Python 如何迭代一组张量并将每个组中的元素传递给函数?
假设有3个大小相同的张量:Python 如何迭代一组张量并将每个组中的元素传递给函数?,python,function,iteration,pytorch,tensor,Python,Function,Iteration,Pytorch,Tensor,假设有3个大小相同的张量: a = torch.randn(3,3) a = ([[ 0.1945, 0.8583, 2.6479], [-0.1000, 1.2136, -0.3706], [-0.0094, 0.4279, -0.6840]]) b = torch.randn(3, 3) b = ([[-1.1155, 0.2106, -0.2183], [ 1.6610, -0.6953, 0.00
a = torch.randn(3,3)
a = ([[ 0.1945, 0.8583, 2.6479],
[-0.1000, 1.2136, -0.3706],
[-0.0094, 0.4279, -0.6840]])
b = torch.randn(3, 3)
b = ([[-1.1155, 0.2106, -0.2183],
[ 1.6610, -0.6953, 0.0052],
[-0.8955, 0.0953, -0.7737]])
c = torch.randn(3, 3)
c = ([[-0.2303, -0.3427, -0.4990],
[-1.1254, 0.4432, 0.3999],
[ 0.2489, -0.9459, -0.5576]])
在Lua(火炬7)中,它们具有以下功能:
[self] map2(tensor1, tensor2, function(x, xt1, xt2))
它将给定的函数
应用于self
的所有元素
我的问题是:
for loop
和index
0.1945 -1.1155 -0.2303
0.8583 0.2106 -0.3427
2.6479 -0.2183 -0.4990
-0.1000 1.6610 -1.1254
...
Edit_1:我也尝试了itertools.zip和zip,但结果与我前面提到的不一样您可以使用Python的
map
函数,类似于您所提到的。像这样:
>>> tensor_list = [torch.tensor([i, i, i]) for i in range(3)]
>>> list(map(lambda x: x**2, tensor_list))
[tensor([0, 0, 0]), tensor([1, 1, 1]), tensor([4, 4, 4])]
>>>
编辑:对于仅PyTorch的方法,您可以使用torch.Tensor.apply
(注意,这会进行适当的更改,不会返回新的Tensor)
>>> x = torch.tensor([[1, 2, 3], [4, 5, 6], [7, 8, 9]])
>>> x.apply_(lambda y: y ** 2)
tensor([[ 1, 4, 9],
[16, 25, 36],
[49, 64, 81]])
>>>