### 基于深度学习的水果分类 Python 源代码实现 以下是基于深度学习的水果分类系统的 Python 源代码实现，主要利用卷积神经网络 (CNN) 和 PyTorch 框架完成。该系统可以加载预训练模型并微调以适应特定的水果数据集。 #### 数据准备 在实际应用中，需要先准备好水果图片的数据集，并将其划分为训练集、验证集和测试集。常见的水果数据集包括 CIFAR-10 或自定义采集的水果图片集合[^1]。 ```python import torch from torchvision import datasets, transforms, models from torch.utils.data import DataLoader import torch.nn as nn import torch.optim as optim # 定义数据增强和标准化操作 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 = './fruits_data' # 替换为您的数据路径 image_datasets = {x: datasets.ImageFolder(f'{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 ``` #### 构建模型 这里我们使用迁移学习的方法，加载预训练的 ResNet-18 模型，并替换最后一层全连接层以适配水果分类任务。 ```python device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') model = models.resnet18(pretrained=True) for param in model.parameters(): param.requires_grad = False num_ftrs = model.fc.in_features model.fc = nn.Linear(num_ftrs, len(class_names)) # 修改最后的全连接层以匹配类别数量 model = model.to(device) criterion = nn.CrossEntropyLoss() optimizer = optim.Adam(model.fc.parameters(), lr=0.001) scheduler = optim.lr_scheduler.StepLR(optimizer, step_size=7, gamma=0.1) ``` #### 训练过程 通过循环迭代的方式对模型进行训练和评估。 ```python def train_model(model, criterion, optimizer, scheduler, num_epochs=25): best_acc = 0.0 for epoch in range(num_epochs): print(f'Epoch {epoch}/{num_epochs - 1}') print('-' * 10) for phase in ['train', 'val']: if phase == 'train': model.train() else: model.eval() running_loss = 0.0 running_corrects = 0 for inputs, labels in dataloaders[phase]: inputs = inputs.to(device) labels = labels.to(device) optimizer.zero_grad() with torch.set_grad_enabled(phase == 'train'): outputs = model(inputs) _, preds = torch.max(outputs, 1) loss = criterion(outputs, labels) if phase == 'train': loss.backward() optimizer.step() running_loss += loss.item() * inputs.size(0) running_corrects += torch.sum(preds == labels.data) if phase == 'train': scheduler.step() epoch_loss = running_loss / dataset_sizes[phase] epoch_acc = running_corrects.double() / dataset_sizes[phase] print(f'{phase} Loss: {epoch_loss:.4f} Acc: {epoch_acc:.4f}') if phase == 'val' and epoch_acc > best_acc: best_acc = epoch_acc best_model_wts = model.state_dict() print(f'Best val Acc: {best_acc:.4f}') model.load_state_dict(best_model_wts) return model model_trained = train_model(model, criterion, optimizer, scheduler, num_epochs=25) ``` #### 测试与保存模型 完成训练后，可将模型保存至本地文件以便后续部署或进一步优化。 ```python torch.save(model_trained.state_dict(), 'fruit_classifier.pth') print("Model has been saved successfully.") ``` --- ###