本文主要是介绍AlexNet-pytorch实现，希望对大家解决编程问题提供一定的参考价值，需要的开发者们随着小编来一起学习吧！
AlexNet
1.网络架构
如图所示可见其结构为：
AlexNet网络共八层，五层卷积层和三层全连接层。这是一个非常经典的设计，为后续神经网络的发展提供了极大的贡献。
2.pytorch网络设计
网络设计部分做了一些小的修改，目的是为了适配minist的3x28x28的输入图片大小。
网络构造代码部分：
class AlexNet(nn.Module):def __init__(self):super(AlexNet, self).__init__()self.conv = nn.Sequential(nn.Conv2d(3, 96, 11, 1, 5), # in_channels, out_channels, kernel_size, stride, paddingnn.ReLU(),nn.MaxPool2d(3, 1), # kernel_size, stride 26x26# 减少卷积窗口，使用填充为2来使输入输出大小一致nn.Conv2d(96, 256, 5, 1, 2),nn.ReLU(),nn.MaxPool2d(4, 2), # 12x12# 下面接三个卷积层nn.Conv2d(256, 384, 3, 1, 1),nn.ReLU(),nn.Conv2d(384, 384, 3, 1, 1),nn.ReLU(),nn.Conv2d(384, 256, 3, 1, 1),nn.ReLU(),nn.MaxPool2d(4, 2) # 5x5)self.fc = nn.Sequential(nn.Linear(256 * 5 * 5, 4096),nn.Dropout(0.5),nn.Linear(4096, 4096),nn.ReLU(),nn.Dropout(0.5),nn.Linear(4096, 10),)def forward(self, img):img.shape[0]# img.resize_(3,224,224)feature = self.conv(img)output = self.fc(feature.view(img.shape[0], -1))return output3.网络测试
一些基础设置与上一篇文章一致，还是贴一下代码。
网络测试部分我使用的是minist数据集，为了贴近真实（主要是方便我自己懂），在下载了数据集之后将其转为了图片数据集，更为直观。数据集分为train 和test两部分，在测试中需要做如下配置：
1.依赖资源引入
draw_tool是一个自己编写的绘制loss，acc的画图库，device使用了我电脑的1050ti显卡。
import torch
from matplotlib import pyplot as plt
import torch.optim as optim
from torch.autograd import Variable
import torch.nn.functional as F
from torch import nn
from torchsummary import summary
from torchvision import transforms
from torch.utils.data import Dataset, DataLoader
from PIL import Image
import draw_toolroot = "F:/pycharm/dataset/mnist/MNIST/"
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
draw = draw_tool.draw_tool()
2.数据集的读取和分类
#加载图片
def default_loader(path):return Image.open(path).convert('RGB')#构造标注和图片相关
class MyDataset(Dataset):def __init__(self, txt, transform=None, target_transform=None, loader=default_loader):fh = open(txt, 'r')imgs = []for line in fh:line = line.strip('\n')line = line.rstrip()words = line.split()imgs.append((words[0], int(words[1])))self.imgs = imgsself.transform = transformself.target_transform = target_transformself.loader = loaderdef __getitem__(self, index):fn, label = self.imgs[index]img = self.loader(fn)if self.transform is not None:img = self.transform(img)return img, labeldef __len__(self):return len(self.imgs)train_data = MyDataset(txt=root + 'rawtrain.txt', transform=transforms.ToTensor())
test_data = MyDataset(txt=root + 'rawtest.txt', transform=transforms.ToTensor())
train_loader = DataLoader(dataset=train_data, batch_size=31, shuffle=True)
test_loader = DataLoader(dataset=test_data, batch_size=31, shuffle=True)
transform = transforms.Compose([transforms.ToTensor(), transforms.Normalize((0.1307,), (0.3081,))])
3.模型训练设置
model = AlexNet()
#使用softmax分类
criterion = torch.nn.CrossEntropyLoss()
#设置随机梯度下降 学习率和L2正则
optimizer = optim.SGD(model.parameters(), lr=0.01, momentum=0.5)
#使用GPU训练
model = model.to(device)
4.训练
每训练一个epoch 做一次平均loss train acc test acc的计算绘制
def train(epoch):running_loss = 0.0num_correct = 0.0total = 0correct = 0total = 0test_acc = 0.0# trainfor batch_idx, data in enumerate(train_loader, 0):inputs, target = datainputs = inputs.to(device)target = target.to(device)optimizer.zero_grad()# forward + backward + updateoutputs = model(inputs)loss = criterion(outputs, target)loss.backward()optimizer.step()running_loss += loss.item()_, predicted = torch.max(outputs.data, dim=1)total += target.size(0)num_correct += (predicted == target).sum().item()# #test# with torch.no_grad():# for data in test_loader:# images, labels = data# images = images.to(device)# labels = labels.to(device)# outputs = model(images)# _, predicted = torch.max(outputs.data, dim=1)# total += labels.size(0)## correct += (predicted == labels).sum().item()print('[%d, %5d] loss: %.3f' % (epoch + 1, batch_idx + 1, running_loss / len(train_loader)))# print('Accuracy on test set: %d %%' % (100 * correct / total))# test_acc=100 * correct / totaltest_acc = test()acc = (num_correct / len(train_loader.dataset) * 100)print("num_correct=")print(acc)running_loss /= len(train_loader)draw.new_data(running_loss, acc, test_acc, 2)draw.draw()def test():correct = 0total = 0with torch.no_grad():for data in test_loader:images, labels = dataimages = images.to(device)labels = labels.to(device)outputs = model(images)_, predicted = torch.max(outputs.data, dim=1)total += labels.size(0)correct += (predicted == labels).sum().item()test_acc = 100 * correct / totalprint('Accuracy on test set: ', test_acc, '%')return test_acc
5.结果统计
if __name__ == '__main__':for epoch in range(20):train(epoch)torch.save(model.state_dict(), "minist_last.pth")draw.show()
从图中效果可以看到随着训练次数的增加，loss在不断下降，train acc 和test acc 也在慢慢收敛，最终达到了train acc=97% test acc=96%的效果。但与之前上一文的训练有一样的问题所在，不知道为什么中途的test acc会突然下降，这里就不在往下继续训练了，网络变得更为复杂并不代表精度一定会上升，反而对于简单数据的预测来说，只会更差。
留下一个问题，就是为什么我的test acc 会突然下滑这么多，如果有朋友有自己的想法或者有大佬愿意回复我一下还请评论一下，谢谢。
这篇关于AlexNet-pytorch实现的文章就介绍到这儿，希望我们推荐的文章对编程师们有所帮助！
