如何将带有resnet101的keras模型导出到使用exporter.export_saved_model方法的tensorflow服务

from tensorflow.keras.applications import ResNet101
from tensorflow.keras.models import Model
from tensorflow.keras.layers import Dense

# 加载ResNet101模型
base_model = ResNet101(weights='imagenet', include_top=False, input_shape=(224, 224, 3))

# 添加自定义的全连接层
x = base_model.output
x = Dense(1024, activation='relu')(x)
predictions = Dense(num_classes, activation='softmax')(x)

# 构建完整的模型
model = Model(inputs=base_model.input, outputs=predictions)

# 编译模型
model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])

# 进行模型训练
model.fit(x_train, y_train, epochs=10, batch_size=32)

import tensorflow as tf
from tensorflow.keras import backend as K

# 定义导出路径
export_path = '/path/to/exported_model'

# 创建一个新的TensorFlow会话
with tf.compat.v1.Session(graph=tf.compat.v1.get_default_graph()) as sess:
    # 导出模型
    tf.compat.v1.saved_model.simple_save(
        sess,
        export_path,
        inputs={'input_image': model.input},
        outputs={t.name: t for t in model.outputs}
    )

tensorflow_model_server --port=8501 --model_name=resnet101 --model_base_path=/path/to/exported_model