PyTorch安装与基本使用详解
什么要学习PyTorch?
有的人总是选择,选择的人最多的框架,来作为自己的初学框架,比如Tensorflow,但是大多论文的实现都是基于PyTorch的,如果我们要深入论文的细节,就必须选择学习入门PyTorch
安装PyTorch
一行命令即可官网
pipinstalltorch===1.6.0torchvision===0.7.0-https://download.pytorch.org/whl/torch_stable.html
时间较久,耐心等待
测试自己是否安装成功
运行命令测试
importtorch x=torch.rand(5,3) print(x)
输出
tensor([[0.5096,0.1209,0.7721],
[0.9486,0.8676,0.2157],
[0.0586,0.3467,0.5015],
[0.9470,0.5654,0.9317],
[0.2127,0.2386,0.0629]])
开始学习PyTorch
不初始化的创建张量
importtorch x=torch.empty([5,5]) print(x)
输出
tensor([[0.,0.,0.],
[0.,0.,0.],
[0.,0.,0.],
[0.,0.,0.],
[0.,0.,0.]])
随机创建一个0-1的张量
importtorch x=torch.rand(5,5) print(x)
输出
tensor([[0.3369,0.5339,0.8419,0.6857,0.6241],
[0.4991,0.1691,0.8356,0.4574,0.0395],
[0.9714,0.2975,0.9322,0.5213,0.8509],
[0.3037,0.8690,0.3481,0.2538,0.9513],
[0.0156,0.9516,0.3674,0.1831,0.6466]])
创建全为0的张量
importtorch x=torch.zeros(5,5,dtype=torch.float32) print(x)
创建的时候可以通过dtype指定数据类型
输出
tensor([[0.,0.,0.,0.,0.],
[0.,0.,0.,0.,0.],
[0.,0.,0.,0.,0.],
[0.,0.,0.,0.,0.],
[0.,0.,0.,0.,0.]])
使用数据来直接创建张量
importtorch x=torch.zeros([5,5],dtype=torch.float32) print(x)
输出
tensor([5.,5.])
使用原有tensor创建新的tensor
importtorch x=torch.tensor([5,5],dtype=torch.float32) x=x.new_zeros(5,3) y=torch.rand_like(x) print(x) print(y)
输出
tensor([[0.,0.,0.],
[0.,0.,0.],
[0.,0.,0.],
[0.,0.,0.],
[0.,0.,0.]])
tensor([[0.5552,0.3333,0.0426],
[0.3861,0.3945,0.6658],
[0.6978,0.3508,0.4813],
[0.8193,0.2274,0.8384],
[0.9360,0.9226,0.1453]])
观察tensor的维度信息
x=torch.rand(3,3) x.size()
输出
torch.Size([3,3])
一些简单的运算
x=torch.tensor([1]) y=torch.tensor([3]) ''' 方式1 ''' z=x+y ''' 方式2 ''' z=torch.add(x,y) ''' 方式3 ''' result=torch.empty(1) #不初始化数据 torch.add(x,y,out=result) #将结果返回到result中 ''' 方式4 ''' x.add_(y)
输出
tensor([4])
索引操作
x=torch.rand(5,5) x[:,:] x[1,:] x[:,1] x[1,1]
分别输出
tensor([[0.4012,0.2604,0.1720,0.0996,0.7806],
[0.8734,0.9087,0.4828,0.3543,0.2375],
[0.0924,0.9040,0.4408,0.9758,0.2250],
[0.7179,0.7244,0.6165,0.1142,0.7363],
[0.8504,0.0391,0.0753,0.4530,0.7372]])
tensor([0.8734,0.9087,0.4828,0.3543,0.2375])
tensor([0.2604,0.9087,0.9040,0.7244,0.0391])
tensor(0.9087)
维度变换
x=torch.rand(4,4) x.view(16) x.view(8,2) x.view(-1,8)
分别输出
tensor([0.9277,0.9547,0.9487,0.9841,0.4114,0.1693,0.8691,0.3954,0.4679,
0.7914,0.7456,0.0522,0.0043,0.2097,0.5932,0.9797])
tensor([[0.9277,0.9547],
[0.9487,0.9841],
[0.4114,0.1693],
[0.8691,0.3954],
[0.4679,0.7914],
[0.7456,0.0522],
[0.0043,0.2097],
[0.5932,0.9797]])
tensor([[0.9277,0.9547,0.9487,0.9841,0.4114,0.1693,0.8691,0.3954],
[0.4679,0.7914,0.7456,0.0522,0.0043,0.2097,0.5932,0.9797]])
注意:必须维度变换数据的数量必须保持一致
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