pytorch实现VGG19

PyTorch可以使用预训练的VGG16模型来进行图片识别。以下是实现步骤：

1. 导入必要的库和模块：

import torch
import torchvision
import torchvision.transforms as transforms
import torch.nn as nn

2. 加载数据集并进行预处理：

transform = transforms.Compose(
    [transforms.Resize(256),
     transforms.CenterCrop(224),
     transforms.ToTensor(),
     transforms.Normalize(mean=[.485, .456, .406],
                          std=[.229, .224, .225])])

trainset = torchvision.datasets.CIFAR10(root='./data', train=True,
                                        download=True, transform=transform)
trainloader = torch.utils.data.DataLoader(trainset, batch_size=4,
                                          shuffle=True, num_workers=2)

testset = torchvision.datasets.CIFAR10(root='./data', train=False,
                                       download=True, transform=transform)
testloader = torch.utils.data.DataLoader(testset, batch_size=4,
                                         shuffle=False, num_workers=2)

3. 加载预训练的VGG16模型：

vgg16 = torchvision.models.vgg16(pretrained=True)

4. 修改模型的最后一层，使其适应于CIFAR10数据集：

vgg16.classifier[6] = nn.Linear(4096, 10)

5. 定义损失函数和优化器：

criterion = nn.CrossEntropyLoss()
optimizer = torch.optim.SGD(vgg16.parameters(), lr=.001, momentum=.9)

6. 训练模型：

for epoch in range(2):  # 进行2个epoch的训练

    running_loss = .
    for i, data in enumerate(trainloader, ):
        # 获取输入数据
        inputs, labels = data

        # 梯度清零
        optimizer.zero_grad()

        # 前向传播、反向传播、优化
        outputs = vgg16(inputs)
        loss = criterion(outputs, labels)
        loss.backward()
        optimizer.step()

        # 统计损失值
        running_loss += loss.item()
        if i % 200 == 1999:    # 每200个batch输出一次损失值
            print('[%d, %5d] loss: %.3f' %
                  (epoch + 1, i + 1, running_loss / 200))
            running_loss = .

print('Finished Training')

7. 测试模型：

correct = 
total = 
with torch.no_grad():
    for data in testloader:
        images, labels = data
        outputs = vgg16(images)
        _, predicted = torch.max(outputs.data, 1)
        total += labels.size()
        correct += (predicted == labels).sum().item()

print('Accuracy of the network on the 10000 test images: %d %%' % (
    100 * correct / total))

以上就是使用PyTorch实现VGG16图片识别的步骤。