按照文件的内容修改代码，给出修改后完整代码

以下是根据您提供的路径信息修改后的完整代码：

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.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

# 定义数据路径
train_data_dir = "C:\\Users\\24067\\Desktop\\peach_split\\train"
val_data_dir = "C:\\Users\\24067\\Desktop\\peach_split\\val"
test_data_dir = "C:\\Users\\24067\\Desktop\\peach_split\\test"

train_label_path = "C:\\Users\\24067\\Desktop\\train_label.json"
val_label_path = "C:\\Users\\24067\\Desktop\\val_label.json"
output_path = "C:\\Users\\24067\\Desktop\\data\\submission.csv"

# 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)

# 加载训练数据
X_train, y_train = load_data(train_data_dir, train_label_path)

# 加载验证数据
X_val, y_val = 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}
y_train = np.array([label_map[label] for label in y_train])
y_val = np.array([label_map[label] for label in y_val])

# 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