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keras模型如何保存为tensorflow的二进制模型-创新互联

这篇文章主要讲解了keras模型如何保存为tensorflow的二进制模型，内容清晰明了，对此有兴趣的小伙伴可以学习一下，相信大家阅读完之后会有帮助。

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最近需要将使用keras训练的模型移植到手机上使用， 因此需要转换到tensorflow的二进制模型。

折腾一下午，终于找到一个合适的方法，废话不多说，直接上代码：

# 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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