如何在 PyTorch 中访问张量的元数据？
我们访问张量的大小（或形状）和张量中元素的数量作为张量的元数据。要访问张量的大小，我们使用.size()方法和张量的形状使用.shape访问。
两者。size()和.shape产生相同的结果。我们使用该函数来查找张量中元素的总数。torch.numel()
脚步
导入所需的库。在这里，所需的库是torch。确保您已经安装了torch。
定义PyTorch张量。
查找张量的元数据。使用.size()和.shape访问张量的大小和形状。使用访问张量元素的数量。torch.numel()
打印张量和元数据以更好地理解。
示例1
# Python Program to access meta-data of a Tensor
# import necessary libraries
import torch
# Create a tensor of size 4x3
T = torch.Tensor([[1,2,3],[2,1,3],[2,3,5],[5,6,4]])
print("T:\n", T)
# Find the meta-data of tensor
# Find the size of the above tensor "T"
size_T = T.size()
print("size of tensor T:\n", size_T)
# Other method to get size using .shape
print("Shape of tensor:\n", T.shape)
# Find the number of elements in the tensor "T"
num_T = torch.numel(T)
print("Number of elements in tensor T:\n", num_T)输出结果当您运行上述Python3代码时，它将产生以下输出。
T:
tensor([[1., 2., 3.],
[2., 1., 3.],
[2., 3., 5.],
[5., 6., 4.]])
size of tensor T:
torch.Size([4, 3])
Shape of tensor:
torch.Size([4, 3])
Number of elements in tensor T:
12示例2
# Python Program to access meta-data of a Tensor
# import the libraries
import torch
# Create a tensor of random numbers
T = torch.randn(4,3,2)
print("T:\n", T)
# Find the meta-data of tensor
# Find the size of the above tensor "T"
size_T = T.size()
print("size of tensor T:\n", size_T)
# Other method to get size using .shape
print("Shape of tensor:\n", T.shape)
# Find the number of elements in the tensor "T"
num_T = torch.numel(T)
print("Number of elements in tensor T:\n", num_T)输出结果当您运行上述Python3代码时，它将产生以下输出。
T:
tensor([[[-1.1806, 0.5569],
[ 2.2237, 0.9709],
[ 0.4775, -0.2491]],
[[-0.9703, 1.9916],
[ 0.1998, -0.6501],
[-0.7489, -1.3013]],
[[ 1.3191, 2.0049],
[-0.1195, 0.1860],
[-0.6061, -1.2451]],
[[-0.6044, 0.6153],
[-2.2473, -0.1531],
[ 0.5341, 1.3697]]])
size of tensor T:
torch.Size([4, 3, 2])
Shape of tensor:
torch.Size([4, 3, 2])
Number of elements in tensor T:
24