铁雪资源网 Design By www.gsvan.com

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.h5'
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))

上面代码实现保存到当前目录的tensor_model目录下。

验证:

import tensorflow as tf
import numpy as np
import PIL.Image as Image
import cv2

def recognize(jpg_path, pb_file_path):
 with tf.Graph().as_default():
  output_graph_def = tf.GraphDef()

  with open(pb_file_path, "rb") as f:
   output_graph_def.ParseFromString(f.read())
   tensors = tf.import_graph_def(output_graph_def, name="")
   print tensors

  with tf.Session() as sess:
   init = tf.global_variables_initializer()
   sess.run(init)

   op = sess.graph.get_operations()
   
   for m in op:
    print(m.values())

   input_x = sess.graph.get_tensor_by_name("convolution2d_1_input:0") #具体名称看上一段代码的input.name
   print input_x

   out_softmax = sess.graph.get_tensor_by_name("activation_4/Softmax:0") #具体名称看上一段代码的output.name

   print out_softmax

   img = cv2.imread(jpg_path, 0)
   img_out_softmax = sess.run(out_softmax,
          feed_dict={input_x: 1.0 - np.array(img).reshape((-1,28, 28, 1)) / 255.0})

   print "img_out_softmax:", img_out_softmax
   prediction_labels = np.argmax(img_out_softmax, axis=1)
   print "label:", prediction_labels

pb_path = 'tensorflow_model/constant_graph_weights.pb'
img = 'test/6/8_48.jpg'
recognize(img, pb_path)

补充知识:如何将keras训练好的模型转换成tensorflow的.pb的文件并在TensorFlow serving环境调用

首先keras训练好的模型通过自带的model.save()保存下来是 .model (.h5) 格式的文件

模型载入是通过 my_model = keras . models . load_model( filepath )

要将该模型转换为.pb 格式的TensorFlow 模型,代码如下:

# -*- coding: utf-8 -*-
from keras.layers.core import Activation, Dense, Flatten
from keras.layers.embeddings import Embedding
from keras.layers.recurrent import LSTM
from keras.layers import Dropout
from keras.layers.wrappers import Bidirectional
from keras.models import Sequential,load_model
from keras.preprocessing import sequence
from sklearn.model_selection import train_test_split
import collections
from collections import defaultdict
import jieba
import numpy as np
import sys
reload(sys)
sys.setdefaultencoding('utf-8')
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):
 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 = '/data/codebase/Keyword-fenci/brand_recogniton_biLSTM/'
weight_file = 'biLSTM_brand_recognize.model'
output_graph_name = 'tensor_model_v3.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=True)

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

然后模型就存成了.pb格式的文件

问题就来了,这样存下来的.pb格式的文件是frozen model

如果通过TensorFlow serving 启用模型的话,会报错:

E tensorflow_serving/core/aspired_versions_manager.cc:358] Servable {name: mnist version: 1} cannot be loaded: Not found: Could not find meta graph def matching supplied tags: { serve }. To inspect available tag-sets in the SavedModel, please use the SavedModel CLI: `saved_model_cli`

因为TensorFlow serving 希望读取的是saved model

于是需要将frozen model 转化为 saved model 格式,解决方案如下:

from tensorflow.python.saved_model import signature_constants
from tensorflow.python.saved_model import tag_constants

export_dir = '/data/codebase/Keyword-fenci/brand_recogniton_biLSTM/saved_model'
graph_pb = '/data/codebase/Keyword-fenci/brand_recogniton_biLSTM/tensorflow_model/tensor_model.pb'

builder = tf.saved_model.builder.SavedModelBuilder(export_dir)

with tf.gfile.GFile(graph_pb, "rb") as f:
 graph_def = tf.GraphDef()
 graph_def.ParseFromString(f.read())

sigs = {}

with tf.Session(graph=tf.Graph()) as sess:
 # name="" is important to ensure we don't get spurious prefixing
 tf.import_graph_def(graph_def, name="")
 g = tf.get_default_graph()
 inp = g.get_tensor_by_name(net_model.input.name)
 out = g.get_tensor_by_name(net_model.output.name)

 sigs[signature_constants.DEFAULT_SERVING_SIGNATURE_DEF_KEY] =   tf.saved_model.signature_def_utils.predict_signature_def(
   {"in": inp}, {"out": out})

 builder.add_meta_graph_and_variables(sess,
           [tag_constants.SERVING],
           signature_def_map=sigs)
builder.save()

于是保存下来的saved model 文件夹下就有两个文件:

saved_model.pb variables

其中variables 可以为空

于是将.pb 模型导入serving再读取,成功!

以上这篇keras模型保存为tensorflow的二进制模型方式就是小编分享给大家的全部内容了,希望能给大家一个参考,也希望大家多多支持。

标签:
keras,模型保存,tensorflow,二进制

铁雪资源网 Design By www.gsvan.com
广告合作:本站广告合作请联系QQ:858582 申请时备注:广告合作(否则不回)
免责声明:本站文章均来自网站采集或用户投稿,网站不提供任何软件下载或自行开发的软件! 如有用户或公司发现本站内容信息存在侵权行为,请邮件告知! 858582#qq.com
铁雪资源网 Design By www.gsvan.com

评论“keras模型保存为tensorflow的二进制模型方式”

暂无keras模型保存为tensorflow的二进制模型方式的评论...

稳了!魔兽国服回归的3条重磅消息!官宣时间再确认!

昨天有一位朋友在大神群里分享,自己亚服账号被封号之后居然弹出了国服的封号信息对话框。

这里面让他访问的是一个国服的战网网址,com.cn和后面的zh都非常明白地表明这就是国服战网。

而他在复制这个网址并且进行登录之后,确实是网易的网址,也就是我们熟悉的停服之后国服发布的暴雪游戏产品运营到期开放退款的说明。这是一件比较奇怪的事情,因为以前都没有出现这样的情况,现在突然提示跳转到国服战网的网址,是不是说明了简体中文客户端已经开始进行更新了呢?