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