### 基于 PyTorch 的鲜花识别代码 以下是基于 PyTorch 实现的简单花卉图片分类模型示例代码。此代码展示了如何加载数据集、定义卷积神经网络 (CNN) 并训练模型。 #### 数据预处理部分 ```python import torch from torchvision import datasets, transforms from torch.utils.data import DataLoader # 定义数据变换操作 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 = './flower_photos' 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=32, shuffle=True) for x in ['train', 'val']} dataset_sizes = {x: len(image_datasets[x]) for x in ['train', 'val']} class_names = image_datasets['train'].classes ``` #### CNN 模型定义 ```python import torch.nn as nn import torch.optim as optim # 定义简单的 CNN 模型 class SimpleCNN(nn.Module): def __init__(self, num_classes=4): # 假设有 4 类花朵 super(SimpleCNN, self).__init__() self.features = nn.Sequential( nn.Conv2d(3, 64, kernel_size=3, padding=1), nn.ReLU(inplace=True), nn.MaxPool2d(kernel_size=2, stride=2), nn.Conv2d(64, 128, kernel_size=3, padding=1), nn.ReLU(inplace=True), nn.MaxPool2d(kernel_size=2, stride=2) ) self.classifier = nn.Sequential( nn.Linear(128 * 56 * 56, 256), nn.ReLU(inplace=True), nn.Dropout(p=0.5), nn.Linear(256, num_classes) ) def forward(self, x): x = self.features(x) x = x.view(x.size(0), -1) x = self.classifier(x) return x model = SimpleCNN(num_classes=len(class_names)) device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') model.to(device) criterion = nn.CrossEntropyLoss() optimizer = optim.Adam(model.parameters(), lr=0.001) ``` #### 训练过程 ```python def train_model(model, criterion, optimizer, dataloaders, dataset_sizes, num_epochs=25): model.train() best_acc = 0.0 for epoch in range(num_epochs): running_loss = 0.0 correct_preds = 0 for inputs, labels in dataloaders['train']: inputs, labels = inputs.to(device), labels.to(device) optimizer.zero_grad() outputs = model(inputs) _, preds = torch.max(outputs, 1) loss = criterion(outputs, labels) loss.backward() optimizer.step() running_loss += loss.item() * inputs.size(0) correct_preds += torch.sum(preds == labels.data) epoch_loss = running_loss / dataset_sizes['train'] epoch_acc = correct_preds.double() / dataset_sizes['train'] print(f'Epoch [{epoch}/{num_epochs}] Loss: {epoch_loss:.4f} Acc: {epoch_acc:.4f}') return model trained_model = train_model(model, criterion, optimizer, dataloaders, dataset_sizes, num_epochs=10) ``` 以上是一个完整的基于 PyTorch 的花卉图片分类代码[^3]。它涵盖了数据加载、模型构建以及训练流程的设计。 --- ###