如何将.ckpt文件转换为Hd5文件？

import tensorflow as tf
from tensorflow.keras.models import Model
from tensorflow.keras.layers import Dense, Conv2D, MaxPooling2D, Flatten

# 加载预训练的模型权重文件（.ckpt文件）
checkpoint_path = 'path/to/model.ckpt'
ckpt = tf.train.Checkpoint(model=tf.keras.Model())
ckpt.restore(checkpoint_path).expect_partial()

# 创建相应的模型结构
inputs = tf.keras.Input(shape=(32, 32, 3))
x = Conv2D(32, (3, 3), activation='relu')(inputs)
x = MaxPooling2D((2, 2))(x)
x = Flatten()(x)
outputs = Dense(10, activation='softmax')(x)
model = Model(inputs=inputs, outputs=outputs)

# 提取并赋值权重参数
for i, layer in enumerate(model.layers):
    if isinstance(layer, tf.keras.layers.Conv2D) or isinstance(layer, tf.keras.layers.Dense):
        layer.set_weights(ckpt.model.layers[i].get_weights())

# 创建新的Hd5文件来保存转换后的模型权重
h5_path = 'path/to/model.h5'

# 保存转换后的模型权重到Hd5文件中
model.save_weights(h5_path)