机器学习基础（2）——基于pytorch的LeNet模型

1、前言

根据上一篇文章中所配置的数据集开始一些简单基础的神经网络模型搭建，首先是LeNet模型，LeNet-5出自1998年Y Lecun的论文《Gradient-Based Learning Applied to Document Recognition》，论文链接如下：

Gradient-based learning applied to document recognition - 百度学术

 2、模型复现

（1）模型搭建

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


class LeNet(nn.Module):
    def __init__(self):
        super(LeNet, self).__init__()
        self.conv1 = nn.Conv2d(3, 16, 5)
        self.pool1 = nn.MaxPool2d(2, 2)
        self.conv2 = nn.Conv2d(16, 32, 5)
        self.pool2 = nn.MaxPool2d(2, 2)
        self.fc1 = nn.Linear(32*5*5, 120)
        self.fc2 = nn.Linear(120, 84)
        self.fc3 = nn.Linear(84, 10)

    def forward(self, x):
        x = F.relu(self.conv1(x))    
        x = self.pool1(x)           
        x = F.relu(self.conv2(x))    
        x = self.pool2(x)            
        x = x.view(-1, 32*5*5)      
        x = F.relu(self.fc1(x))      
        x = F.relu(self.fc2(x))      
        x = self.fc3(x)             
        return x

（2）训练部分

此处采用torchvision.datasets中CIFAR10的数据集，有可能因为境外源的原因下载失败或太慢，此处给出百度网盘分享链接：

链接：https://pan.baidu.com/s/1-wk3iJ2bvki-Q0w2dPrpGQ 
提取码：abc7 
--来自百度网盘超级会员V4的分享

下载后放置路径如下图所示

代码如下：

import torch
import torchvision
import torch.nn as nn
import torch.optim as optim
import torchvision.transforms as transforms


def main():
    transform = transforms.Compose(
        [transforms.ToTensor(),
         transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))])

    train_set = torchvision.datasets.CIFAR10(root='./data', train=True,
                                             download=True, transform=transform)
    train_loader = torch.utils.data.DataLoader(train_set, batch_size=36,
                                               shuffle=True, num_workers=0)


    val_set = torchvision.datasets.CIFAR10(root='./data', train=False,
                                           download=True, transform=transform)
    val_loader = torch.utils.data.DataLoader(val_set, batch_size=5000,
                                             shuffle=False, num_workers=0)
    val_data_iter = iter(val_loader)
    val_image, val_label = next(val_data_iter)
    
    net = LeNet()
    loss_function = nn.CrossEntropyLoss()
    optimizer = optim.Adam(net.parameters(), lr=0.001)

    for epoch in range(5):  # loop over the dataset multiple times

        running_loss = 0.0
        for step, data in enumerate(train_loader, start=0):

            inputs, labels = data

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

            running_loss += loss.item()
            if step % 500 == 499:    # print every 500 mini-batches
                with torch.no_grad():
                    outputs = net(val_image)  # [batch, 10]
                    predict_y = torch.max(outputs, dim=1)[1]
                    accuracy = torch.eq(predict_y, val_label).sum().item() / val_label.size(0)

                    print('[%d, %5d] train_loss: %.3f  test_accuracy: %.3f' %
                          (epoch + 1, step + 1, running_loss / 500, accuracy))
                    running_loss = 0.0

    print('Finished Training')

    save_path = './Lenet.pth'
    torch.save(net.state_dict(), save_path)


if __name__ == '__main__':
    main()

训练效果：