一、保存：
graph_util.convert_variables_to_constants 可以把当前session的计算图串行化成一个字节流（二进制），这个函数包含三个参数：参数1：当前活动的session，它含有各变量
参数2：GraphDef 对象，它描述了计算网络
参数3：Graph图中需要输出的节点的名称的列表
返回值：精简版的GraphDef 对象，包含了原始输入GraphDef和session的网络和变量信息，它的成员函数SerializeToString()可以把这些信息串行化为字节流，然后写入文件里：
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())
需要指出的是，如果原始张量（包含在参数1和参数2中的组成部分）不参与参数3指定的输出节点列表所指定的张量计算的话，这些张量将不会存在返回的GraphDef对象里，也不会被串行化写入pb文件。
二、恢复：
恢复时，创建一个GraphDef，然后从上述的文件里加载进来，接着输入到当前的session：
graph0 = tf.GraphDef()
with open( pbName, mode='rb') as f:
graph0.ParseFromString( f.read() )
tf.import_graph_def( graph0 , name = '' )
三、代码：
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' )#实参name='var2'指定了操作名，该操作返回的张量名是在
#'var2'后面:0 ,即var2:0 是返回的张量名，也就是说变量
# var2的名称是'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) #张量是tf.Variable/tf.Add之类操作的结果，
#张量的名字使用操作名加:0来表示
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()
四、保存pb函数代码里的操作名称/类型/返回的张量：
| operation name | operation type | output | ||
| var1 | Placeholder | Tensor("var1:0" | dtype=int32) | |
| var2/initial_value | Const | Tensor("var2/initial_value:0" | shape=() | dtype=int32) |
| var2 | VariableV2 | Tensor("var2:0" | shape=() | dtype=int32_ref) |
| var2/Assign | Assign | Tensor("var2/Assign:0" | shape=() | dtype=int32_ref) |
| var2/read | Identity | Tensor("var2/read:0" | shape=() | dtype=int32) |
| var3/initial_value | Const | Tensor("var3/initial_value:0" | shape=() | dtype=int32) |
| var3 | VariableV2 | Tensor("var3:0" | shape=() | dtype=int32_ref) |
| var3/Assign | Assign | Tensor("var3/Assign:0" | shape=() | dtype=int32_ref) |
| var3/read | Identity | Tensor("var3/read:0" | shape=() | dtype=int32) |
| var4/initial_value | Const | Tensor("var4/initial_value:0" | shape=() | dtype=int32) |
| var4 | VariableV2 | Tensor("var4:0" | shape=() | dtype=int32_ref) |
| var4/Assign | Assign | Tensor("var4/Assign:0" | shape=() | dtype=int32_ref) |
| var4/read | Identity | Tensor("var4/read:0" | shape=() | dtype=int32) |
| var4op1/value | Const | Tensor("var4op1/value:0" | shape=() | dtype=int32) |
| var4op1 | Assign | Tensor("var4op1:0" | shape=() | dtype=int32_ref) |
| sum/initial_value | Const | Tensor("sum/initial_value:0" | shape=() | dtype=int32) |
| sum | VariableV2 | Tensor("sum:0" | shape=() | dtype=int32_ref) |
| sum/Assign | Assign | Tensor("sum/Assign:0" | shape=() | dtype=int32_ref) |
| sum/read | Identity | Tensor("sum/read:0" | shape=() | dtype=int32) |
| var1_var2 | Add | Tensor("var1_var2:0" | dtype=int32) | |
| sum_var3 | Add | Tensor("sum_var3:0" | dtype=int32) | |
| sum_operation | Add | Tensor("sum_operation:0" | dtype=int32) |
以上这篇Tensorflow 使用pb文件保存(恢复)模型计算图和参数实例详解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。
