写一个检验神经网络模型的代码,要求用datasets.ImageFolder的方法导入数据集,并输出正确率
时间: 2024-05-09 07:16:52 浏览: 9
好的,这是一个技术问题,我可以为您提供帮助。以下是一个检验神经网络模型的代码示例,使用datasets.ImageFolder方法导入数据集,并输出正确率:
```python
import torch
from torch.utils.data import DataLoader
from torchvision import datasets, models, transforms
# 定义数据增强
data_transforms = {
'train': transforms.Compose([
transforms.RandomResizedCrop(224),
transforms.RandomHorizontalFlip(),
transforms.ToTensor(),
transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
]),
'val': transforms.Compose([
transforms.Resize(256),
transforms.CenterCrop(224),
transforms.ToTensor(),
transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
])
}
# 加载数据集
data_dir = 'path/to/image/folder'
image_datasets = {x: datasets.ImageFolder(
os.path.join(data_dir, x), data_transforms[x]) for x in ['train', 'val']}
dataloaders = {x: DataLoader(
image_datasets[x], batch_size=4, shuffle=True, num_workers=4) for x in ['train', 'val']}
# 加载模型
model = models.resnet18(pretrained=True)
num_features = model.fc.in_features
model.fc = torch.nn.Linear(num_features, len(image_datasets['train'].classes))
# 设置优化器和损失函数
criterion = torch.nn.CrossEntropyLoss()
optimizer = torch.optim.SGD(model.parameters(), lr=0.001, momentum=0.9)
# 训练模型
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
model = model.to(device)
for epoch in range(10):
for images, labels in dataloaders['train']:
images = images.to(device)
labels = labels.to(device)
optimizer.zero_grad()
outputs = model(images)
loss = criterion(outputs, labels)
loss.backward()
optimizer.step()
# 在验证集上测试模型
correct = 0
total = 0
with torch.no_grad():
for images, labels in dataloaders['val']:
images = images.to(device)
labels = labels.to(device)
outputs = model(images)
_, predicted = torch.max(outputs.data, 1)
total += labels.size(0)
correct += (predicted == labels).sum().item()
print('Epoch {} Validation Accuracy: {:.2f}%'.format(epoch+1, 100. * correct / total))
```
这段代码使用了PyTorch框架,首先对数据集进行了数据增强处理,然后使用datasets.ImageFolder方法导入数据集。接着加载了一个预训练的ResNet18模型,并对其最后一层全连接层进行了重新定义,使其输出与数据集中的类别数量一致。然后定义了优化器和损失函数,并使用训练集数据对模型进行了训练。在每个训练周期中,通过验证集进行了模型的验证,并输出正确率。