按照文件的具体内容，生成完整的代码，注意，测试，训练，验证，路径的分类

根据提供的文档内容，以下是使用Python和TensorFlow/Keras框架构建桃子智能分级分选系统的完整代码示例。该代码包括数据加载、预处理、模型构建、训练、验证和测试等步骤。

import os
import json
import numpy as np
import pandas as pd
from tensorflow.keras.preprocessing.image import ImageDataGenerator
from tensorflow.keras.applications import MobileNetV2
from tensorflow.keras.layers import Dense, GlobalAveragePooling2D
from tensorflow.keras.models import Model
from tensorflow.keras.optimizers import Adam
from sklearn.metrics import accuracy_score, f1_score

# 数据路径
train_dir = "C:\\Users\\24067\\Desktop\\peach_split\\train"
val_dir = "C:\\Users\\24067\\Desktop\\peach_split\\val"
test_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"

# 加载标签数据
with open(train_label_path, 'r') as f:
    train_labels = json.load(f)

with open(val_label_path, 'r') as f:
    val_labels = json.load(f)

# 将标签转换为DataFrame
train_df = pd.DataFrame(list(train_labels.items()), columns=['filename', 'label'])
val_df = pd.DataFrame(list(val_labels.items()), columns=['filename', 'label'])

# 标签编码
label_mapping = {'特级': 0, '一级': 1, '二级': 2, '三级': 3}
train_df['label'] = train_df['label'].map(label_mapping)
val_df['label'] = val_df['label'].map(label_mapping)

# 图像数据生成器
datagen = ImageDataGenerator(rescale=1./255)

train_generator = datagen.flow_from_dataframe(
    dataframe=train_df,
    directory=train_dir,
    x_col="filename",
    y_col="label",
    target_size=(224, 224),
    batch_size=32,
    class_mode='sparse'
)

val_generator = datagen.flow_from_dataframe(
    dataframe=val_df,
    directory=val_dir,
    x_col="filename",
    y_col="label",
    target_size=(224, 224),
    batch_size=32,
    class_mode='sparse',
    shuffle=False
)

# 构建模型
base_model = MobileNetV2(weights='imagenet', include_top=False, input_shape=(224, 224, 3))
x = base_model.output
x = GlobalAveragePooling2D()(x)
x = Dense(1024, activation='relu')(x)
predictions = Dense(4, activation='softmax')(x)
model = Model(inputs=base_model.input, outputs=predictions)

# 冻结基础模型的层
for layer in base_model.layers:
    layer.trainable = False

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

# 训练模型
model.fit(train_generator, epochs=10, validation_data=val_generator)

# 验证模型
val_predictions = model.predict(val_generator)
val_predictions = np.argmax(val_predictions, axis=1)

# 计算验证集的正确率和F1值
val_accuracy = accuracy_score(val_generator.classes, val_predictions)
val_f1_special = f1_score(val_generator.classes, val_predictions, average=None)[0]

print(f'Validation Accuracy: {val_accuracy}')
print(f'F1 Score for Special Grade: {val_f1_special}')

# 测试集预测
test_datagen = ImageDataGenerator(rescale=1./255)
test_generator = test_datagen.flow_from_directory(
    test_dir,
    target_size=(224, 224),
    batch_size=32,
    class_mode=None,
    shuffle=False
)

test_predictions = model.predict(test_generator)
test_predictions = np.argmax(test_predictions, axis=1)

# 提交文件准备
submission = pd.DataFrame({'filename': test_generator.filenames, 'label': test_predictions})
submission['label'] = submission['label'].map({v: k for k, v in label_mapping.items()})
submission.to_csv('sample_submission.csv', index=False)

### 代码说明：
1. **数据路径**：定义了训练、验证和测试数据集的路径以及标签文件的路径。
2. **加载标签数据**：从JSON文件中加载训练和验证集的标签，并将其转换为Pandas DataFrame。
3. **标签编码**：将文本标签映射到数值标签。
4. **图像数据生成器**：使用`ImageDataGenerator`生成训练和验证数据流。
5. **构建模型**：使用MobileNetV2作为基础模型，添加全局平均池化层和全连接层，构建最终的模型。
6. **冻结基础模型的层**：冻结MobileNetV2的层，只训练顶部的自定义层。
7. **编译模型**：使用Adam优化器和稀疏分类交叉熵损失函数编译模型。
8. **训练模型**：使用训练数据流训练模型，并在验证数据流上进行验证。
9. **验证模型**：计算验证集上的正确率和特级桃的F1值。
10. **测试集预测**：对测试集进行预测，并生成提交文件。

希望这段代码能帮助你完成任务！如果有任何问题或需要进一步的帮助，请随时告诉我。