這篇文章主要講解了keras模型如何保存為tensorflow的二進制模型，內容清晰明了，對此有興趣的小伙伴可以學習一下，相信大家閱讀完之后會有幫助。

最近需要將使用keras訓練的模型移植到手機上使用， 因此需要轉換到tensorflow的二進制模型。

折騰一下午，終于找到一個合適的方法，廢話不多說，直接上代碼：

# coding=utf-8
import sys

from keras.models import load_model
import tensorflow as tf
import os
import os.path as osp
from keras import backend as K

def freeze_session(session, keep_var_names=None, output_names=None, clear_devices=True):
 """
 Freezes the state of a session into a prunned 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
 prunned so subgraphs that are not neccesary 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()]
  input_graph_def = graph.as_graph_def()
  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

input_fld = sys.path[0]
weight_file = 'your_model.h6'
output_graph_name = 'tensor_model.pb'

output_fld = input_fld + '/tensorflow_model/'
if not os.path.isdir(output_fld):
 os.mkdir(output_fld)
weight_file_path = osp.join(input_fld, weight_file)

K.set_learning_phase(0)
net_model = load_model(weight_file_path)

print('input is :', net_model.input.name)
print ('output is:', net_model.output.name)

sess = K.get_session()

frozen_graph = freeze_session(K.get_session(), output_names=[net_model.output.op.name])

from tensorflow.python.framework import graph_io

graph_io.write_graph(frozen_graph, output_fld, output_graph_name, as_text=False)

print('saved the constant graph (ready for inference) at: ', osp.join(output_fld, output_graph_name))

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