在卷积神经网络时经常会用到numpy的数组变量类型与tensor张量类型之间的转换，在这里总结了一下。
import numpy as np
import torch
a = np.random.normal(0, 1, (2, 3))
b= torch.tensor(a)
a,b
(array([[-0.37468825, 0.81942854, -1.56010579],
[-0.00805839, 0.9578339 , 1.95072451]]),
tensor([[-0.3747, 0.8194, -1.5601],
[-0.0081, 0.9578, 1.9507]], dtype=torch.float64))
a = np.random.normal(0, 1, (4, 5))
b= torch.from_numpy(a)
a,b
(array([[-0.47197733, 1.53828686, -1.72156097],
[-2.05017441, 1.23956538, -0.80934275]]),
tensor([[-0.4720, 1.5383, -1.7216],
[-2.0502, 1.2396, -0.8093]], dtype=torch.float64))
import numpy as np
import torch
t = torch.arange(1, 10).reshape(3, 3)
x = t.numpy()
t,x
(tensor([[1, 2, 3],
[4, 5, 6],
[7, 8, 9]]),
array([[1, 2, 3],
[4, 5, 6],
[7, 8, 9]], dtype=int64))
x = t.detach().numpy()
使用.item将张量转化为单独的数值进行输出
a = torch.tensor(1)
a.item()
tensor(1)
1
t = torch.arange(10)
t1 = t.tolist()
t2 = list(t)
t,t1,t2
(tensor([0, 1, 2, 3, 4, 5, 6, 7, 8, 9]),
[0, 1, 2, 3, 4, 5, 6, 7, 8, 9])
[tensor(0),
tensor(1),
tensor(2),
tensor(3),
tensor(4),
tensor(5),
tensor(6),
tensor(7),
tensor(8),
tensor(9)]
Original: https://blog.csdn.net/weixin_45464524/article/details/127953053
Author: Chaoy6565
Title: numpy与tensor张量的转换
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