python - Keras：以适合 Google Cloud ML Engine 的格式保存模型（缺少功能）

Question

我正在尝试在 Google Cloud ML Engine 上部署最近训练的 Keras 模型。我四处搜索以查看保存的模型对于 ML Engine 需要采用什么格式，然后发现：

import keras.backend as K
import tensorflow as tf
from keras.models import load_model, Sequential
from tensorflow.python.saved_model import builder as saved_model_builder
from tensorflow.python.saved_model import tag_constants, signature_constants
from tensorflow.python.saved_model.signature_def_utils_impl import predict_signature_def

# reset session
K.clear_session()
sess = tf.Session()
K.set_session(sess)

# disable loading of learning nodes
K.set_learning_phase(0)

# load model
model = load_model('model.h5')
config = model.get_config()
weights = model.get_weights()
new_Model = Sequential.from_config(config)
new_Model.set_weights(weights)

# export saved model
export_path = 'YOUR_EXPORT_PATH' + '/export'
builder = saved_model_builder.SavedModelBuilder(export_path)

signature = predict_signature_def(inputs={'NAME_YOUR_INPUT': new_Model.input},
                                  outputs={'NAME_YOUR_OUTPUT': new_Model.output})

with K.get_session() as sess:
    builder.add_meta_graph_and_variables(sess=sess,
                                         tags=[tag_constants.SERVING],
                                         signature_def_map={
                                             signature_constants.DEFAULT_SERVING_SIGNATURE_DEF_KEY: signature})
    builder.save()

但是，在 Keras 2.1.3 中，keras.backend似乎不再具有clear_session(), set_session(), 或get_session(). 处理这个问题的现代方法是什么？这些功能现在是否存在于其他地方？

谢谢！

标签： pythontensorflowkeras