mnist数据集训练简洁实现

Posted 2021-06-20 洪流之源

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]))