引入VGG16训练模型，并在代码中做出注释

好的，我将为您修改代码以引入 VGG16 模型，并添加相应的注释。以下是修改后的代码：

import os
import json
import numpy as np
import pandas as pd
from PIL import Image
import tensorflow as tf
from sklearn.model_selection import train_test_split
from tensorflow.keras.applications.vgg16 import VGG16
from tensorflow.keras.models import Model
from tensorflow.keras.layers import Dense, GlobalAveragePooling2D, Dropout
from tensorflow.keras.preprocessing.image import ImageDataGenerator
from tensorflow.keras.optimizers import Adam
from sklearn.metrics import f1_score, accuracy_score

# 加载数据集
def load_data(data_dir, label_path):
    # 读取标签文件
    with open(label_path, 'r') as f:
        labels_list = json.load(f)
    
    # 将标签文件转换为字典格式
    labels = {item['文件名']: item['标签'] for item in labels_list if '文件名' in item and '标签' in item}
    
    images = []
    targets = []
    
    # 遍历所有图像文件并加载图像
    for file_name, label in labels.items():
        img_path = os.path.join(data_dir, file_name)
        if not os.path.exists(img_path):
            continue
        img = Image.open(img_path).resize((224, 224))  # VGG16 输入尺寸为 224x224
        img_array = np.array(img) / 255.0  # 归一化图像像素值
        images.append(img_array)
        targets.append(label)
    
    if len(images) == 0:
        raise ValueError("No valid images found.")
    
    return np.array(images), np.array(targets)

# 数据目录和标签路径
data_dir = "C:\\Users\\24067\\Desktop\\peach_split\\train"
label_path = "C:\\Users\\24067\\Desktop\\train_label.json"

# 加载训练数据
images, labels = load_data(data_dir, label_path)

# 标签映射
label_map = {'特级': 3, '一级': 2, '二级': 1, '三级': 0}
labels = np.array([label_map[label] for label in labels])

# 划分训练集和验证集
X_train, X_val, y_train, y_val = train_test_split(images, labels, test_size=0.2, random_state=42)

# 创建 VGG16 模型
def create_vgg16_model(input_shape=(224, 224, 3)):
    # 加载预训练的 VGG16 模型，不包括顶层分类器
    base_model = VGG16(weights='imagenet', include_top=False, input_shape=input_shape)
    
    # 冻结基础模型的权重
    for layer in base_model.layers:
        layer.trainable = False
    
    # 添加自定义顶层分类器
    x = base_model.output
    x = GlobalAveragePooling2D()(x)
    x = Dense(1024, activation='relu')(x)
    x = Dropout(0.5)(x)
    predictions = Dense(4, activation='softmax')(x)
    
    # 构建新的模型
    model = Model(inputs=base_model.input, outputs=predictions)
    
    return model

# 实例化模型
model = create_vgg16_model()

# 编译模型
model.compile(optimizer=Adam(learning_rate=0.001), loss='sparse_categorical_crossentropy', metrics=['accuracy'])

# 图像增强
datagen = ImageDataGenerator(
    rotation_range=20,  # 随机旋转角度
    width_shift_range=0.2,  # 水平偏移范围
    height_shift_range=0.2,  # 垂直偏移范围
    horizontal_flip=True,  # 水平翻转
    vertical_flip=True  # 垂直翻转
)

# 训练模型
history = model.fit(datagen.flow(X_train, y_train, batch_size=32), epochs=1, validation_data=(X_val, y_val))

# 评估模型
def evaluate_model(model, X, y):
    predictions = model.predict(X)
    predicted_labels = np.argmax(predictions, axis=1)
    accuracy = accuracy_score(y, predicted_labels)
    f1 = f1_score(y, predicted_labels, average='weighted')
    print(f'Accuracy: {accuracy:.4f}')
    print(f'F1 Score: {f1:.4f}')
    return accuracy, f1

evaluate_model(model, X_val, y_val)

# 预测测试集并保存结果
def predict_and_save(test_data_dir, test_label_path, output_path):
    test_images = []
    test_file_names = []
    
    # 读取测试集标签文件
    with open(test_label_path, 'r') as f:
        test_labels_list = json.load(f)
    test_labels = {item['文件名']: item['标签'] for item in test_labels_list if '文件名' in item and '标签' in item}
    
    # 加载测试集图像
    for file_name in test_labels.keys():
        img_path = os.path.join(test_data_dir, file_name)
        if not os.path.exists(img_path):
            continue
        img = Image.open(img_path).resize((224, 224))  # VGG16 输入尺寸为 224x224
        img_array = np.array(img) / 255.0
        test_images.append(img_array)
        test_file_names.append(file_name)
    
    test_images = np.array(test_images)
    predictions = model.predict(test_images)
    predicted_labels = np.argmax(predictions, axis=1)
    
    # 反向映射标签
    label_map_inv = {v: k for k, v in label_map.items()}
    predicted_labels = [label_map_inv[label] for label in predicted_labels]
    
    # 生成提交文件
    submission_df = pd.DataFrame({'文件名': test_file_names, '标签': predicted_labels})
    submission_df.to_csv(output_path, index=False)

# 测试集目录和标签路径
test_data_dir = "C:\\Users\\24067\\Desktop\\peach_split\\test"
test_label_path = "C:\\Users\\24067\\Desktop\\train_label.json"
output_path = "C:\\Users\\24067\\Desktop\\data\\submission.csv"

# 进行预测并保存结果
predict_and_save(test_data_dir, test_label_path, output_path)

### 主要改动点：
1. **调整图像尺寸**：VGG16 的输入尺寸为 `224x224`，因此需要将图像调整为该尺寸。
2. **创建 VGG16 模型**：使用 `VGG16` 预训练模型，并冻结其权重，然后在其基础上添加自定义的顶层分类器。
3. **编译和训练模型**：保持原有的编译和训练步骤不变。

希望这些修改对您有帮助！如果您有任何进一步的问题，请随时告诉我。