构建卷积神经网络

• 卷积网络中的输入和层与传统神经网络有些区别，需重新设计，训练模块基本一致

1.首先读取数据

 - 分别构建训练集和测试集（验证集）
- DataLoader来迭代取数据

# 定义超参数 
input_size = 28  #图像的总尺寸28*28
num_classes = 10  #标签的种类数
num_epochs = 3  #训练的总循环周期
batch_size = 64  #一个撮（批次）的大小，64张图片# 训练集
train_dataset = datasets.MNIST(root='./data',  train=True,   transform=transforms.ToTensor(),  download=True) # 测试集
test_dataset = datasets.MNIST(root='./data', train=False, transform=transforms.ToTensor())# 构建batch数据
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=True)

2.卷积网络模块构建

- 一般卷积层，relu层，池化层可以写成一个套餐
- 注意卷积最后结果还是一个特征图，需要把图转换成向量才能做分类或者回归任务

class CNN(nn.Module):def __init__(self):super(CNN, self).__init__()self.conv1 = nn.Sequential(         # 输入大小 (1, 28, 28)nn.Conv2d(in_channels=1,              # 灰度图out_channels=16,            # 要得到几多少个特征图kernel_size=5,              # 卷积核大小stride=1,                   # 步长padding=2,                  # 如果希望卷积后大小跟原来一样，需要设置padding=(kernel_size-1)/2 if stride=1),                              # 输出的特征图为 (16, 28, 28)nn.ReLU(),                      # relu层nn.MaxPool2d(kernel_size=2),    # 进行池化操作（2x2 区域）, 输出结果为： (16, 14, 14))self.conv2 = nn.Sequential(         # 下一个套餐的输入 (16, 14, 14)nn.Conv2d(16, 32, 5, 1, 2),     # 输出 (32, 14, 14)nn.ReLU(),                      # relu层nn.Conv2d(32, 32, 5, 1, 2),nn.ReLU(),nn.MaxPool2d(2),                # 输出 (32, 7, 7))self.conv3 = nn.Sequential(         # 下一个套餐的输入 (16, 14, 14)nn.Conv2d(32, 64, 5, 1, 2),     # 输出 (32, 14, 14)nn.ReLU(),             # 输出 (32, 7, 7))self.out = nn.Linear(64 * 7 * 7, 10)   # 全连接层得到的结果def forward(self, x):x = self.conv1(x)x = self.conv2(x)x = self.conv3(x)x = x.view(x.size(0), -1)           # flatten操作，结果为：(batch_size, 32 * 7 * 7)output = self.out(x)return output

 

3.准确率作为评估标准

def accuracy(predictions, labels):pred = torch.max(predictions.data, 1)[1] rights = pred.eq(labels.data.view_as(pred)).sum() return rights, len(labels) 

 

4训练网络模型

# 实例化
net = CNN() 
#损失函数
criterion = nn.CrossEntropyLoss() 
#优化器
optimizer = optim.Adam(net.parameters(), lr=0.001) #定义优化器，普通的随机梯度下降算法#开始训练循环
for epoch in range(num_epochs):#当前epoch的结果保存下来train_rights = [] for batch_idx, (data, target) in enumerate(train_loader):  #针对容器中的每一个批进行循环net.train()                             output = net(data) loss = criterion(output, target) optimizer.zero_grad() loss.backward() optimizer.step() right = accuracy(output, target) train_rights.append(right) if batch_idx % 100 == 0: net.eval() val_rights = [] for (data, target) in test_loader:output = net(data) right = accuracy(output, target) val_rights.append(right)#准确率计算train_r = (sum([tup[0] for tup in train_rights]), sum([tup[1] for tup in train_rights]))val_r = (sum([tup[0] for tup in val_rights]), sum([tup[1] for tup in val_rights]))print('当前epoch: {} [{}/{} ({:.0f}%)]\t损失: {:.6f}\t训练集准确率: {:.2f}%\t测试集正确率: {:.2f}%'.format(epoch, batch_idx * batch_size, len(train_loader.dataset),100. * batch_idx / len(train_loader), loss.data, 100. * train_r[0].numpy() / train_r[1], 100. * val_r[0].numpy() / val_r[1]))