AlexNet: ImageNet Classification with Deep Convolutional Neural Networks

目录

• 激活函数
• 防止过拟合
  • 增加数据
  • Dropout
• 细节
• 代码

AlexNet

上图是论文的网络的结构图，包括5个卷积层和3个全连接层，作者还特别强调，depth的重要性，少一层结果就会变差，所以这种超参数的调节可真是不简单.

激活函数

首先讨论的是激活函数，作者选择的不是\(f(x)=\mathrm{tanh}(x)=(1+e^{-x})^{-1}\)，而是ReLUs ( Rectified Linear Units)——\(f(x)=\max (0, x)\), 当然，作者考虑的问题是比赛的那个数据集，其网络的收敛速度为:



接下来，作者讨论了标准化的问题，说ReLUs是不需要进行这一步的，论文中的那句话我感觉理解的怪怪的:

ReLUs have the desirable property that they do not require input normalization to prevent them fromsaturating.

饱和？

作者说，也可以对ReLUs进行扩展，使得其更有泛化性，把多个核进行标准化处理:



\(i\)表示核的顺序，\(a_{x,y}^i\)则是其值, 说实话，这部分也没怎么弄懂.

然后是关于池化层的部分，一般的池化层的核是不用重叠的，作者这部分也考虑进去了.

防止过拟合

为了防止过拟合，作者提出了他的几点经验.

增加数据

这个数据不是简单的多找点数据，而是通过一些变换使得数据增加.

比如对图片进行旋转，以及PCA提主成分，改变score等.

Dropout

多个模型，进行综合评价是防止过拟合的好方法，但是训练网络不易，dropout, 即让隐层的神经元以一定的概率输出为0来，所以每一次训练，网络的结构实际上都是不一样的，但是整个网络是共享参数的，所以可以一次性训练多个模型?

细节

batch size： 128

momentum: 0.9

weight decay: 0.0005

一般的随机梯度下降好像是没有weight decay这一部分的，但是作者说，实验中这个的选择还是蛮有效的.

代码

"""
epochs: 50
lr: 0.001
batch_size = 128
在训练集上的正确率达到了97%,
在测试集上的正确率为83%.
"""
import torch
import torch.nn as nn
import torchvision
import torchvision.transforms as transforms
import os

class AlexNet(nn.Module):

    def __init__(self, output_size=10):
        super(AlexNet, self).__init__()
        self.conv1 = nn.Sequential(  # 3 x 227 x 227
            nn.Conv2d(3, 96, 11, 4, 0),  # 3通道 输出96通道 卷积核为11 x 11 滑动为4 不补零
            nn.BatchNorm2d(96),
            nn.ReLU()
        )
        self.conv2 = nn.Sequential(  # 96 x 55 x 55
            nn.Conv2d(48, 128, 5, 1, 2),
            nn.BatchNorm2d(128),
            nn.ReLU(),
            nn.MaxPool2d(3, 2)
        )
        self.conv3 = nn.Sequential(  # 256 x 27 x 27
            nn.Conv2d(256, 192, 3, 1, 1),
            nn.BatchNorm2d(192),
            nn.ReLU(),
            nn.MaxPool2d(3, 2)
        )
        self.conv4 = nn.Sequential(  # 384 x 13 x 13
            nn.Conv2d(192, 192, 3, 1, 1),
            nn.BatchNorm2d(192),
            nn.ReLU()
        )
        self.conv5 = nn.Sequential(  # 384 x 13 x 13
            nn.Conv2d(192, 128, 3, 1, 1),
            nn.BatchNorm2d(128),
            nn.ReLU(),
            nn.MaxPool2d(3, 2)
        )
        self.dense = nn.Sequential(
            nn.Linear(9216, 4096),
            nn.BatchNorm1d(4096),
            nn.ReLU(),
            nn.Dropout(0.5),
            nn.Linear(4096, 4096),
            nn.BatchNorm1d(4096),
            nn.ReLU(),
            nn.Dropout(0.5),
            nn.Linear(4096, output_size)
        )

    def forward(self, input):
        x = self.conv1(input)
        x1, x2 = x[:, :48, :, :], x[:, 48:, :, :]  # 拆分
        x1 = self.conv2(x1)
        x2 = self.conv2(x2)
        x = torch.cat((x1, x2), 1)  # 合并
        x1 = self.conv3(x)
        x2 = self.conv3(x)
        x1 = self.conv4(x1)
        x2 = self.conv4(x2)
        x1 = self.conv5(x1)
        x2 = self.conv5(x2)
        x = torch.cat((x1, x2), 1)
        x = x.view(-1, 9216)
        output = self.dense(x)
        return output

class Train:

    def __init__(self, lr=0.001, momentum=0.9, weight_decay=0.0005):
        self.net = AlexNet()
        self.criterion = nn.CrossEntropyLoss()
        self.opti = torch.optim.SGD(self.net.parameters(),
                                    lr=lr, momentum=momentum,
                                    weight_decay=weight_decay)
        self.generate_path()

    def gpu(self):
        self.device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
        if torch.cuda.device_count() > 1:
            print("Let'us use %d GPUs" % torch.cuda.device_count())
            self.net = nn.DataParallel(self.net)
        self.net = self.net.to(self.device)

    def generate_path(self):
        """
        生成保存数据的路径
        :return:
        """
        try:
            os.makedirs('./paras')
            os.makedirs('./logs')
            os.makedirs('./images')
        except FileExistsError as e:
            pass
        name = self.net.__class__.__name__
        paras = os.listdir('./paras')
        self.para_path = "./paras/{0}{1}.pt".format(
            name,
            len(paras)
        )
        logs = os.listdir('./logs')
        self.log_path = "./logs/{0}{1}.txt".format(
            name,
            len(logs)
        )

    def log(self, strings):
        """
        运行日志
        :param strings:
        :return:
        """
        # a 往后添加内容
        with open(self.log_path, 'a', encoding='utf8') as f:
            f.write(strings)

    def save(self):
        """
        保存网络参数
        :return:
        """
        torch.save(self.net.state_dict(), self.para_path)

    def derease_lr(self, multi=10):
        """
        降低学习率
        :param multi:
        :return:
        """
        self.opti.param_groups()[0]['lr'] /= multi

    def train(self, trainloder, epochs=50):
        data_size = len(trainloder) * trainloder.batch_size
        for epoch in range(epochs):
            running_loss = 0.
            acc_count = 0.
            if (epoch + 1) % 10 is 0:
                self.derease_lr()
                self.log(
                    "learning rate change!!!\n"
                )
            for i, data in enumerate(trainloder):
                imgs, labels = data
                imgs = imgs.to(self.device)
                labels = labels.to(self.device)
                out = self.net(imgs)
                loss = self.criterion(out, labels)
                _, pre = torch.max(out, 1)  #判断是否判断正确
                acc_count += (pre == labels).sum().item() #加总对的个数

                self.opti.zero_grad()
                loss.backward()
                self.opti.step()

                running_loss += loss.data

                if (i+1) % 10 is 0:
                    strings = "epoch {0:<3} part {1:<5} loss: {2:<.7f}\n".format(
                        epoch, i, running_loss * 50
                    )
                    self.log(strings)
                    running_loss = 0.
            self.log(
                "Accuracy of the network on %d train images: %d %%\n" %(
                    data_size, acc_count / data_size * 100
                )
            )
            self.save()

class Test:

    def __init__(self, classes, path=0):
        self.net = AlexNet()
        self.classes = classes
        self.load(path)

    def load(self, path=0):
        if isinstance(path, int):
            name = self.net.__class__.__name__
            path = "./paras/{0}{1}.pt".format(
                name, path
            )
        #加载参数, map_location 因为是用GPU训练的, 保存的是是GPU的模型
        #如果需要在cpu的情况下测试, 选择map_location="cpu".
        self.net.load_state_dict(torch.load(path, map_location="cpu"))
        self.net.eval()

    def showimgs(self, imgs, labels):
        n = imgs.size(0)
        pres = self.__call__(imgs)
        n = max(n, 7)
        fig, axs = plt.subplots(n)
        for i, ax in enumerate(axs):
            img = imgs[i].numpy().transpose((1, 2, 0))
            img = img / 2 + 0.5
            label = self.classes[labels[i]]
            pre = self.classes[pres[i]]
            ax.set_title("{0}|{1}".format(
                label, pre
            ))
            ax.plot(img)
            ax.get_xaxis().set_visible(False)
            ax.get_yaxis().set_visible(False)
        plt.tight_layout()
        plt.show()

    def acc_test(self, testloader):
        data_size = len(testloader) * testloader.batch_size
        acc_count = 0.
        for (imgs, labels) in testloader:
            pre = self.__call__(imgs)
            acc_count += (pre == labels).sum().item()
        return acc_count / data_size

    def __call__(self, imgs):
        out = self.net(imgs)
        _, pre = torch.max(out, 1)
        return pre