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 optimbatch_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 xmodel = 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 = 0.0for batch_idx,data in enumerate(train_loader,0):inputs,target = datainputs,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 = 0.0def test():correct = 0total = 0with torch.no_grad():for data in test_loader:images,labels = dataimages, 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()如果喜欢内容不妨点个关注，后续会持续更新
)
)