• Pytorch入门学习写一个简单的分类程序

    参考视频:https://www.bilibili.com/video/BV1AP4y167bX/?spm_id_from=333.999.0.0&vd_source=98d31d5c9db8c0021988f2c2c25a9620

    目录

    网络初学

    在这里插入图片描述

    import torch
import torch.nn as nn
import torch.nn.functional as F

class Net(nn.Module):
    def __init__(self):
        super(Net, self).__init__()
        self.conv1=nn.Conv2d(1,6,5)
        self.conv2=nn.Conv2d(6,16,5)
        self.fc1=nn.Linear(16*5*5,120)
        self.fc2=nn.Linear(120,84)
        self.fc3=nn.Linear(84,10)
    def forward(self,x):
        #x : tensor[batch,channel,H,W]
        #假设这里的x的维度是(1,1,32,32)
        x=self.conv1(x)#x的维度是(1,6,28,28)
        x=F.relu(x)#x的维度是(1,6,28,28)
        x=F.max_pool2d(x,(2,2))#x的维度是(1,6,14,14)
        x=F.max_pool2d(F.relu(self.conv2(x)),2)#(1,6,14,14)->(1,16,10,10)->(1,16,5,5)
        x=x.view(-1,x.size()[1:].numel())#(1,16,5,5)-->(1,16*5*5)
        x=F.relu(self.fc1(x))#(1,16*5*5)->(1,120)
        x=F.relu(self.fc2(x))#(1,120)->(1,84)
        x=self.fc3(x)#(1,84)->(1,10)
        return x

net=Net()
print(net)
a=torch.randn(1,1,32,32)
b=net(a)
print(b.size())

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    简单的分类程序

    环境配置：

    conda create -n szh python=3.8
conda install pytorch==1.6.0 cudatoolkit=10.1 -c https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/pytorch/
conda install torchvision -c pytorch
pip install matplotlib

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    数据集：
    用的是CIFAR-10数据集：
    数据集介绍

    项目开发的主要流程是：

    • 用torch的torchvision加载初始化数据集。
    • 定义卷积神经网络。
    • 定义损失函数。
    • 根据训练数据训练网络。
    • 测试数据上测试网络。

    项目结构：
    在这里插入图片描述
    data文件是运行后自动生成的用来保存数据集。
    dataset.py

    """
Dataset
定义好数据的格式和数据变换形式
Dataloader
用iterative的方式不断读入批次数据
"""
import torch
import torchvision
import torchvision.transforms as transforms
import matplotlib.pyplot as plt
import numpy as np
transform=transforms.Compose(
    [
        transforms.ToTensor(),
        transforms.Normalize((0.5,0.5,0.5),(0.5,0.5,0.5))
    ]
)

trainset=torchvision.datasets.CIFAR10(root='./data',train=True,download=True,transform=transform)
testset=torchvision.datasets.CIFAR10(root='./data',train=False,download=True,transform=transform)

trainloader=torch.utils.data.DataLoader(
    trainset,batch_size=4,shuffle=True,num_workers=0
)

testloader=torch.utils.data.DataLoader(
   testset,batch_size=4,shuffle=False,num_workers=0
)
classes=('airplane','automobile','bird','cat','deer','dog','frog','horse','ship','truck')

def imshow(img):
    img=img/2 +0.5#unnormalize
    npimg=img.numpy()
    plt.imshow(np.transpose(npimg,(1,2,0)))
    plt.show()
    plt.savefig('1.jpg')
    plt.close()

if __name__ == '__main__':#测试数据集是否正确的加载成功
    dataiter = iter(trainloader)
    images, labels = dataiter.next()
    imshow(torchvision.utils.make_grid(images))
    print(labels)
    print(labels[0], classes[labels[0]])

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    model.py

    import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim


class Net(nn.Module):#分类的主干网络
    def __init__(self):
        super(Net, self).__init__()
        self.conv1=nn.Conv2d(3,6,5)
        self.conv2=nn.Conv2d(6,16,5)
        self.fc1=nn.Linear(16*5*5,120)
        self.fc2=nn.Linear(120,84)
        self.fc3=nn.Linear(84,10)
    def forward(self,x):
        #x : tensor[batch,channel,H,W]
        #假设这里的x的维度是(1,1,32,32)
        x=self.conv1(x)#x的维度是(1,6,28,28)
        x=F.relu(x)#x的维度是(1,6,28,28)
        x=F.max_pool2d(x,(2,2))#x的维度是(1,6,14,14)
        x=F.max_pool2d(F.relu(self.conv2(x)),2)#(1,6,14,14)->(1,16,10,10)->(1,16,5,5)
        x=x.view(-1,x.size()[1:].numel())#(1,16,5,5)-->(1,16*5*5)
        x=F.relu(self.fc1(x))#(1,16*5*5)->(1,120)
        x=F.relu(self.fc2(x))#(1,120)->(1,84)
        x=self.fc3(x)#(1,84)->(1,10)
        return x

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    train.py

    import torch
import torchvision
import torchvision.transforms as transforms
import matplotlib.pyplot as plt
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
from model import Net
transform=transforms.Compose(
    [
        transforms.ToTensor(),
        transforms.Normalize((0.5,0.5,0.5),(0.5,0.5,0.5))
    ]
)

trainset=torchvision.datasets.CIFAR10(root='./data',train=True,download=True,transform=transform)
testset=torchvision.datasets.CIFAR10(root='./data',train=False,download=True,transform=transform)

trainloader=torch.utils.data.DataLoader(
    trainset,batch_size=4,shuffle=True,num_workers=0
)

testloader=torch.utils.data.DataLoader(
   testset,batch_size=4,shuffle=False,num_workers=0
)

net=Net()
criterion=nn.CrossEntropyLoss()
optimizer=optim.SGD(net.parameters(),lr=0.001,momentum=0.9)

for epoch in range(2):
    running_loss=0.0
    for i,data in enumerate(trainloader,0):
        inputs,labels=data
        optimizer.zero_grad()

        outputs=net(inputs)
        loss=criterion(outputs,labels)
        loss.backward()
        optimizer.step()

        running_loss+=loss.item()
        if i%2000 ==1999:
            print("[%d,%5d] loss:%.3f" % (epoch+1,i+1,running_loss/2000))
            running_loss=0.0

print("Finish")
PATH='./cifar_net.pth'
torch.save(net.state_dict(),PATH)

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    test.py

    import torch
import torchvision
import torchvision.transforms as transforms
import matplotlib.pyplot as plt
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
from model import Net
transform=transforms.Compose(
    [
        transforms.ToTensor(),
        transforms.Normalize((0.5,0.5,0.5),(0.5,0.5,0.5))
    ]
)

trainset=torchvision.datasets.CIFAR10(root='./data',train=True,download=True,transform=transform)
testset=torchvision.datasets.CIFAR10(root='./data',train=False,download=True,transform=transform)

trainloader=torch.utils.data.DataLoader(
    trainset,batch_size=4,shuffle=True,num_workers=0
)

testloader=torch.utils.data.DataLoader(
   testset,batch_size=4,shuffle=False,num_workers=0
)

net=Net()
PATH='./cifar_net.pth'
net.load_state_dict(torch.load(PATH))

correct=0
total=0
with torch.no_grad():
    for data in testloader:
        images,labels=data
        outputs=net(images)
        _,predicted=torch.max(outputs,1)
        total+=labels.size(0)
        correct+=(predicted==labels).sum().item()
correctGailv=100*(correct/total)
print(correctGailv)

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  • 原文地址：https://blog.csdn.net/bettle_king/article/details/136358279