使用 VGG16 對 CIFAR10 分類
阿新 • • 發佈:2020-08-01
1.定義 dataloader
import torch import torchvision import torchvision.transforms as transforms import matplotlib.pyplot as plt import numpy as np import torch.nn as nn import torch.nn.functional as F import torch.optim as optim # 使用GPU訓練,可以在選單 "程式碼執行工具" -> "更改執行時型別" 裡進行設定 device = torch.device("cuda:0" 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=True, transform=transform_train) testset = torchvision.datasets.CIFAR10(root='./data', train=False, download=True, transform=transform_test) trainloader = torch.utils.data.DataLoader(trainset, batch_size=128, shuffle=True, num_workers=2) testloader = torch.utils.data.DataLoader(testset, batch_size=128, shuffle=False, num_workers=2) classes = ('plane', 'car', 'bird', 'cat', 'deer', 'dog', 'frog', 'horse', 'ship', 'truck')
2.VGG 網路定義並初始化
class VGG(nn.Module): def __init__(self): super(VGG, self).__init__() self.cfg = [64, 'M', 128, 'M', 256, 256, 'M', 512, 512, 'M', 512, 512, 'M'] self.features = self._make_layers(cfg) self.classifier = nn.Linear(2048, 10) def forward(self, x): out = self.features(x) out = out.view(out.size(0), -1) out = self.classifier(out) return out def _make_layers(self, cfg): layers = [] in_channels = 3 for 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 = x layers += [nn.AvgPool2d(kernel_size=1, stride=1)] return nn.Sequential(*layers) # 網路放到GPU上 net = VGG().to(device) criterion = nn.CrossEntropyLoss() optimizer = optim.Adam(net.parameters(), lr=0.001)
3.網路訓練
for epoch in range(10): # 重複多輪訓練 for i, (inputs, labels) in enumerate(trainloader): inputs = inputs.to(device) labels = labels.to(device) # 優化器梯度歸零 optimizer.zero_grad() # 正向傳播 + 反向傳播 + 優化 outputs = net(inputs) loss = criterion(outputs, labels) loss.backward() optimizer.step() # 輸出統計資訊 if i % 100 == 0: print('Epoch: %d Minibatch: %5d loss: %.3f' %(epoch + 1, i + 1, loss.item())) print('Finished Training')
4.測試驗證準確率
correct = 0
total = 0
for data in testloader:
images, labels = data
images, labels = images.to(device), labels.to(device)
outputs = net(images)
_, predicted = torch.max(outputs.data, 1)
total += labels.size(0)
correct += (predicted == labels).sum().item()
print('Accuracy of the network on the 10000 test images: %.2f %%' % (
100 * correct / total))