Tensorflow 使用pb文件保存(恢復)模型計算圖和參數實例詳解

constant_graph = graph_util.convert_variables_to_constants( sess, sess.graph_def , ['sum_operation'] )
with open( pbName, mode='wb') as f:
f.write(constant_graph.SerializeToString())

    graph0 = tf.GraphDef()
    with open( pbName, mode='rb') as f:
      graph0.ParseFromString( f.read() )
      tf.import_graph_def( graph0 , name = '' )

 
import tensorflow as tf 
from tensorflow.python.framework import graph_util
 
pbName = 'graphA.pb'
def graphCreate() :
  with tf.Session() as sess :
    var1 = tf.placeholder ( tf.int32 , name='var1' ) 
    var2 = tf.Variable( 20 , name='var2' )#實參name='var2'指定了操作名，該操作返回的張量名是在
                       #'var2'后面:0 ,即var2:0 是返回的張量名，也就是說變量
                       # var2的名稱是'var2:0'
    var3 = tf.Variable( 30 , name='var3' )
    var4 = tf.Variable( 40 , name='var4' )
    var4op = tf.assign( var4 , 1000 , name = 'var4op1' )
    sum = tf.Variable( 4, name='sum' )
    sum = tf.add ( var1 , var2, name = 'var1_var2' ) 
    sum = tf.add( sum , var3 , name='sum_var3' )
    sumOps = tf.add( sum , var4 , name='sum_operation' )
    oper = tf.get_default_graph().get_operations()
    with open( 'operation.csv','wt' ) as f:
      s = 'name,type,output\n'
      f.write( s ) 
      for o in oper:
        s = o.name
        s += ','+ o.type 
        inp = o.inputs
        oup = o.outputs
        for iip in inp :
          s #s += ','+ str(iip)
        for iop in oup :
          s += ',' + str(iop)
        s += '\n'
        f.write( s ) 
         
      for var in tf.global_variables():
        print('variable=> ' , var.name) #張量是tf.Variable/tf.Add之類操作的結果，
                        #張量的名字使用操作名加:0來表示
    init = tf.global_variables_initializer()
    sess.run( init )
    sess.run( var4op )
    print('sum_operation result is Tensor ' , sess.run( sumOps , feed_dict={var1:1}) )
 
    constant_graph = graph_util.convert_variables_to_constants( sess, sess.graph_def , ['sum_operation'] )
    with open( pbName, mode='wb') as f:
      f.write(constant_graph.SerializeToString())
 
def graphGet() :
  print("start get:" )
  with tf.Graph().as_default():
    graph0 = tf.GraphDef()
    with open( pbName, mode='rb') as f:
      graph0.ParseFromString( f.read() )
      tf.import_graph_def( graph0 , name = '' )
    with tf.Session() as sess :
      init = tf.global_variables_initializer()
      sess.run(init)
      v1 = sess.graph.get_tensor_by_name('var1:0' )
      v2 = sess.graph.get_tensor_by_name('var2:0' )
      v3 = sess.graph.get_tensor_by_name('var3:0' )
      v4 = sess.graph.get_tensor_by_name('var4:0' )
      
      sumTensor = sess.graph.get_tensor_by_name("sum_operation:0")
      print('sumTensor is : ' , sumTensor )
      print( sess.run( sumTensor , feed_dict={v1:1} ) ) 
  
graphCreate()
graphGet()