cifar10数据集pytorch

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cifar10数据集

导入库

import torch import torchvision import torchvision.transforms as transforms import ssl from torch.autograd import Variable import torch.nn as nn import torch.nn.functional as F #交叉熵 import torch.optim as optim import matplotlib.pyplot as plt #图像绘制 import numpy as np import time #时间 

导入数据集

关于cifar10数据集，可以访问它的官网http://www.cs.toronto.edu/~kriz/cifar.html

transform = transforms.Compose( [transforms.RandomHorizontalFlip(), transforms.RandomGrayscale(), transforms.ToTensor(), transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))]#数据类型的转化，以及将数据进行归一化 ) trainset = torchvision.datasets.CIFAR10(root='./cifar10', train=True, download=True, transform=transform) trainloader = torch.utils.data.DataLoader(trainset, batch_size=100, shuffle=True, num_workers=2) #训练集 testset = torchvision.datasets.CIFAR10(root='./cifar10', train=False, download=True, transform=transform) testloader = torch.utils.data.DataLoader(testset, batch_size=4, shuffle=False, num_workers=2) #测试集 classes = ('plane', 'car', 'bird', 'cat', 'deer', 'dog', 'frog', 'horse', 'ship', 'truck')#数据的分类 

我查了许多代码，有些代码里会出现下面的一行代码，这个是和前面import ssl相对应的。

ssl._create_default_https_context = ssl._create_unverified_context # 解决访问https时不受ssl信任证书的问题 

定义网络

这里采用的是简单网络处理的具体代码如下：

class Net(nn.Module):#简单网络 def __init__(self): super(Net, self).__init__() self.conv1 = nn.Conv2d(3, 6, 5)#卷积 self.pool = nn.MaxPool2d(2, 2)#池化 self.conv2 = nn.Conv2d(6, 16, 5) self.fc1 = nn.Linear(16*5*5, 120) self.fc2 = nn.Linear(120, 84)#全连接层 self.fc3 = nn.Linear(84, 10) def forward(self,x):#构建模型 x = self.pool(F.relu(self.conv1(x))) x = self.pool(F.relu(self.conv2(x))) x = x.view(-1, 16 * 5 * 5) x = F.relu(self.fc1(x)) x = F.relu(self.fc2(x)) x = self.fc3(x) return x 

标定义损失函数和优化器

criterion = nn.CrossEntropyLoss() optimizer = optim.SGD(net.parameters(), lr=0.001, momentum=0.9) 

 for epoch in range(20): timestart = time.time() running_loss = 0.0 for i,data in enumerate(trainloader, 0): inputs, labels = data inputs, labels = Variable(inputs), Variable(labels) optimizer.zero_grad() outputs = net(inputs) loss = criterion(outputs, labels) loss.backward() optimizer.step() running_loss += loss.item() if i % 500 == 499: print('[%d ,%5d] loss: %.3f' % (epoch + 1, i + 1, running_loss / 500)) running_loss = 0.0 print('epoch %d cost %3f sec' % (epoch + 1, time.time()-timestart)) print('Finished Training') 

训练结果

dataiter = iter(testloader) images, labels = dataiter.__next__() imshow(torchvision.utils.make_grid(images)) print('GroundTruth:', ' '.join('%5s' % classes[labels[j]] for j in range(4))) outputs = net(Variable(images)) _, predicted = torch.max(outputs.data,1) print('Predicted:', ' '.join('%5s' % classes[labels[j]] for j in range(4))) correct = 0 total = 0 for data in testloader: images, labels = data outputs = net(Variable(images)) _, predicted = torch.max(outputs.data, 1) total += labels.size(0) correct += (predicted == labels).sum() print('Accuracy of the network on the 10000 test images: %d %%' % (100*correct/total)) class_correct = list(0. for i in range(10)) class_total = list(0. for i in range(10)) for data in testloader: images, labels = data outputs = net(Variable(images)) _, predicted = torch.max(outputs.data, 1) c = (predicted == labels).squeeze() for i in range(4): label = labels[i] class_correct[label] += c[i] class_total[label] += 1 for i in range(10): print('Accuracy of %5s : %2d %%' % (classes[i], 100 * class_correct[i] / class_total[i])) 

采用其他网络进行优化

我这里只尝试了lenet和vgg16两种网络

LeNet

class 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): x = F.relu(self.conv1(x)) x = F.max_pool2d(x, 2) x = F.relu(self.conv2(out)) x = F.max_pool2d(x, 2) x = out.view(x.size(0), -1) x = F.relu(self.fc1(x)) x = F.relu(self.fc2(x)) x = self.fc3(x) return out 

VGG16

class VGGTest(nn.Module): def __init__(self, pretrained=True, numClasses=10): super(VGGTest, self).__init__() self.conv1_1 = nn.Conv2d(3, 64, kernel_size=3, padding=1) self.relu1_1 = nn.ReLU(inplace=True) self.conv1_2 = nn.Conv2d(64, 64, kernel_size=3, padding=1) self.relu1_2 = nn.ReLU(inplace=True) self.pool1 = nn.MaxPool2d(kernel_size=2, stride=2) self.conv2_1 = nn.Conv2d(64, 128, kernel_size=3, padding=1) self.relu2_1 = nn.ReLU(inplace=True) self.conv2_2 = nn.Conv2d(128, 128, kernel_size=3, padding=1) self.relu2_2 = nn.ReLU(inplace=True) self.pool2 = nn.MaxPool2d(kernel_size=2, stride=2) self.conv3_1 = nn.Conv2d(128, 256, kernel_size=3, padding=1) self.relu3_1 = nn.ReLU(inplace=True) self.conv3_2 = nn.Conv2d(256, 256, kernel_size=3, padding=1) self.relu3_2 = nn.ReLU(inplace=True) self.conv3_3 = nn.Conv2d(256, 256, kernel_size=3, padding=1) self.relu3_3 = nn.ReLU(inplace=True) self.pool3 = nn.MaxPool2d(kernel_size=2, stride=2) self.conv4_1 = nn.Conv2d(256, 512, kernel_size=3, padding=1) self.relu4_1 = nn.ReLU(inplace=True) self.conv4_2 = nn.Conv2d(512, 512, kernel_size=3, padding=1) self.relu4_2 = nn.ReLU(inplace=True) self.conv4_3 = nn.Conv2d(512, 512, kernel_size=3, padding=1) self.relu4_3 = nn.ReLU(inplace=True) self.pool4 = nn.MaxPool2d(kernel_size=2, stride=2) self.conv5_1 = nn.Conv2d(512, 512, kernel_size=3, padding=1) self.relu5_1 = nn.ReLU(inplace=True) self.conv5_2 = nn.Conv2d(512, 512, kernel_size=3, padding=1) self.relu5_2 = nn.ReLU(inplace=True) self.conv5_3 = nn.Conv2d(512, 512, kernel_size=3, padding=1) self.relu5_3 = nn.ReLU(inplace=True) self.pool5 = nn.MaxPool2d(kernel_size=2, stride=2) # 从原始的 models.vgg16(pretrained=True) 中预设值参数值。 if pretrained: pretrained_model = torchvision.models.vgg16(pretrained=pretrained) # 从预训练模型加载VGG16网络参数 pretrained_params = pretrained_model.state_dict() keys = list(pretrained_params.keys()) new_dict = { 
      } for index, key in enumerate(self.state_dict().keys()): new_dict[key] = pretrained_params[keys[index]] self.load_state_dict(new_dict) self.classifier = nn.Sequential( # 定义自己的分类层 nn.Linear(in_features=512 * 1 * 1, out_features=256), # 自定义网络输入后的大小。 # nn.Linear(in_features=512 * 7 * 7, out_features=256), # 原始vgg16的大小是 512 * 7 * 7 ，由VGG16网络决定的，第二个参数为神经元个数可以微调 nn.ReLU(True), nn.Dropout(), nn.Linear(in_features=256, out_features=256), nn.ReLU(True), nn.Dropout(), nn.Linear(in_features=256, out_features=numClasses), )