mnist数据集训练简洁实现

妖狐艹你老母 2022-10-17 13:47 152阅读 0赞

import torch
    import torch.nn as nn
    import torch.optim as optim
    from torchvision import datasets, transforms 
    
    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.MaxPool2d(2),                # 输出 (32, 7, 7)
            )
            self.out = nn.Linear(32 * 7 * 7, num_classes)   # 全连接层得到的结果
    
        def forward(self, x):
            x = self.conv1(x)
            x = self.conv2(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
    
    def accuracy(predictions, labels):
        # 对于任意一行（一个样本）的输出值的第1个维度，求最大，得到每一行的最大元素的下标
        pred = torch.max(predictions.data, 1)[1]
        # 比较标签与预测结果是否相同，对相同的进行求和得到正确的数量
        rights = pred.eq(labels.data.view_as(pred)).sum()
        # 返回正确的数量与样本总数
        return rights, len(labels)
    
    if __name__ == '__main__':
        # 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)
        # 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]))