使用torchvision.models.vgg16编写代码
时间: 2023-12-02 11:06:08 浏览: 36
好的,以下是使用torchvision.models.vgg16编写一个简单的分类器代码示例。
首先,我们需要导入所需的库:
```python
import torch
import torchvision
import torchvision.transforms as transforms
import torch.nn as nn
import torch.optim as optim
```
接下来,我们需要定义一些超参数:
```python
batch_size = 32
num_classes = 10
lr = 0.001
num_epochs = 10
```
然后,我们需要加载数据集并进行预处理:
```python
transform = transforms.Compose(
[transforms.Resize((224, 224)),
transforms.ToTensor(),
transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))])
trainset = torchvision.datasets.CIFAR10(root='./data', train=True,
download=True, transform=transform)
trainloader = torch.utils.data.DataLoader(trainset, batch_size=batch_size,
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=batch_size,
shuffle=False, num_workers=2)
```
接着,我们需要定义模型和损失函数:
```python
model = torchvision.models.vgg16(pretrained=True)
model.classifier[6] = nn.Linear(4096, num_classes)
criterion = nn.CrossEntropyLoss()
```
注意到我们使用了预训练的vgg16模型,并将其分类器层修改为输出我们需要的类别数。
接下来,我们定义优化器:
```python
optimizer = optim.SGD(model.parameters(), lr=lr, momentum=0.9)
```
最后,我们可以开始训练模型:
```python
for epoch in range(num_epochs):
running_loss = 0.0
for i, data in enumerate(trainloader, 0):
inputs, labels = data
optimizer.zero_grad()
outputs = model(inputs)
loss = criterion(outputs, labels)
loss.backward()
optimizer.step()
running_loss += loss.item()
if i % 2000 == 1999:
print('[%d, %5d] loss: %.3f' %
(epoch + 1, i + 1, running_loss / 2000))
running_loss = 0.0
print('Finished Training')
```
这个代码示例中,我们训练了一个vgg16模型对CIFAR10数据集进行分类。你可以根据自己的需求修改代码来适应不同的任务。