import torch
import numpy as np
a = torch.tensor([[[1]]])
#只有一个数据的时候,获取其数值
print(a.item()) #tensor转化为nparray
b = a.numpy()
print(b,type(b),type(a)) #获取张量的形状
a = torch.tensor(np.arange(30).reshape(3,2,5))
print(a)
print(a.shape)
print(a.size())
print(a.size(0)) #形状变换
print(a.view([2,3,5])) #转置
b = torch.tensor(np.arange(15).reshape(3,5))
print(b)
print(b.transpose(0,1))
print(b.T) #最大值
print(b.max(dim=-1)) D:\anaconda\python.exe C:/Users/liuxinyu/Desktop/pytorch_test/day1/张量的属性和方法.py
1
[[[1]]] <class 'numpy.ndarray'> <class 'torch.Tensor'>
tensor([[[ 0, 1, 2, 3, 4],
[ 5, 6, 7, 8, 9]], [[10, 11, 12, 13, 14],
[15, 16, 17, 18, 19]], [[20, 21, 22, 23, 24],
[25, 26, 27, 28, 29]]], dtype=torch.int32)
torch.Size([3, 2, 5])
torch.Size([3, 2, 5])
3
tensor([[[ 0, 1, 2, 3, 4],
[ 5, 6, 7, 8, 9],
[10, 11, 12, 13, 14]], [[15, 16, 17, 18, 19],
[20, 21, 22, 23, 24],
[25, 26, 27, 28, 29]]], dtype=torch.int32)
tensor([[ 0, 1, 2, 3, 4],
[ 5, 6, 7, 8, 9],
[10, 11, 12, 13, 14]], dtype=torch.int32)
tensor([[ 0, 5, 10],
[ 1, 6, 11],
[ 2, 7, 12],
[ 3, 8, 13],
[ 4, 9, 14]], dtype=torch.int32)
tensor([[ 0, 5, 10],
[ 1, 6, 11],
[ 2, 7, 12],
[ 3, 8, 13],
[ 4, 9, 14]], dtype=torch.int32)
torch.return_types.max(
values=tensor([ 4, 9, 14], dtype=torch.int32),
indices=tensor([4, 4, 4])) Process finished with exit code 0