怎么將tensorflow 2.0的模型轉成 tf1.x 版本的pb模型

import tensorflow as tf
import os
from nets.efficientNet import *
os.environ['CUDA_VISIBLE_DEVICES'] = '-1'
# 這個代碼網上說需要加上, 如果模型里有dropout , bn層的話, 我測試過加不加結果都一樣, 保險起見還是加上吧
tf.keras.backend.set_learning_phase(0)

# 首先是定義你的模型, 這個需要和tf2.0下一毛一樣
inputs = tf.keras.Input(shape=(224, 224, 3), name='modelInput')
outputs = yourModel(inputs, training=False)
model = tf.keras.Model(inputs=inputs, outputs=outputs)
model.load_weights('save_weights.h6')
def freeze_session(session, keep_var_names=None, output_names=None, clear_devices=True):
  """
  Freezes the state of a session into a pruned computation graph.

  Creates a new computation graph where variable nodes are replaced by
  constants taking their current value in the session. The new graph will be
  pruned so subgraphs that are not necessary to compute the requested
  outputs are removed.
  @param session The TensorFlow session to be frozen.
  @param keep_var_names A list of variable names that should not be frozen,
             or None to freeze all the variables in the graph.
  @param output_names Names of the relevant graph outputs.
  @param clear_devices Remove the device directives from the graph for better portability.
  @return The frozen graph definition.
  """
  from tensorflow.python.framework.graph_util import convert_variables_to_constants
  graph = session.graph
  with graph.as_default():
    freeze_var_names = list(set(v.op.name for v in tf.global_variables()).difference(keep_var_names or []))
    output_names = output_names or []
    output_names += [v.op.name for v in tf.global_variables()]
    # Graph -> GraphDef ProtoBuf
    input_graph_def = graph.as_graph_def(add_shapes=True)
    if clear_devices:
      for node in input_graph_def.node:
        node.device = ""
    frozen_graph = convert_variables_to_constants(session, input_graph_def,
                           output_names, freeze_var_names)
    return frozen_graph

frozen_graph = freeze_session(tf.keras.backend.get_session(), output_names=[out.op.name for out in model.outputs])
tf.train.write_graph(frozen_graph, "model", "tf_model.pb", as_text=False)