如何对上述代码进行训练?写一段训练上述的代码
时间: 2024-02-09 15:10:23 浏览: 25
好的,以下是一个简单的示例代码,用于训练上述的CNN模型:
```python
import torch
import torch.nn as nn
import torch.optim as optim
import torchvision
import torchvision.transforms as transforms
# Define the CNN model
class ConvNet(nn.Module):
def __init__(self):
super(ConvNet, self).__init__()
self.conv1 = nn.Conv2d(1, 16, kernel_size=5)
self.conv2 = nn.Conv2d(16, 32, kernel_size=5)
self.fc1 = nn.Linear(512, 100)
self.fc2 = nn.Linear(100, 10)
def forward(self, x):
x = self.conv1(x)
x = nn.functional.relu(nn.functional.max_pool2d(x, 2))
x = self.conv2(x)
x = nn.functional.relu(nn.functional.max_pool2d(x, 2))
x = x.view(-1, 512)
x = nn.functional.relu(self.fc1(x))
x = self.fc2(x)
return x
# Define the training function
def train(model, trainloader, criterion, optimizer):
model.train()
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()
return running_loss / len(trainloader)
# Define the testing function
def test(model, testloader, criterion):
model.eval()
running_loss = 0.0
correct = 0
total = 0
with torch.no_grad():
for data in testloader:
inputs, labels = data
outputs = model(inputs)
loss = criterion(outputs, labels)
running_loss += loss.item()
_, predicted = torch.max(outputs.data, 1)
total += labels.size(0)
correct += (predicted == labels).sum().item()
accuracy = 100 * correct / total
return running_loss / len(testloader), accuracy
# Set up the dataset and data loaders
transform = transforms.Compose(
[transforms.ToTensor(),
transforms.Normalize((0.5,), (0.5,))])
trainset = torchvision.datasets.MNIST(root='./data', train=True,
download=True, transform=transform)
trainloader = torch.utils.data.DataLoader(trainset, batch_size=64,
shuffle=True, num_workers=2)
testset = torchvision.datasets.MNIST(root='./data', train=False,
download=True, transform=transform)
testloader = torch.utils.data.DataLoader(testset, batch_size=64,
shuffle=False, num_workers=2)
# Set up the model, loss function, and optimizer
model = ConvNet()
criterion = nn.CrossEntropyLoss()
optimizer = optim.Adam(model.parameters(), lr=0.001)
# Train the model for 10 epochs
for epoch in range(10):
train_loss = train(model, trainloader, criterion, optimizer)
test_loss, test_accuracy = test(model, testloader, criterion)
print(f"Epoch {epoch+1:2d} - Train Loss: {train_loss:.3f}, Test Loss: {test_loss:.3f}, Test Accuracy: {test_accuracy:.2f}%")
```
在此示例代码中,我们首先定义了一个简单的 CNN 模型 `ConvNet`,然后定义了 `train` 和 `test` 函数,用于训练和测试模型。在 `train` 函数中,我们首先将模型设为训练模式,然后遍历训练数据集中的所有批次,计算每个批次的输出和损失,并使用反向传播更新模型参数。最后,我们返回训练损失的平均值。在 `test` 函数中,我们首先将模型设为评估模式,然后遍历测试数据集中的所有批次,计算每个批次的输出和损失,并计算模型在测试数据集上的准确率和测试损失。最后,我们返回测试损失的平均值和模型的准确率。
在示例中,我们使用 MNIST 数据集进行训练和测试,并将模型训练 10 个 epoch。在每个 epoch 结束时,我们输出训练损失、测试损失和测试准确率。
请注意,这只是一个简单的示例代码,您可以根据您的具体需求进行修改。
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