基于densenet121的遥感影像分类代码
时间: 2023-11-24 15:03:06 浏览: 29
基于densenet121的遥感影像分类代码可分为以下几个步骤:首先,加载待分类的遥感影像数据集,通常会包括训练集、验证集和测试集。接着,利用Python中的深度学习库例如PyTorch或TensorFlow来构建densenet121模型。然后,对加载的遥感影像数据进行预处理,包括数据增强、归一化和转换。接下来,利用构建好的densenet121模型对预处理后的数据集进行训练和验证,以调整模型参数和提高分类精度。在训练过程中,需要设置合适的损失函数、优化器和学习率等超参数。训练完成后,利用测试集对模型进行评估,得出分类的精度和准确率。最后,根据模型评估结果,可以对模型进行微调或者用于实际的遥感影像分类任务。
示例代码如下:
```python
import torch
import torchvision.models as models
import torchvision.transforms as transforms
import torchvision.datasets as datasets
import torch.nn as nn
import torch.optim as optim
# 加载数据集并进行预处理
data_transforms = 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=data_transforms)
train_loader = torch.utils.data.DataLoader(train_dataset, batch_size=32, shuffle=True, num_workers=4)
# 构建densenet121模型
model = models.densenet121(pretrained=True)
# 修改模型的全连接层
num_ftrs = model.classifier.in_features
model.classifier = nn.Linear(num_ftrs, num_classes)
# 定义损失函数和优化器
criterion = nn.CrossEntropyLoss()
optimizer = optim.SGD(model.parameters(), lr=0.001, momentum=0.9)
# 训练模型
num_epochs = 10
for epoch in range(num_epochs):
model.train()
running_loss = 0.0
for inputs, labels in train_loader:
optimizer.zero_grad()
outputs = model(inputs)
loss = criterion(outputs, labels)
loss.backward()
optimizer.step()
running_loss += loss.item()
print(f'Epoch [{epoch+1}/{num_epochs}], Loss: {running_loss/len(train_loader)}')
# 评估模型
model.eval()
correct = 0
total = 0
with torch.no_grad():
for inputs, labels in test_loader:
outputs = model(inputs)
_, predicted = torch.max(outputs, 1)
total += labels.size(0)
correct += (predicted == labels).sum().item()
print(f'Accuracy of the network on the test images: {100 * correct / total}%')
```
以上示例代码是一个简单的基于densenet121的遥感影像分类模型训练和评估过程,可以根据具体的数据集和任务需求进行进一步的定制和优化。