the existing size (7) at non-singleton dimension 2.Copy 向后兼容 只要每个张量中的元素数量相等,以前的 PyTorch 版本都可以在具有不同形状的张量上执行某些逐点函数。然后,通过将每个张量视为一维来执行逐点操...