由于代码一比代码二的准确率高，请仿代码一改进代码二

为了提高代码二（使用ResNet50模型）的性能，可以参考代码一中的一些优化策略。以下是改进后的代码：

### 改进后的代码二

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.resnet50 import ResNet50
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

# 定义路径常量
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"
MODEL_SAVE_PATH = "C:\\Users\\24067\\Desktop\\data\\model.h5"

# 加载数据集
def load_data(data_dir, label_path):
    with open(label_path, 'r') as f:
        labels_list = json.load(f)
    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 = []
    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))
        img_array = np.array(img) / 255.0
        images.append(img_array)
        targets.append(label)
    if len(images) == 0:
        raise ValueError("No valid images found.")
    return np.array(images), np.array(targets)

# 加载训练集和验证集
X_train, y_train = load_data(TRAIN_DATA_DIR, TRAIN_LABEL_PATH)
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_resnet_model(input_shape=(128, 128, 3)):
    base_model = ResNet50(weights=None, include_top=False, input_shape=input_shape)
    x = base_model.output
    x = GlobalAveragePooling2D()(x)
    x = Dense(256, activation='relu')(x)
    x = Dropout(0.5)(x)
    predictions = Dense(4, activation='softmax')(x)
    model = Model(inputs=base_model.input, outputs=predictions)
    return model

# 实例化模型
model = create_resnet_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,
    width_shift_range=0.2,
    height_shift_range=0.2,
    shear_range=0.15,
    zoom_range=0.15,
    horizontal_flip=True,
    vertical_flip=True,
    fill_mode='nearest'
)

# 训练模型
history = model.fit(datagen.flow(X_train, y_train, batch_size=32), epochs=10, validation_data=(X_val, y_val))

# 保存模型
model.save(MODEL_SAVE_PATH)

# 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, output_path):
    test_images = []
    test_file_names = []
    for file_name in os.listdir(test_data_dir):
        img_path = os.path.join(test_data_dir, file_name)
        if not os.path.exists(img_path) or not file_name.lower().endswith(('.png', '.jpg', '.jpeg')):
            continue
        img = Image.open(img_path).resize((128, 128))
        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)
    label_map_inv = {v: k for k, v in label_map.items()}
    predicted_labels = [label_map_inv[label] for label in predicted_labels]
    submission_df = pd.DataFrame({'文件名': test_file_names, '标签': predicted_labels})
    submission_df.to_csv(output_path, index=False)

# 进行预测并保存结果
predict_and_save(TEST_DATA_DIR, OUTPUT_PATH)

### 主要改进点

1. **学习率调整**：将学习率从 `0.0001` 调整为 `0.001`，以加快收敛速度。
2. **增加训练轮数**：将 `epochs` 从 `1` 增加到 `10`，以便模型有更多机会学习数据中的模式。
3. **图像增强参数**：保持与代码一相同的图像增强参数，以提高模型的泛化能力。

通过这些改进，希望代码二的性能能够接近或超过代码一的性能。