卷积神经网络（demo)

import torch
import torchvision as tv
import torchvision.transforms as transforms
import torch.nn as nn
import torch.optim as optim
import argparse
# 定义是否使用GPU
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
# 定义网络结构
class LeNet(nn.Module):
    def __init__(self):
        super(LeNet, self).__init__()
        self.conv1 = nn.Sequential(     #input_size=(3*200*200)
            nn.Conv2d(3, 6, kernel_size=5), #padding=2保证输入输出尺寸相同
            nn.ReLU(),      #input_size=(6*196*196)
            nn.MaxPool2d(kernel_size=2, stride=2),#output_size=(6*98*98)
        )
        self.conv2 = nn.Sequential(
            nn.Conv2d(6, 16, kernel_size=5),
            nn.ReLU(),      #input_size=(16*94*94)
            nn.MaxPool2d(2, 2)  #output_size=(16*47*47)
        )
        self.fc1 = nn.Sequential(
            nn.Linear(16*47*47, 120),
            nn.ReLU()
        )
        self.fc2 = nn.Sequential(
            nn.Linear(120, 84),
            nn.ReLU()
        )
        self.fc3 = nn.Linear(84, 2)
 
    # 定义前向传播过程，输入为x
    def forward(self, x):
        x = self.conv1(x)
        x = self.conv2(x)
        # nn.Linear()的输入输出都是维度为一的值，所以要把多维度的tensor展平成一维
        x = x.view(x.size(0), -1)
        x = self.fc1(x)
        x = self.fc2(x)
        x = self.fc3(x)
        return x
#使得我们能够手动输入命令行参数，就是让风格变得和Linux命令行差不多
parser = argparse.ArgumentParser()
parser.add_argument('--outf', default='./model/', help='folder to output images and model checkpoints') #模型保存路径
parser.add_argument('--net', default='./model/net.pth', help="path to netG (to continue training)")  #模型加载路径
opt = parser.parse_args()
 
# 超参数设置
EPOCH = 8   #遍历数据集次数
BATCH_SIZE = 64      #批处理尺寸(batch_size)
LR = 0.001        #学习率
 
# 定义数据预处理方式
transform = transforms.ToTensor()
 
# 定义训练数据集
trainset = tv.datasets.ImageFolder(
    root='./train',
    
    transform=transform)
 
# 定义训练批处理数据
trainloa
der = torch.utils.data.DataLoader(
    trainset,
    batch_size=BATCH_SIZE,
    shuffle=True,
    )
 
# 定义测试数据集
testset = tv.datasets.ImageFolder(
    root='./test',
   
    transform=transform)
 
# 定义测试批处理数据
testloader = torch.utils.data.DataLoader(
    testset,
    batch_size=BATCH_SIZE,
    shuffle=False,
    )
 
# 定义损失函数loss function 和优化方式（采用SGD）
net = LeNet().to(device)
criterion = nn.CrossEntropyLoss()  # 交叉熵损失函数，通常用于多分类问题上
optimizer = optim.SGD(net.parameters(), lr=LR, momentum=0.9)
 
# 训练
if __name__ == "__main__":
 
    for epoch in range(EPOCH):
        sum_loss = 0.0
        # 数据读取
        for i, data in enumerate(trainloader):
            inputs, labels = data
            inputs, labels = inputs.to(device), labels.to(device)
 
            # 梯度清零
            optimizer.zero_grad()
 
            # forward + backward
            outputs = net(inputs)
            loss = criterion(outputs, labels)
            loss.backward()
            optimizer.step()
 
            # 每训练100个batch打印一次平均loss
            sum_loss += loss.item()
            if i % 100 == 99:
                print('[%d, %d] loss: %.03f'
                      % (epoch + 1, i + 1, sum_loss / 100))
                sum_loss = 0.0
        # 每跑完一次epoch测试一下准确率
        with torch.no_grad():
            correct = 0
            total = 0
            for data in testloader:
                images, labels = data
                images, labels = images.to(device), labels.to(device)
                outputs = net(images)
                # 取得分最高的那个类
                _, predicted = torch.max(outputs.data, 1)
                total += labels.size(0)
                correct += (predicted == labels).sum()
            print('第%d个epoch的识别准确率为：%d%%' % (epoch + 1, (100 * correct / total)))
卷积神经网络（demo)

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