Pytorch优化器-Optimizer
发布日期：2024-04-22浏览次数：
要使用PyTorch训练CIFAR-10数据集，可以按照以下步骤进行操作：
1. 导入所需的PyTorch库：
```python
import torch
import torchvision
import torchvision.transforms as transforms
import torch.nn as nn
import torch.optim as optim
```
2. 定义训练和测试数据集的转换方式：
```python
transform=transforms.Compose(
[transforms.ToTensor(),
transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))])
```
3. 加载CIFAR-10数据集并创建数据加载器：
```python
trainset=torchvision.datasets.CIFAR10(root='https://blog.csdn.net/weixin_43869493/article/details/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='https://blog.csdn.net/weixin_43869493/article/details/data', train=False,
download=True, transform=transform)
testloader=torch.utils.data.DataLoader(testset, batch_size=4,
shuffle=False, num_workers=2)
```
4. 定义神经网络模型：
```python
class Net(nn.Module):
def __init__(self):
super(Net, self).__init__()
self.conv1=nn.Conv2d(3, 6, 5)
self.pool=nn.MaxPool2d(2, 2)
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=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
net=Net()
```
5. 定义损失函数和优化器：
```python
criterion=nn.CrossEntropyLoss()
optimizer=optim.SGD(net.parameters(), lr=0.001, momentum=0.9)
```
6. 进行模型的训练和测试：
```python
for epoch in range(2):
running_loss=0.0
for i, data in enumerate(trainloader, 0):
# 获取输入
inputs, labels=data
# 梯度清零
optimizer.zero_grad()
# 正向传播、反向传播、优化
outputs=net(inputs)
loss=criterion(outputs, labels)
loss.backward()
optimizer.step()
# 统计损失
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')
# 在测试集上测试模型
correct=0
total=0
with torch.no_grad():
for data in testloader:
images, labels=data
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))
```
通过以上步骤，我们可以使用PyTorch训练CIFAR-10数据集，并获得模型的准确率。
