卷积神经网络(Convolutional Neural Network, CNN)是深度学习技术中极具代表的网络结构之一，在图像处理领域取得了很大的成功，在国际标准的ImageNet数据集上，许多成功的模型都是基于CNN的。
卷积神经网络CNN的结构一般包含这几个层：
- 输入层：用于数据的输入
- 卷积层：使用卷积核进行特征提取和特征映射
- 激励层：由于卷积也是一种线性运算，因此需要增加非线性映射
- 池化层：进行下采样，对特征图稀疏处理，减少数据运算量。
- 全连接层：通常在CNN的尾部进行重新拟合，减少特征信息的损失
- 输出层：用于输出结果
用pytorch0.4 做的cnn网络做的minist 分类，代码如下：
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 # Training settings
batch_size = 64 # MNIST Dataset
train_dataset = datasets.MNIST(root='./data/',train=True,transform=transforms.ToTensor(),download=True)
test_dataset = datasets.MNIST(root='./data/',train=False,transform=transforms.ToTensor()) # Data Loader (Input Pipeline)
train_loader = torch.utils.data.DataLoader(dataset=train_dataset,batch_size=batch_size,shuffle=True)
test_loader = torch.utils.data.DataLoader(dataset=test_dataset,batch_size=batch_size,shuffle=False) class Net(nn.Module):
def __init__(self):
super(Net, self).__init__()
# 输入1通道，输出10通道，kernel 5*5
self.conv1 = nn.Conv2d(1, 10, kernel_size=5) # 定义conv1函数的是图像卷积函数：输入为图像（1个频道，即灰度图）,输出为 10张特征图, 卷积核为5x5正方形
self.conv2 = nn.Conv2d(10, 20, kernel_size=5) # # 定义conv2函数的是图像卷积函数：输入为10张特征图,输出为20张特征图, 卷积核为5x5正方形
self.mp = nn.MaxPool2d(2)
# fully connect
self.fc = nn.Linear(320, 10) def forward(self, x):
# in_size = 64
in_size = x.size(0) # one batch
# x: 64*10*12*12
x = F.relu(self.mp(self.conv1(x)))
# x: 64*20*4*4
x = F.relu(self.mp(self.conv2(x)))
# x: 64*320
x = x.view(in_size, -1) # flatten the tensor
# x: 64*10
x = self.fc(x)
return F.log_softmax(x,dim=0) model = Net()
optimizer = optim.SGD(model.parameters(), lr=0.01, momentum=0.5) def train(epoch):
for batch_idx, (data, target) in enumerate(train_loader):
data, target = Variable(data), Variable(target)
optimizer.zero_grad()
output = model(data)
loss = F.nll_loss(output, target)
loss.backward()
optimizer.step()
if batch_idx % 200 == 0:
print('Train Epoch: {} [{}/{} ({:.0f}%)]\tLoss: {:.6f}'.format(
epoch, batch_idx * len(data), len(train_loader.dataset),
100. * batch_idx / len(train_loader), loss.item())) def test():
test_loss = 0
correct = 0
for data, target in test_loader:
data, target = Variable(data), Variable(target)
output = model(data)
# sum up batch loss
#test_loss += F.nll_loss(output, target, size_average=False).item()
test_loss += F.nll_loss(output, target, reduction = 'sum').item()
# get the index of the max log-probability
pred = output.data.max(1, keepdim=True)[1]
correct += pred.eq(target.data.view_as(pred)).cpu().sum() test_loss /= len(test_loader.dataset)
print('\nTest set: Average loss: {:.4f}, Accuracy: {}/{} ({:.0f}%)\n'.format(
test_loss, correct, len(test_loader.dataset),
100. * correct / len(test_loader.dataset))) if __name__=="__main__":
for epoch in range(1, 4):
train(epoch)
test()
运行效果如下：
Train Epoch: [/ (%)] Loss: 4.163342
Train Epoch: [/ (%)] Loss: 2.689871
Train Epoch: [/ (%)] Loss: 2.553686
Train Epoch: [/ (%)] Loss: 2.376630
Train Epoch: [/ (%)] Loss: 2.321894 Test set: Average loss: 2.2703, Accuracy: / (%) Train Epoch: [/ (%)] Loss: 2.321601
Train Epoch: [/ (%)] Loss: 2.293680
Train Epoch: [/ (%)] Loss: 2.377935
Train Epoch: [/ (%)] Loss: 2.150829
Train Epoch: [/ (%)] Loss: 2.201805 Test set: Average loss: 2.1848, Accuracy: / (%) Train Epoch: [/ (%)] Loss: 2.238524
Train Epoch: [/ (%)] Loss: 2.224833
Train Epoch: [/ (%)] Loss: 2.240626
Train Epoch: [/ (%)] Loss: 2.217183
Train Epoch: [/ (%)] Loss: 2.357141 Test set: Average loss: 2.1426, Accuracy: / (%)