背景：目前keras框架使用简单，很容易上手，深得广大算法工程师的喜爱，但是当部署到客户端时，可能会出现各种各样的bug，甚至不支持使用keras，本文来解决的是将keras的h5模型转换为客户端常用的tensorflow的pb模型并使用tensorflow加载pb模型。

h5_to_pb.py

from keras.models import load_model

import tensorflow as tf

import os

import os.path as osp

from keras import backend as K

#路径参数

input_path = 'input path'

weight_file = 'weight.h5'

weight_file_path = osp.join(input_path,weight_file)

output_graph_name = weight_file[:-3] + '.pb'

#转换函数

def h5_to_pb(h5_model,output_dir,model_name,out_prefix = "output_",log_tensorboard = True):

if osp.exists(output_dir) == False:

os.mkdir(output_dir)

out_nodes = []

for i in range(len(h5_model.outputs)):

out_nodes.append(out_prefix + str(i + 1))

tf.identity(h5_model.output[i],out_prefix + str(i + 1))

sess = K.get_session()

from tensorflow.python.framework import graph_util,graph_io

init_graph = sess.graph.as_graph_def()

main_graph = graph_util.convert_variables_to_constants(sess,init_graph,out_nodes)

graph_io.write_graph(main_graph,output_dir,name = model_name,as_text = False)

if log_tensorboard:

from tensorflow.python.tools import import_pb_to_tensorboard

import_pb_to_tensorboard.import_to_tensorboard(osp.join(output_dir,model_name),output_dir)

#输出路径

output_dir = osp.join(os.getcwd(),"trans_model")

#加载模型

h5_model = load_model(weight_file_path)

h5_to_pb(h5_model,output_dir = output_dir,model_name = output_graph_name)

print('model saved')

将转换成的pb模型进行加载

load_pb.py

import tensorflow as tf

from tensorflow.python.platform import gfile

def load_pb(pb_file_path):

sess = tf.Session()

with gfile.FastGFile(pb_file_path, 'rb') as f:

graph_def = tf.GraphDef()

graph_def.ParseFromString(f.read())

sess.graph.as_default()

tf.import_graph_def(graph_def, name='')

print(sess.run('b:0'))

#输入

input_x = sess.graph.get_tensor_by_name('x:0')

input_y = sess.graph.get_tensor_by_name('y:0')

#输出

op = sess.graph.get_tensor_by_name('op_to_store:0')

#预测结果

ret = sess.run(op, {input_x: 3, input_y: 4})

print(ret)