tensorflow学习笔记1：导出和加载模型

用一个非常简单的例子学习导出和加载模型；

导出

写一个y=a*x+b的运算，然后保存graph；

import tensorflow as tf
from tensorflow.python.framework.graph_util import convert_variables_to_constants

with tf.Session() as sess:
    a = tf.Variable(5.0, name='a')
    x = tf.Variable(6.0, name='x')
    b = tf.Variable(3.0, name='b')
    y = tf.add(tf.multiply(a,x),b, name="y")

    tf.global_variables_initializer().run()
    
    print (a.eval()) # 5.0
    print (x.eval()) # 6.0
    print (b.eval()) # 3.0
    print (y.eval()) # 33.0

    graph = convert_variables_to_constants(sess, sess.graph_def, ["y"])
    #writer = tf.summary.FileWriter("logs/", graph)
    tf.train.write_graph(graph, 'models/', 'test_graph.pb', as_text=False)

运行

在models目录下生成了test_graph.pb；

注：convert_variables_to_constants操作是将模型参数froze（保存）进graph中，这时的graph相当于是sess.graph_def + checkpoint，即有模型结构也有模型参数；

加载

只加载，获取各个变量的值

import tensorflow as tf
from tensorflow.python.platform import gfile

with gfile.FastGFile("models/test_graph.pb", 'rb') as f:
    graph_def = tf.GraphDef()
    graph_def.ParseFromString(f.read())
    output = tf.import_graph_def(graph_def, return_elements=['a:0', 'x:0', 'b:0','y:0'])
    #print(output)
    
with tf.Session() as sess:
    result = sess.run(output)
    print (result)

运行看以看到原本保存的结果（因为几个变量都已经带入模型，又从模型中加载了出来）