GoogleNet的简易实现

这里使用GooleNet对MNIST手写数据集进行分类，最后的效果达到了在测试集98%的准确率。这里关于该网络的细节可以在网络上搜索到，相关原理也可以搜索到，这里仅展示网络的代码实现，这里是基于pytorch实现的，详细的代码如下：

import torch
from torchvision import transforms
from torchvision import datasets
from torch.utils.data import DataLoader
import torch.nn.functional as F
import torch.optim as optim

batch_size = 64
transform = transforms.Compose([
    transforms.ToTensor(),
    transforms.Normalize((0.1307,),(0.3081,))
])
train_dataset = datasets.MNIST(root=r"C:\Users\pszpszpsz\Desktop\dataset\mnist\MNIST\raw",
                            train=True,
                            download=True,
                            transform=transform)
train_loader = DataLoader(train_dataset,
                          shuffle=True,
                          batch_size=batch_size)
test_dataset = datasets.MNIST(root=r"C:\Users\pszpszpsz\Desktop\dataset\mnist\MNIST\raw",
                            train=False,
                            download=True,
                            transform=transform)
test_loader = DataLoader(test_dataset,
                          shuffle=False,
                          batch_size=batch_size)

class InceptionA(torch.nn.Module):
    def __init__(self,in_channels):
        super(InceptionA,self).__init__()
        self.branch1x1 = torch.nn.Conv2d(in_channels,16,kernel_size=1)

        self.branch5x5_1 = torch.nn.Conv2d(in_channels,16,kernel_size=1)
        self.branch5x5_2 = torch.nn.Conv2d(16, 24, kernel_size=5,padding=2)

        self.branch3x3_1 = torch.nn.Conv2d(in_channels, 16, kernel_size=1)
        self.branch3x3_2 = torch.nn.Conv2d(16, 24, kernel_size=3,padding=1)
        self.branch3x3_3 = torch.nn.Conv2d(24, 24, kernel_size=3, padding=1)

        self.branch_pool = torch.nn.Conv2d(in_channels, 24, kernel_size=1)

    def forward(self,x):
        branch1x1 = self.branch1x1(x)

        branch5x5 = self.branch5x5_1(x)
        branch5x5 = self.branch5x5_2(branch5x5)

        branch3x3 = self.branch3x3_1(x)
        branch3x3 = self.branch3x3_2(branch3x3)
        branch3x3 = self.branch3x3_3(branch3x3)

        branch_pool = F.avg_pool2d(x,kernel_size=3,stride=1,padding=1)
        branch_pool = self.branch_pool(branch_pool)

        outputs = [branch1x1,branch5x5,branch3x3,branch_pool]
        return torch.cat(outputs,dim=1)

class Net(torch.nn.Module):
    def __init__(self):
        super(Net,self).__init__()
        self.conv1 = torch.nn.Conv2d(1,10,kernel_size=5)
        self.conv2 = torch.nn.Conv2d(88,20,kernel_size=5)

        self.incep1 = InceptionA(in_channels=10)
        self.incep2 = InceptionA(in_channels=20)

        self.mp = torch.nn.MaxPool2d(2)
        self.fc = torch.nn.Linear(1408,10)

    def forward(self,x):
        in_size = x.size(0)
        x = F.relu(self.mp(self.conv1(x)))
        x = self.incep1(x)
        x = F.relu(self.mp(self.conv2(x)))
        x = self.incep2(x)
        x = x.view(in_size,-1)
        x = self.fc(x)
        return x

model = Net()
device = torch.device("cude:0"if torch.cuda.is_available() else "cpu")
model.to(device)
criterion = torch.nn.CrossEntropyLoss()
optimizer = optim.SGD(model.parameters(),lr=0.01,momentum=0.5)

def train(epoch):
    running_loss = 0.0
    for batch_idx,data in enumerate(train_loader,0):
        inputs,target = data
        inputs,target = inputs.to(device),target.to(device)
        optimizer.zero_grad()

        outputs = model(inputs)
        loss = criterion(outputs,target)
        loss.backward()
        optimizer.step()

        running_loss += loss.item()
        if batch_idx % 300 == 299:
            print('[%d,%5d] loss:%.3f'%(epoch + 1,batch_idx + 1,running_loss / 300))
            running_loss = 0.0

def test():
    correct = 0
    total = 0
    with torch.no_grad():
        for data in test_loader:
            images,labels = data
            images, labels = images.to(device), labels.to(device)
            outputs = model(images)
            _,predicted = torch.max(outputs.data,dim=1)
            total += labels.size(0)
            correct += (predicted == labels).sum().item()
    print('Accuracy on test set: %d %%' %(100 * correct / total))

if __name__ == '__main__':
    for epoch in range(10):
        train(epoch)
        test()

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