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脚本之家。