keras模型如何保存为tensorflow的二进制模型

# 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))

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)

# -*- 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))

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()