经典卷积神经网络-AlexNet

AlexNet是由Alex Krizhevsky、Ilya Sutskever和Geoffrey Hinton在2012年ImageNet图像分类竞赛中提出的一种经典的卷积神经网络。当时，AlexNet在 ImageNet 大规模视觉识别竞赛中取得了优异的成绩，把深度学习模型在比赛中的正确率提升到一个前所未有的高度。因此，它的出现对深度学习发展具有里程碑式的意义。

基本结构

AlexNet输入为RGB三通道的224 × 224 × 3大小的图像（也可填充为227 × 227 × 3 ）。AlexNet 共包含5 个卷积层（包含3个池化）和 3 个全连接层。其中，每个卷积层都包含卷积核、偏置项、ReLU激活函数和局部响应归一化（LRN）模块。第1、2、5个卷积层后面都跟着一个最大池化层，后三个层为全连接层。最终输出层为softmax，将网络输出转化为概率值，用于预测图像的类别。

由于ImageNet数据集太大，本文以MNIST数据集进行代替，修改网络参数，输入通道为1，输出结果为10个。

代码实现

model.py

import torch from torch import nn class AlexNet(nn.Module): def __init__(self, *args, **kwargs) -> None: super().__init__(*args, **kwargs) self.model = nn.Sequential( nn.Conv2d(1,96,kernel_size=11,stride=4,padding=1),nn.ReLU(), nn.MaxPool2d(kernel_size=3,stride=2), nn.Conv2d(96,256,kernel_size=5,padding=2),nn.ReLU(), nn.MaxPool2d(kernel_size=3,stride=2), nn.Conv2d(256,384,kernel_size=3,padding=1),nn.ReLU(), nn.Conv2d(384,384,kernel_size=3,padding=1),nn.ReLU(), nn.Conv2d(384,256,kernel_size=3,padding=1),nn.ReLU(), nn.MaxPool2d(kernel_size=3,stride=2), nn.Flatten(), nn.Linear(6400,4096),nn.ReLU(), nn.Dropout(p=0.5), nn.Linear(4096,4096),nn.ReLU(), nn.Dropout(p=0.5), nn.Linear(4096,10) ) def forward(self,x): return self.model(x) # 验证网络正确性 if __name__ == '__main__': net = AlexNet() my_input = torch.ones((64,1,28,28)) my_output = net(my_input) print(my_output.shape)

train.py

import torch from torch import nn from torch.utils.data import DataLoader from torch.utils.tensorboard import SummaryWriter from torchvision import datasets from torchvision.transforms import transforms from model import AlexNet # 扫描数据次数 epochs = 10 # 分组大小 batch = 64 # 学习率 learning_rate = 0.01 # 训练次数 train_step = 0 # 测试次数 test_step = 0 # 定义图像转换 transform = transforms.Compose([ transforms.Resize(224), transforms.ToTensor() ]) # 读取数据 train_dataset = datasets.MNIST(root="./dataset",train=True,transform=transform,download=True) test_dataset = datasets.MNIST(root="./dataset",train=False,transform=transform,download=True) # 加载数据 train_dataloader = DataLoader(train_dataset,batch_size=batch,shuffle=True,num_workers=0) test_dataloader = DataLoader(test_dataset,batch_size=batch,shuffle=True,num_workers=0) # 数据大小 train_size = len(train_dataset) test_size = len(test_dataset) print("训练集大小：{}".format(train_size)) print("验证集大小：{}".format(test_size)) # GPU device = torch.device("mps" if torch.backends.mps.is_available() else "cpu") print(device) # 创建网络 net = AlexNet() net = net.to(device) # 定义损失函数 loss = nn.CrossEntropyLoss() loss = loss.to(device) # 定义优化器 optimizer = torch.optim.SGD(net.parameters(),lr=learning_rate) writer = SummaryWriter("logs") # 训练 for epoch in range(epochs): print("-------------------第 {} 轮训练开始-------------------".format(epoch)) net.train() for data in train_dataloader: train_step = train_step + 1 images,targets = data images = images.to(device) targets = targets.to(device) outputs = net(images) loss_out = loss(outputs,targets) optimizer.zero_grad() loss_out.backward() optimizer.step() if train_step%100==0: writer.add_scalar("Train Loss",scalar_value=loss_out.item(),global_step=train_step) print("训练次数：{}，Loss：{}".format(train_step,loss_out.item())) # 测试 net.eval() total_loss = 0 total_accuracy = 0 with torch.no_grad(): for data in test_dataloader: test_step = test_step + 1 images, targets = data images = images.to(device) targets = targets.to(device) outputs = net(images) loss_out = loss(outputs, targets) total_loss = total_loss + loss_out accuracy = (targets == torch.argmax(outputs,dim=1)).sum() total_accuracy = total_accuracy + accuracy # 计算精确率 print(total_accuracy) accuracy_rate = total_accuracy / test_size print("第 {} 轮，验证集总损失为：{}".format(epoch+1,total_loss)) print("第 {} 轮，精确率为：{}".format(epoch+1,accuracy_rate)) writer.add_scalar("Test Total Loss",scalar_value=total_loss,global_step=epoch+1) writer.add_scalar("Accuracy Rate",scalar_value=accuracy_rate,global_step=epoch+1) torch.save(net,"./model/net_{}.pth".format(epoch+1)) print("模型net_{}.pth已保存".format(epoch+1))