卷積神經網路(Convolutional Neural Network, CNN)是深度學習技術中極具代表的網路結構之一，在影象處理領域取得了很大的成功，在國際標準的ImageNet資料集上，許多成功的模型都是基於CNN的。

卷積神經網路CNN的結構一般包含這幾個層：

1. 輸入層：用於資料的輸入
2. 卷積層：使用卷積核進行特徵提取和特徵對映
3. 激勵層：由於卷積也是一種線性運算，因此需要增加非線性對映
4. 池化層：進行下采樣，對特徵圖稀疏處理，減少資料運算量。
5. 全連線層：通常在CNN的尾部進行重新擬合，減少特徵資訊的損失
6. 輸出層：用於輸出結果

用pytorch0.4 做的cnn網路做的minist 分類，程式碼如下：

 1 import torch
 2 import torch.nn as nn
 3 import torch.nn.functional as F
 4 import torch.optim as optim
 5 from torchvision import datasets, transforms
 6 from torch.autograd import Variable
 7 
 8 # Training settings
 9 batch_size = 64
10 
11 # MNIST Dataset
12 train_dataset = datasets.MNIST(root='
 
./data/',train=True,transform=transforms.ToTensor(),download=True)
13 test_dataset = datasets.MNIST(root='./data/',train=False,transform=transforms.ToTensor())
14 
15 # Data Loader (Input Pipeline)
16 train_loader = torch.utils.data.DataLoader(dataset=train_dataset,batch_size=batch_size,shuffle=True)
17
 
 test_loader = torch.utils.data.DataLoader(dataset=test_dataset,batch_size=batch_size,shuffle=False)
18 
19 class Net(nn.Module):
20     def __init__(self):
21         super(Net, self).__init__()
22         # 輸入1通道，輸出10通道，kernel 5*5
23         self.conv1 = nn.Conv2d(1, 10, kernel_size=5) # 定義conv1函式的是影象卷積函式：輸入為影象（1個頻道，即灰度圖）,輸出為 10張特徵圖, 卷積核為5x5正方形
24         self.conv2 = nn.Conv2d(10, 20, kernel_size=5) # # 定義conv2函式的是影象卷積函式：輸入為10張特徵圖,輸出為20張特徵圖, 卷積核為5x5正方形
25         self.mp = nn.MaxPool2d(2)
26         # fully connect
27         self.fc = nn.Linear(320, 10)
28 
29     def forward(self, x):
30         # in_size = 64
31         in_size = x.size(0)  # one batch
32         # x: 64*10*12*12
33         x = F.relu(self.mp(self.conv1(x)))
34         # x: 64*20*4*4
35         x = F.relu(self.mp(self.conv2(x)))
36         # x: 64*320
37         x = x.view(in_size, -1)  # flatten the tensor
38         # x: 64*10
39         x = self.fc(x)
40         return F.log_softmax(x,dim=0)
41 
42 
43 model = Net()
44 optimizer = optim.SGD(model.parameters(), lr=0.01, momentum=0.5)
45 
46 def train(epoch):
47     for batch_idx, (data, target) in enumerate(train_loader):
48         data, target = Variable(data), Variable(target)
49         optimizer.zero_grad()
50         output = model(data)
51         loss = F.nll_loss(output, target)
52         loss.backward()
53         optimizer.step()
54         if batch_idx % 200 == 0:
55             print('Train Epoch: {} [{}/{} ({:.0f}%)]\tLoss: {:.6f}'.format(
56                 epoch, batch_idx * len(data), len(train_loader.dataset),
57                        100. * batch_idx / len(train_loader), loss.item()))
58 
59 
60 def test():
61     test_loss = 0
62     correct = 0
63     for data, target in test_loader:
64         data, target = Variable(data), Variable(target)
65         output = model(data)
66         # sum up batch loss
67         #test_loss += F.nll_loss(output, target, size_average=False).item()
68         test_loss += F.nll_loss(output, target, reduction = 'sum').item()
69         # get the index of the max log-probability
70         pred = output.data.max(1, keepdim=True)[1]
71         correct += pred.eq(target.data.view_as(pred)).cpu().sum()
72 
73     test_loss /= len(test_loader.dataset)
74     print('\nTest set: Average loss: {:.4f}, Accuracy: {}/{} ({:.0f}%)\n'.format(
75         test_loss, correct, len(test_loader.dataset),
76         100. * correct / len(test_loader.dataset)))
77 
78 
79 if __name__=="__main__":
80     for epoch in range(1, 4):
81       train(epoch)
82       test()

 執行效果如下：

Train Epoch: 1 [0/60000 (0%)]    Loss: 4.163342
Train Epoch: 1 [12800/60000 (21%)]    Loss: 2.689871
Train Epoch: 1 [25600/60000 (43%)]    Loss: 2.553686
Train Epoch: 1 [38400/60000 (64%)]    Loss: 2.376630
Train Epoch: 1 [51200/60000 (85%)]    Loss: 2.321894

Test set: Average loss: 2.2703, Accuracy: 9490/10000 (94%)

Train Epoch: 2 [0/60000 (0%)]    Loss: 2.321601
Train Epoch: 2 [12800/60000 (21%)]    Loss: 2.293680
Train Epoch: 2 [25600/60000 (43%)]    Loss: 2.377935
Train Epoch: 2 [38400/60000 (64%)]    Loss: 2.150829
Train Epoch: 2 [51200/60000 (85%)]    Loss: 2.201805

Test set: Average loss: 2.1848, Accuracy: 9658/10000 (96%)

Train Epoch: 3 [0/60000 (0%)]    Loss: 2.238524
Train Epoch: 3 [12800/60000 (21%)]    Loss: 2.224833
Train Epoch: 3 [25600/60000 (43%)]    Loss: 2.240626
Train Epoch: 3 [38400/60000 (64%)]    Loss: 2.217183
Train Epoch: 3 [51200/60000 (85%)]    Loss: 2.357141

Test set: Average loss: 2.1426, Accuracy: 9723/10000 (97%)