keras模型保存为tensorflow的二进制模型

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我是靠谱客的博主细腻钢笔，最近开发中收集的这篇文章主要介绍keras模型保存为tensorflow的二进制模型，觉得挺不错的，现在分享给大家，希望可以做个参考。

概述

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)