利用FGNET数据集对这个模型的训练代码

这里是一个基于FGNET数据集对上述模型进行训练的代码示例：

import os
import numpy as np
from tensorflow.keras.preprocessing.image import ImageDataGenerator
from tensorflow.keras.optimizers import Adam
from tensorflow.keras.callbacks import EarlyStopping, ModelCheckpoint
from age_model import Age_Model # 假设Age_Model类定义在age_model.py文件中

# 数据集路径
data_dir = 'path/to/FGNET'
train_dir = os.path.join(data_dir, 'train')
val_dir = os.path.join(data_dir, 'val')

# 模型参数
input_shape = (224, 224, 3)
batch_size = 32
epochs = 50
lr = 1e-4

# 数据增强
train_datagen = ImageDataGenerator(
    rescale=1./255,
    rotation_range=20,
    width_shift_range=0.2,
    height_shift_range=0.2,
    zoom_range=0.2,
    horizontal_flip=True
)

val_datagen = ImageDataGenerator(rescale=1./255)

train_generator = train_datagen.flow_from_directory(
    train_dir,
    target_size=input_shape[:2],
    batch_size=batch_size,
    class_mode='categorical',
    shuffle=True
)

val_generator = val_datagen.flow_from_directory(
    val_dir,
    target_size=input_shape[:2],
    batch_size=batch_size,
    class_mode='categorical',
    shuffle=False
)

# 创建模型
model = Age_Model().model
model.compile(loss='categorical_crossentropy', optimizer=Adam(lr=lr), metrics=['accuracy'])

# 训练模型
callbacks = [
    EarlyStopping(patience=10, verbose=1),
    ModelCheckpoint('age_model_best.h5', save_best_only=True, verbose=1)
]

history = model.fit(
    train_generator,
    steps_per_epoch=train_generator.n//batch_size,
    epochs=epochs,
    validation_data=val_generator,
    validation_steps=val_generator.n//batch_size,
    callbacks=callbacks
)

# 保存模型
model.save_weights('age_model_weights.h5')

在训练时，我们使用ImageDataGenerator进行数据增强，以增加数据集的样本多样性。对于模型的训练，我们使用categorical_crossentropy作为损失函数，Adam作为优化器，并监控模型的准确率。在训练过程中，我们使用EarlyStopping和ModelCheckpoint两个回调函数，分别用于早期停止和保存最佳模型。最后，我们保存训练好的模型权重。