目录

本文为针对CIFAR-10数据集的基于简单神经网络LeNet分类实现（pytorch实现）

LeNet 网络模型

Tip：（以上为原始LeNet）为了更好的效果，我在模型实现时此处将池化层换为Max

CIFAR-10 数据集

CIFAR-10数据集由60000张32x32的彩色图像组成，分为10类，每类有6000张图像。有50000张训练图像和10000张测试图像。

该数据集被分为五个训练批和一个测试批，每个批有10000张图像。测试批包含从每个类中随机选择的1000张图像。训练批包含其余的随机顺序的图像，但有些训练批可能包含一个类别的图像多于另一个。在它们之间，训练批次恰好包含了每个类别的5000张图像。

下面是数据集中的类别，以及每个类别的10张随机图像。

关于数据集更多详情请见：CIFAR-10数据集官方说明

Pytorch 实现代码

import torch
from torch import  nn
import torch.nn.functional as F
import torchvision
import torch.utils.data as data
import torchvision.transforms as transforms


class Lenet5(nn.Module):

    def __init__(self,input_channels):
        super().__init__()
        #第1个卷积层
        self.conv1 = nn.Conv2d(input_channels , 6 , kernel_size = 5 , padding = 2)
        #第1个池化层
        self.pooling1 = nn.MaxPool2d(kernel_size = 2, stride = 2)
        #第2个卷积层
        self.conv2= nn.Conv2d(6 , 16 , kernel_size=5)
        #第2个池化层
        self.pooling2 = nn.MaxPool2d(kernel_size = 2, stride=2)
        ##最后的三个FC
        self.Flatten = nn.Flatten()
        # 计算得出的当前的前面处理过后的shape，当然也可print出来以后再确定
        self.Linear1 = nn.Linear(16*6*6,120)
        self.Linear2 = nn.Linear(120,84)
        self.Linear3 = nn.Linear(84,10)

    def forward(self,X):
    ''' 前向推导 '''
        X = self.pooling1(F.relu(self.conv1(X)))
        X = self.pooling2(F.relu(self.conv2(X)))
        X = X.view(X.size()[0],-1)
        X = F.relu(self.Linear1(X))
        X = F.relu(self.Linear2(X))
        X = F.relu(self.Linear3(X))

        return X


def load_CIFAR10(batch_size, resize=None):
    """ 加载数据集到内存 """
    trans = [transforms.ToTensor(),transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))]
    if resize:
        trans.insert(0, transforms.Resize(resize))

    trans = transforms.Compose(trans)
    mnist_train = torchvision.datasets.CIFAR10(root="dataset",
                                                    train=True,
                                                    transform=trans,
                                                    download=True)
    mnist_test = torchvision.datasets.CIFAR10(root="dataset",
                                                   train=False,
                                                   transform=trans,
                                                   download=True)
    return (data.DataLoader(mnist_train, batch_size, shuffle=True,
                            num_workers=2),
                data.DataLoader(mnist_test, batch_size, shuffle=False,
                            num_workers=2))



def get_labels(labels):
    '''    标签转换  '''
    text_labels = ['plane', 'car', 'bird', 'cat', 'deer', 'dog', 'frog', 'horse', 'ship', 'truck']
    return [text_labels[int(i)] for i in labels]




def train(loss,updater,train_iter,net,epoches):
    '''  训练模型  '''
    for epoch in range(epoches):
        run_loss = 0
        for step,(X,y) in enumerate(train_iter):
            if torch.cuda.is_available():
                X = X.cuda()
                y = y.cuda()
            y_hat = net.forward(X)  #前向推导
            ls = loss(y_hat,y).sum() #计算误差
            updater.zero_grad() #梯度清零
            ls.backward() #计算新的梯度
            run_loss += ls.item()
            updater.step() #更新权值
        print( f'true:y preds:y_hat.argmax(axis=1) epoch:epoch:02d\\t epoch_loss run_loss/5000\\t ')
    print('finished training\\n')

def predict(net,test_iter,n=6):
    '''   测试集预测 '''
    for X, y in test_iter:
        if torch.cuda.is_available():
            X = X.cuda()
            y = y.cuda()
        trues = get_labels(y)
        preds = get_labels(net(X).argmax(axis=1))
        titles = ['groundTruth :'+true + ' ' +'preds: '+ pred for true, pred in zip(trues, preds)]
        print(titles[0:n])



if __name__ == '__main__':
    #设置超参数
    batch_size,  learning_rate,  epoches = 10, 0.05, 1  
    #加载数据
    trainSet,testSet = load_CIFAR10(batch_size) 
    #加载模型
    net = Lenet5(3) 
    if torch.cuda.is_available():
        net.cuda()
    
     # 选择损失函数
    loss = nn.CrossEntropyLoss() 
    # 优化器
    updater = torch.optim.SGD(net.parameters(), lr=learning_rate) 
    #训练 
    train(loss,updater,trainSet,net,batch_size,epoches,learning_rate)
    #测试集预测  
    predict(net,testSet)