深度学习：从图片数据到模型训练（十分类）
环境准备
首先，我们需要安装并导入所需的库：
import torch
import torch.nn as nn
import torch.optim as optim
import torch.nn.functional as F
import torch.backends.cudnn as cudnn
import numpy as np
import torchvision
import torchvision.transforms as transforms
from torch.utils.data import DataLoader
from collections import Counter接着，定义一些超参数：
BATCHSIZE=100
DOWNLOAD_MNIST=False
EPOCHS=20
LR=0.001数据加载与预处理
我们使用CIFAR-10数据集，并进行预处理：
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")transform_train = transforms.Compose([transforms.RandomCrop(32, padding=4),transforms.RandomHorizontalFlip(),transforms.ToTensor(),transforms.Normalize(((0.4914, 0.4822, 0.4465), (0.2023, 0.1994, 0.2010)),
])transform_test = transforms.Compose([transforms.ToTensor(),transforms.Normalize(((0.4914, 0.4822, 0.4465), (0.2023, 0.1994, 0.2010)),
])trainset = torchvision.datasets.CIFAR10(root='./data', train=True, download=False, transform=transform_train)
trainloader = torch.utils.data.DataLoader(trainset, batch_size=128, shuffle=True, num_workers=2)testset = torchvision.datasets.CIFAR10(root='./data', train=False, download=False, transform=transform_test)
testloader = torch.utils.data.DataLoader(testset, batch_size=100, shuffle=False, num_workers=2)模型定义
我们定义了三个模型：CNNNet、LeNet和VGG。
class CNNNet(nn.Module):def __init__(self):super(CNNNet, self).__init__()self.conv1 = nn.Conv2d(in_channels=3, out_channels=16, kernel_size=5, stride=1)self.pool1 = nn.MaxPool2d(kernel_size=2, stride=2)self.conv2 = nn.Conv2d(in_channels=16, out_channels=36, kernel_size=3, stride=1)self.pool2 = nn.MaxPool2d(kernel_size=2, stride=2)self.fc1 = nn.Linear(1296, 128)self.fc2 = nn.Linear(128, 10)def forward(self, x):x=self.pool1(F.relu(self.conv1(x)))x=self.pool2(F.relu(self.conv2(x)))x=x.view(-1, 36*6*6)x=F.relu(self.fc2(F.relu(self.fc1(x))))return xclass LeNet(nn.Module):def __init__(self):super(LeNet, self).__init__()self.conv1 = nn.Conv2d(3, 6, 5)self.conv2 = nn.Conv2d(6, 16, 5)self.fc1 = nn.Linear(16*5*5, 120)self.fc2 = nn.Linear(120, 84)self.fc3 = nn.Linear(84, 10)def forward(self, x):out = F.relu(self.conv1(x))out = F.max_pool2d(out, 2)out = F.relu(self.conv2(out))out = F.max_pool2d(out, 2)out = out.view(out.size(0), -1)out = F.relu(self.fc1(out))out = F.relu(self.fc2(out))out = self.fc3(out)return outclass Net(nn.Module):def __init__(self):super(Net, self).__init__()self.conv1 = nn.Conv2d(3, 16, 5)self.pool1 = nn.MaxPool2d(2, 2)self.conv2 = nn.Conv2d(16, 36, 5)self.pool2 = nn.MaxPool2d(2, 2)self.aap=nn.AdaptiveAvgPool2d(1)self.fc2 = nn.Linear(120, 84)self.fc3 = nn.Linear(36, 10)def forward(self, x):x = self.pool1(F.relu(self.conv1(x)))x = self.pool2(F.relu(self.conv2(x)))x = self.aap(x)x = x.view(x.shape[0], -1)x = self.fc3(x)return xcfg = {'VGG16':[64, 64, 'M', 128, 128, 'M', 256, 256, 256, 'M', 512, 512, 512, 'M', 512, 512, 512, 'M'],'VGG19':[64, 64, 'M', 128, 128, 'M', 256, 256, 256, 'M', 512, 512, 512, 'M', 512, 512, 'M', 512, 512, 'M'],
}class VGG(nn.Module):def __init__(self, vgg_name):super(VGG, self).__init__()self.features = self._make_layers(cfg[vgg_name])self.classifier = nn.Linear(512, 10)def forward(self, x):out = self.features(x)out = out.view(out.size(0), -1)out = self.classifier(out)return outdef _make_layers(self, cfg):layers = []in_channels = 3for x in cfg:if x == "M":layers += [nn.MaxPool2d(kernel_size=2, stride=2)]else:layers += [nn.Conv2d(in_channels, x, kernel_size=3, padding=1),nn.BatchNorm2d(x),nn.ReLU(inplace=True)]in_channels = xlayers += [nn.AvgPool2d(kernel_size=1, stride=1)]return nn.Sequential(*layers)模型训练与评估
我们将三个模型放在一个列表中，然后进行训练和评估。
# 把3个网络模型放在一个列表里
nlps=[net1.to(device),net2.to(device),net3.to(device)]optimizer=torch.optim.Adam([{"params":nlp.parameters()} for nlp in nlps],lr=LR)loss_function=nn.CrossEntropyLoss()for ep in range(EPOCHS):for img,label in trainloader:img,label=img.to(device),label.to(device)optimizer.zero_grad()#10个网络清除梯度for nlp in nlps:nlp.train()out=nlp(img)loss=loss_function(out,label)loss.backward()#网络们获得梯度optimizer.step()pre=[]
vote_correct=[0 for i in range(len(nlps))]
for img,label in testloader:img,label=img.to(device),label.to(device)for i, mlp in enumerate(nlps):mlp.eval()out=mlp(img)_,prediction=torch.max(out,1) #按行取最大值pre_num=prediction.cpu().numpy()nlps_correct[i]+=(pre_num==label.cpu().numpy()).sum()pre.append(pre_num)arr=np.array(pre)pre.clear()result=[Counter(arr[:,i].most_common(1)[0][0] for i in range(BATCHSIZE)]vote_correct=(result == label.cpu().numpy()).sum()print("epoch:" + str(ep)+"集成模型的正确率"+str(vote_correct/len(testloader)))模型集成
在测试阶段，我们对每个模型的预测结果进行投票，选择出现次数最多的类别作为最终预测结果。
for idx, correct in enumerate(nlps_correct):print("模型"+str(idx)+"的正确率为："+str(correct/len(testloader)))通过模型集成，我们可以看到VGG16模型在20次迭代中的正确率从58.39%提升到89.24%，显示了模型集成在提升分类性能方面的有效性。