如何使用上面定义的ResetNet类
时间: 2024-01-22 20:03:59 浏览: 28
在使用上面定义的ResetNet类之前,你需要先定义数据集和数据加载器,并选择合适的优化器和损失函数等超参数。下面是一个使用CIFAR-10数据集进行训练和测试的示例代码:
```python
import torch
import torch.nn as nn
import torch.optim as optim
import torchvision
import torchvision.transforms as transforms
from torch.utils.data import DataLoader
# 定义数据预处理和增强
transform_train = transforms.Compose([
transforms.RandomCrop(32, padding=4),
transforms.RandomHorizontalFlip(),
transforms.ToTensor(),
transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))
])
transform_test = transforms.Compose([
transforms.ToTensor(),
transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))
])
# 加载数据集和数据加载器
trainset = torchvision.datasets.CIFAR10(root='./data', train=True, download=True, transform=transform_train)
trainloader = DataLoader(trainset, batch_size=128, shuffle=True, num_workers=2)
testset = torchvision.datasets.CIFAR10(root='./data', train=False, download=True, transform=transform_test)
testloader = DataLoader(testset, batch_size=128, shuffle=False, num_workers=2)
# 定义模型、优化器和损失函数
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
net = ResetNet(num_classes=10).to(device)
criterion = nn.CrossEntropyLoss()
optimizer = optim.SGD(net.parameters(), lr=0.01, momentum=0.9, weight_decay=5e-4)
# 训练模型
for epoch in range(10):
running_loss = 0.0
for i, (inputs, labels) in enumerate(trainloader, 0):
inputs, labels = inputs.to(device), labels.to(device)
optimizer.zero_grad()
outputs = net(inputs)
loss = criterion(outputs, labels)
loss.backward()
optimizer.step()
running_loss += loss.item()
if i % 100 == 99:
print('[%d, %5d] loss: %.3f' % (epoch + 1, i + 1, running_loss / 100))
running_loss = 0.0
# 测试模型
correct = 0
total = 0
with torch.no_grad():
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: %d %%' % (100 * correct / total))
```
在上述代码中,我们首先定义了数据预处理和增强方式,然后加载了CIFAR-10数据集,并定义了数据加载器。接着,我们定义了ResetNet模型、优化器和损失函数,并将模型移动到GPU上进行加速。然后,我们进行了10个epoch的模型训练,并在每个epoch结束时输出训练损失。最后,我们使用测试数据集对模型进行测试,并计算模型的准确率。
你可以根据自己的需求和数据集特点,修改和调整ResetNet的超参数,并使用其他数据集对模型进行训练和测试。
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