Wang-xinyu的resnet18模型训练示例程序
时间: 2024-09-21 20:01:46 浏览: 18
Wang-xinyu的ResNet18模型训练示例通常涉及PyTorch库,这是一个广泛用于深度学习的开源框架。以下是使用Python编写的一个简化版本的训练过程:
```python
import torch
import torchvision
from torchvision import models, datasets, transforms
# 定义超参数
num_epochs = 10
batch_size = 64
learning_rate = 0.001
# 数据预处理
transform = transforms.Compose([
transforms.Resize(256),
transforms.CenterCrop(224),
transforms.ToTensor(),
transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
])
train_dataset = datasets.ImageFolder('path_to_train_data', transform=transform)
train_loader = torch.utils.data.DataLoader(train_dataset, batch_size=batch_size, shuffle=True)
# 加载预训练的ResNet18模型
model = models.resnet18(pretrained=True)
num_features = model.fc.in_features
model.fc = torch.nn.Linear(num_features, num_classes) # 假设num_classes是你的问题数据集类别数
# 模型设置为训练模式
model.train()
# 定义损失函数和优化器
criterion = torch.nn.CrossEntropyLoss()
optimizer = torch.optim.Adam(model.parameters(), lr=learning_rate)
for epoch in range(num_epochs):
for inputs, labels in train_loader:
optimizer.zero_grad() # 梯度清零
outputs = model(inputs)
loss = criterion(outputs, labels)
loss.backward() # 反向传播
optimizer.step() # 更新权重
# 训练结束后你可以保存模型,以便后续使用
torch.save(model.state_dict(), 'resnet18_trained.pth')