1对角矩阵
输入
import torch import numpy as np #numpy实现 a = np.diag([5,6,7]) print(a) #torch实现 b = torch.diag(torch.tensor([5,6,7])) print(b)
输出
[[5 0 0] [0 6 0] [0 0 7]] tensor([[5, 0, 0], [0, 6, 0], [0, 0, 7]])
2单位矩阵
输入
import torch import numpy as np #numpy实现 a = np.eye(3,4) print(a) #torch实现 b = torch.eye(4,5) print(b)
输出
[[1. 0. 0. 0.] [0. 1. 0. 0.] [0. 0. 1. 0.]] tensor([[1., 0., 0., 0., 0.], [0., 1., 0., 0., 0.], [0., 0., 1., 0., 0.], [0., 0., 0., 1., 0.]])
3下三角矩阵
输入
import torch import numpy as np #numpy实现 a = np.tri(3,3) print(a) #torch实现 b = torch.tril(torch.ones(4,4)) print(b)
输出
[[1. 0. 0.] [1. 1. 0.] [1. 1. 1.]] tensor([[1., 0., 0., 0.], [1., 1., 0., 0.], [1., 1., 1., 0.], [1., 1., 1., 1.]])
4 0,1矩阵
4.1 0矩阵
import torch import numpy as np #numpy实现 a = np.zeros((4,3)) print(a) #torch实现 b = torch.zeros((4,4)) print(b)
输出
[[0. 0. 0.]
[0. 0. 0.]
[0. 0. 0.]
[0. 0. 0.]]
tensor([[0., 0., 0., 0.],
[0., 0., 0., 0.],
[0., 0., 0., 0.],
[0., 0., 0., 0.]])
4.2 1矩阵
输入
#特殊矩阵 #单位矩阵 import torch import numpy as np #numpy实现 a = np.ones((4,3)) print(a) #torch实现 b = torch.ones((4,4)) print(b)
输出
[[1. 1. 1.] [1. 1. 1.] [1. 1. 1.] [1. 1. 1.]] tensor([[1., 1., 1., 1.], [1., 1., 1., 1.], [1., 1., 1., 1.], [1., 1., 1., 1.]])
原文:https://www.cnblogs.com/xbbk/p/13362105.html