花卉分类resnet

### 使用ResNet实现花卉图像分类

#### 准备工作
为了使用ResNet进行花卉分类，需先准备环境并加载必要的库。确保安装了PyTorch和其他依赖项。

pip install torch torchvision matplotlib numpy pandas scikit-learn

#### 数据预处理
数据集通常需要经过标准化和增强来提高模型泛化能力。对于224×224大小的图片，在训练过程中应用随机裁剪、水平翻转等操作可以增加样本多样性[^1]。

from torchvision import transforms

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
假设已下载好Flowers17或类似的花卉数据集，并将其分为训练集和验证集两个部分。

import os
from torchvision.datasets.folder import ImageFolder
from torch.utils.data import DataLoader

image_datasets = {x: 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']}
dataset_sizes = {x: len(image_datasets[x]) for x in ['train', 'val']}
class_names = image_datasets['train'].classes

#### 定义模型
采用预训练过的ResNet作为基础网络，冻结其参数以利用迁移学习的优势，仅微调最后几层适应新的类别数量。

import torch.nn as nn
from torchvision.models.resnet import resnet50

model_conv = resnet50(pretrained=True)
for param in model_conv.parameters():
    param.requires_grad = False
    
num_ftrs = model_conv.fc.in_features
model_conv.fc = nn.Linear(num_ftrs, len(class_names))
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model_conv = model_conv.to(device)

#### 设置优化器与损失函数
选择合适的优化算法（如SGD），设置初始学习率为0.001，并定义交叉熵损失用于多类别的预测任务。

criterion = nn.CrossEntropyLoss()
optimizer_ft = optim.SGD(model_conv.fc.parameters(), lr=0.001, momentum=0.9)

exp_lr_scheduler = lr_scheduler.StepLR(optimizer_ft, step_size=7, gamma=0.1)

#### 开始训练过程
通过循环迭代整个数据集多次来进行训练，期间记录下每次epoch的最佳性能指标以便后续评估。

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)

        # Each epoch has a training and validation phase
        for phase in ['train', 'val']:
            if phase == 'train':
                model.train()  
            else:
                model.eval()

            running_loss = 0.0
            running_corrects = 0

            # Iterate over data.
            for inputs, labels in dataloaders[phase]:
                inputs = inputs.to(device)
                labels = labels.to(device)

                # zero the parameter gradients
                optimizer.zero_grad()

                # forward
                with torch.set_grad_enabled(phase == 'train'):
                    outputs = model(inputs)
                    _, preds = torch.max(outputs, 1)
                    loss = criterion(outputs, labels)

                    # backward + optimize only if in training phase
                    if phase == 'train':
                        loss.backward()
                        optimizer.step()

                # statistics
                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('{} Loss: {:.4f} Acc: {:.4f}'.format(
                phase, epoch_loss, epoch_acc))

            # deep copy the model
            if phase == 'val' and epoch_acc > best_acc:
                best_acc = epoch_acc
                best_model_wts = copy.deepcopy(model.state_dict())

        print()

    print('Best val Acc: {:4f}'.format(best_acc))
    
    # load best model weights
    model.load_state_dict(best_model_wts)
    return model

执行上述代码片段即可启动训练流程，最终获得针对特定花卉种类定制化的ResNet模型实例[^3]。