Tensor的基本使用

Tensor的基本使用

1.基本概念

标量：就是一个数，是0维的，只有大小，没有方向

向量：是1*n的一列数，是1维的，有大小，也有方向

张量：是n*n的一堆数，是2维的，n个向量合并而成

2.a.size(),a.shape(),a.numel(),a.dim()的区别

a.size()：输出a的某一维度中元素的个数，若未指定维度，则计算所有元素的个数

a.shape()：输出a数组各维度的长度信息,返回是元组类型。

a.numel()：输出a占用内存的数量

a.dim()：输出a的维数

tensor的创建：

import torch
import numpy as np

if __name__ == '__main__':
    # 随机正太分布
    a=torch.randn(2,3)
    print("a:",a)
    print("a.size():",a.size())
    print("a.size(0):",a.size(0))
    print("a.size(1):",a.size(1))
    print("a.shape[0]:",a.shape[0])
    print("a.shape[1]:",a.shape[1])
    print("a.shape:",a.shape)
    # 将a.shape转换成list
    print("list(a.shape):",list(a.shape))
    # 输出a占用内存的数量=2*3
    print("a.numel():",a.numel())
    # 输出a的维数
    print("a.dim():",a.dim())
    print()


    # 0~1随机均匀分布
    b=torch.rand(2,3,4)
    print("b:",b)
    print("b.size():",b.size())
    print("b.size(0):",b.size(0))
    print("b.size(1):",b.size(1))
    print("b.shape[0]:",b.shape[0])
    print("b.shape[1]:",b.shape[1])
    print("b.shape:",b.shape)
    print("list(b.shape):",list(b.shape))
    # 输出b占用内存的数量=2*3*4
    print("b.numel():",b.numel())
    print("b.dim():",b.dim())
    print()

运行结果：

a: tensor([[-0.2106, -2.1292, -0.8221],
        [-1.5805,  0.2592, -1.1203]])
a.size(): torch.Size([2, 3])
a.size(0): 2
a.size(1): 3
a.shape[0]: 2
a.shape[1]: 3
a.shape: torch.Size([2, 3])
list(a.shape): [2, 3]
a.numel(): 6
a.dim(): 2

b: tensor([[[0.8126, 0.8908, 0.3507, 0.1554],
         [0.8679, 0.5295, 0.5461, 0.5021],
         [0.2570, 0.2250, 0.6310, 0.0662]],

        [[0.1139, 0.9552, 0.5847, 0.5421],
         [0.3589, 0.0090, 0.0324, 0.6984],
         [0.9562, 0.4533, 0.4296, 0.4052]]])
b.size(): torch.Size([2, 3, 4])
b.size(0): 2
b.size(1): 3
b.shape[0]: 2
b.shape[1]: 3
b.shape: torch.Size([2, 3, 4])
list(b.shape): [2, 3, 4]
b.numel(): 24
b.dim(): 3

索引与切片的基本使用

import torch

if __name__ == '__main__':
    # 01正太分布生成一个矩阵
    a=10*torch.rand(3,3)
    print(a,'\n')

    # 按照某个矩阵再生成一个矩阵
    a1=torch.rand_like(a)
    print('a1=torch.rand_like(a):\n',a1,'\n')

    # 规定区间随机整数矩阵
    b=torch.randint(1,6,[3,3])
    print('torch.randint(1,6,[3,3]):\n',b,'\n')

    # 生成一个两行三列的矩阵，并把所有值赋值为3.92
    c=torch.full((2, 3), 3.92)
    print('torch.full((2, 3), 3.92):\n',c,'\n')

    # 步长为2，按序生成0~10之间的数字
    d=torch.arange(0,10,step=2)
    print('d=torch.arange(0,10,step=2):\n',d,'\n')

    # 均匀生成某段数据
    e=torch.linspace(0,10,steps=10)
    print('torch.linspace(0,10,steps=10):\n',e,'\n')
    e1=torch.linspace(0,10,steps=11)
    print('e1=torch.linspace(0,10,steps=11):\n',e1,'\n')

    # 值全为1矩阵
    f=torch.ones(3,3)
    print('torch.ones(3,3):\n',f,'\n')
    # 值全为零矩阵
    f1=torch.zeros(3,3)
    print('torch.zeros(3,3):\n',f1,'\n')
    # 单位矩阵
    f2=torch.eye(3,3)
    print('torch.eye(3,3):\n',f2,'\n')

    g=torch.rand(4,3,28,28)
    print('shape的基本使用:')
    print(g[0].shape)
    print(g[0,0].shape)
    print(g[0,0,2,4])

    print('\ntensor的切片使用:')
    # 取前两张图片
    print(g[:2].shape)
    # 取第二张图片向后及第一个通道向后
    print(g[2:,1:].shape)
    # 行：隔七个采一个样，列：隔14个采一个样，(start:stop:step)
    print(g[0,0,0:28:7,::14])


    h=torch.randn(3,4)
    print('\n',h)
    # 矩阵中值大于0.5的 赋值为ture
    mask=h.__ge__(0.5)
    print(mask)
    print(torch.masked_select(h,mask))

运行结果：

tensor([[6.6247, 1.7639, 2.3681],
        [1.4683, 7.0583, 6.3519],
        [2.0854, 6.2536, 0.0829]])
        
a1=torch.rand_like(a):
 tensor([[0.2688, 0.0892, 0.7759],
        [0.4124, 0.1816, 0.1043],
        [0.8010, 0.4711, 0.5239]]) 
        
torch.randint(1,6,[3,3]):
 tensor([[5, 4, 3],
        [1, 1, 3],
        [2, 1, 3]]) 
        
torch.full((2, 3), 3.92):
 tensor([[3.9200, 3.9200, 3.9200],
        [3.9200, 3.9200, 3.9200]]) 
        
d=torch.arange(0,10,step=2):
 tensor([0, 2, 4, 6, 8]) 
 
torch.linspace(0,10,steps=10):
 tensor([ 0.0000,  1.1111,  2.2222,  3.3333,  4.4444,  5.5556,  6.6667,  7.7778,
         8.8889, 10.0000]) 
         
e1=torch.linspace(0,10,steps=11):
 tensor([ 0.,  1.,  2.,  3.,  4.,  5.,  6.,  7.,  8.,  9., 10.]) 
 
torch.ones(3,3):
 tensor([[1., 1., 1.],
        [1., 1., 1.],
        [1., 1., 1.]]) 
        
torch.zeros(3,3):
 tensor([[0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.]]) 
        
torch.eye(3,3):
 tensor([[1., 0., 0.],
        [0., 1., 0.],
        [0., 0., 1.]]) 
        
shape的基本使用:
torch.Size([3, 28, 28])
torch.Size([28, 28])
tensor(0.7568)

tensor的切片使用:
torch.Size([2, 3, 28, 28])
torch.Size([2, 2, 28, 28])
tensor([[0.4571, 0.3198],
        [0.6540, 0.3359],
        [0.2601, 0.8069],
        [0.9713, 0.6876]])
 tensor([[-2.4096,  1.1243, -1.0314, -1.4685],
        [-2.5054,  0.7131, -0.0376, -0.2110],
        [ 1.8922,  1.8989,  0.0459, -1.6457]])
tensor([[False,  True, False, False],
        [False,  True, False, False],
        [ True,  True, False, False]])
tensor([1.1243, 0.7131, 1.8922, 1.8989])