import math
import torch
import numpy as np
import pandas as pd

A = np.array([[1,2,3],[6,5,3]])
print(A, '\n')
B = torch.from_numpy(A)  #将numpy 转换化为 tensor
print(B)
C = B.numpy()#tensor 转换化为 numpy 但是对该numpy进行修改会改变其他的的值
 # 对C该表 A,B 都会相应的改变
C[1] = 0
print(A, '\n')
print(B)
print(C)

import torch
import numpy as np

#创建一个numpy array的数组
array = np.array([1,2,3,4])

#将numpy array转换为torch tensor
tensor = torch.tensor(array)
Tensor = torch.Tensor(array)
as_tensor = torch.as_tensor(array)
from_array = torch.from_numpy(array)

print(array.dtype)      #int32
#查看torch默认的数据类型
print(torch.get_default_dtype())    #torch.float32

#对比几种不同方法之间的异同
print(tensor.dtype)     #torch.int32
print(Tensor.dtype)     #torch.float32
print(as_tensor.dtype)  #torch.int32
print(from_array.dtype) #torch.int32
array[0] = 10

print(tensor)     # tensor([1, 2, 3, 4], dtype=torch.int32)
print(Tensor)     # tensor([1., 2., 3., 4.])
print(as_tensor)  #tensor([10,  2,  3,  4], dtype=torch.int32)
print(from_array) #tensor([10,  2,  3,  4], dtype=torch.int32)
# 后面两种数据改变，前面不变

tensor 转化为numpy 只有找到 a.numpy