dataanalyze/letnet.py

import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
from torchvision import datasets, transforms
from torch.autograd import Variable

class LeNet5(nn.Module):
    def __init__(self):
        super(LeNet5,self).__init__()
        self.layer1 = nn.Sequential(
            nn.Conv2d(3,16,5,1),
            nn.ReLU(),
            nn.MaxPool2d(2,2,0,1),
            nn.Conv2d(16,32,5,1),
            nn.ReLU(),
            nn.MaxPool2d(2,2,0,1)
        )
        self.fc = nn.Sequential(
            nn.Linear(32*5*5,120),
            nn.Linear(120,84),
            nn.Linear(84, 10)
        )
        
    def forward(self, x):
        x = self.layer1(x)
        x = x.view(-1, 800)
        x = self.fc(x)
        return x


lr = 0.01
momentum = 0.5

log_interval = 10
epochs = 10
batch_size = 64
test_batch_size = 1000
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
model = LeNet5()
optimizer = torch.optim.SGD(model.parameters(),lr=lr,momentum=momentum)

train_loader = torch.utils.data.DataLoader(
    datasets.MNIST('../data', train=True, download=True,
                   transform=transforms.Compose([
                       transforms.ToTensor(),
                       transforms.Normalize((0.1307,), (0.3081,))
                   ])),
    batch_size=batch_size, shuffle=True)

def train(epoch):  # 定义每个epoch的训练细节
    model.train()  # 设置为trainning模式
    for batch_idx, (data, target) in enumerate(train_loader):
        data = data.to(device)
        target = target.to(device)
        data, target = Variable(data), Variable(target)
        optimizer.zero_grad()  # 优化器梯度初始化为零
        output = model(data)  # 把数据输入网络并得到输出，即进行前向传播
        loss = F.cross_entropy(output, target)  # 交叉熵损失函数
        loss.backward()  # 反向传播梯度
        optimizer.step()  # 结束一次前传+反传之后，更新参数