按照文件的内容修改代码

好的，我将根据您提供的路径信息对代码进行修改，使其更清晰地显示训练集、验证集和测试集的路径。

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.models import Sequential
from tensorflow.keras.layers import Conv2D, MaxPooling2D, Flatten, Dense, 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((128, 128))  # 调整图像大小
        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
train_data_dir = "C:\\Users\\24067\\Desktop\\peach_split\\train"
train_label_path = "C:\\Users\\24067\\Desktop\\train_label.json"

val_data_dir = "C:\\Users\\24067\\Desktop\\peach_split\\val"
val_label_path = "C:\\Users\\24067\\Desktop\\val_label.json"

test_data_dir = "C:\\Users\\24067\\Desktop\\peach_split\\test"
test_label_path = "C:\\Users\\24067\\Desktop\\train_label.json"

# 加载训练数据
train_images, train_labels = load_data(train_data_dir, train_label_path)

# 加载验证数据
val_images, val_labels = load_data(val_data_dir, val_label_path)

# 标签映射
label_map = {'特级': 3, '一级': 2, '二级': 1, '三级': 0}

train_labels = np.array([label_map[label] for label in train_labels])
val_labels = np.array([label_map[label] for label in val_labels])

# 创建模型
def create_model(input_shape=(128, 128, 3)):
    model = Sequential([
        Conv2D(32, (3, 3), activation='relu', input_shape=input_shape),  # 第一个卷积层
        MaxPooling2D((2, 2)),  # 最大池化层
        Conv2D(64, (3, 3), activation='relu'),  # 第二个卷积层
        MaxPooling2D((2, 2)),  # 最大池化层
        Conv2D(128, (3, 3), activation='relu'),  # 第三个卷积层
        MaxPooling2D((2, 2)),  # 最大池化层
        Flatten(),  # 展平层
        Dense(128, activation='relu'),  # 全连接层
        Dropout(0.5),  # Dropout层
        Dense(4, activation='softmax')  # 输出层
    ])
    return model

# 实例化模型
model = create_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(train_images, train_labels, batch_size=32),
                    epochs=300,
                    validation_data=(val_images, val_labels))

# 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)