合并多个tensorflow模型的办法

直接上代码：

import tensorflow as tf
from tensorflow.python.tools import freeze_graph
from tensorflow.python.framework.graph_util import convert_variables_to_constants
import os
import numpy as np

filename1 = "model_a.pb"
filename2 = "model_b.pb"

def load_graphdef(filename):
	with tf.gfile.GFile(filename, "rb") as f:
		graph_def = tf.GraphDef()
		graph_def.ParseFromString(f.read())
	return graph_def

def load_graph(graph_def, prefix):

	with tf.Graph().as_default() as graph:
		tf.import_graph_def(graph_def, name=prefix) 

	return graph

graph1 = load_graphdef(filename1)
graph2 = load_graphdef(filename2)

graph1_out, = tf.import_graph_def(graph1, return_elements=['mode_a_output:0'], name="model_a")
graph2_out, = tf.import_graph_def(graph2, return_elements=['mode_b_output:0'], name="model_b")

z = tf.concat([graph1_out,  graph2_out], 1)

tf.identity(z, "merge_output")	

init_op = tf.global_variables_initializer()
with tf.Session() as sess:
	sess.run(init_op)
	graph = convert_variables_to_constants(sess, sess.graph_def, ["merge_output"])
	tf.train.write_graph(graph, '.', 'merge.pb', as_text=False)

合并后的pb文件，输入节点为原来输入节点的并集。和原模型输入的区别是：输入节点分别增加的对应的前缀model_a/, model_b/。