TensorFlow模型保存和提取的方法

# 本文件程序为配合教材及学习进度渐进进行，请按照注释分段执行 
# 执行时要注意IDE的当前工作过路径，最好每段重启控制器一次，输出结果更准确 
 
 
# Part1: 通过tf.train.Saver类实现保存和载入神经网络模型 
 
# 执行本段程序时注意当前的工作路径 
import tensorflow as tf 
 
v1 = tf.Variable(tf.constant(1.0, shape=[1]), name="v1") 
v2 = tf.Variable(tf.constant(2.0, shape=[1]), name="v2") 
result = v1 + v2 
 
saver = tf.train.Saver() 
 
with tf.Session() as sess: 
 sess.run(tf.global_variables_initializer()) 
 saver.save(sess, "Model/model.ckpt") 
 
 
# Part2: 加载TensorFlow模型的方法 
 
import tensorflow as tf 
 
v1 = tf.Variable(tf.constant(1.0, shape=[1]), name="v1") 
v2 = tf.Variable(tf.constant(2.0, shape=[1]), name="v2") 
result = v1 + v2 
 
saver = tf.train.Saver() 
 
with tf.Session() as sess: 
 saver.restore(sess, "./Model/model.ckpt") # 注意此处路径前添加"./" 
 print(sess.run(result)) # [ 3.] 
 
 
# Part3: 若不希望重复定义计算图上的运算，可直接加载已经持久化的图 
 
import tensorflow as tf 
 
saver = tf.train.import_meta_graph("Model/model.ckpt.meta") 
 
with tf.Session() as sess: 
 saver.restore(sess, "./Model/model.ckpt") # 注意路径写法 
 print(sess.run(tf.get_default_graph().get_tensor_by_name("add:0"))) # [ 3.] 
 
 
# Part4： tf.train.Saver类也支持在保存和加载时给变量重命名 
 
import tensorflow as tf 
 
# 声明的变量名称name与已保存的模型中的变量名称name不一致 
u1 = tf.Variable(tf.constant(1.0, shape=[1]), name="other-v1") 
u2 = tf.Variable(tf.constant(2.0, shape=[1]), name="other-v2") 
result = u1 + u2 
 
# 若直接生命Saver类对象，会报错变量找不到 
# 使用一个字典dict重命名变量即可，{"已保存的变量的名称name": 重命名变量名} 
# 原来名称name为v1的变量现在加载到变量u1（名称name为other-v1）中 
saver = tf.train.Saver({"v1": u1, "v2": u2}) 
 
with tf.Session() as sess: 
 saver.restore(sess, "./Model/model.ckpt") 
 print(sess.run(result)) # [ 3.] 
 
 
# Part5: 保存滑动平均模型 
 
import tensorflow as tf 
 
v = tf.Variable(0, dtype=tf.float32, name="v") 
for variables in tf.global_variables(): 
 print(variables.name) # v:0 
 
ema = tf.train.ExponentialMovingAverage(0.99) 
maintain_averages_op = ema.apply(tf.global_variables()) 
for variables in tf.global_variables(): 
 print(variables.name) # v:0 
       # v/ExponentialMovingAverage:0 
 
saver = tf.train.Saver() 
 
with tf.Session() as sess: 
 sess.run(tf.global_variables_initializer()) 
 sess.run(tf.assign(v, 10)) 
 sess.run(maintain_averages_op) 
 saver.save(sess, "Model/model_ema.ckpt") 
 print(sess.run([v, ema.average(v)])) # [10.0, 0.099999905] 
 
 
# Part6: 通过变量重命名直接读取变量的滑动平均值 
 
import tensorflow as tf 
 
v = tf.Variable(0, dtype=tf.float32, name="v") 
saver = tf.train.Saver({"v/ExponentialMovingAverage": v}) 
 
with tf.Session() as sess: 
 saver.restore(sess, "./Model/model_ema.ckpt") 
 print(sess.run(v)) # 0.0999999 
 
 
# Part7: 通过tf.train.ExponentialMovingAverage的variables_to_restore()函数获取变量重命名字典 
 
import tensorflow as tf 
 
v = tf.Variable(0, dtype=tf.float32, name="v") 
# 注意此处的变量名称name一定要与已保存的变量名称一致 
ema = tf.train.ExponentialMovingAverage(0.99) 
print(ema.variables_to_restore()) 
# {'v/ExponentialMovingAverage': <tf.Variable 'v:0' shape=() dtype=float32_ref>} 
# 此处的v取自上面变量v的名称name="v" 
 
saver = tf.train.Saver(ema.variables_to_restore()) 
 
with tf.Session() as sess: 
 saver.restore(sess, "./Model/model_ema.ckpt") 
 print(sess.run(v)) # 0.0999999 
 
 
# Part8: 通过convert_variables_to_constants函数将计算图中的变量及其取值通过常量的方式保存于一个文件中 
 
import tensorflow as tf 
from tensorflow.python.framework import graph_util 
 
v1 = tf.Variable(tf.constant(1.0, shape=[1]), name="v1") 
v2 = tf.Variable(tf.constant(2.0, shape=[1]), name="v2") 
result = v1 + v2 
 
with tf.Session() as sess: 
 sess.run(tf.global_variables_initializer()) 
 # 导出当前计算图的GraphDef部分，即从输入层到输出层的计算过程部分 
 graph_def = tf.get_default_graph().as_graph_def() 
 output_graph_def = graph_util.convert_variables_to_constants(sess, 
              graph_def, ['add']) 
 
 with tf.gfile.GFile("Model/combined_model.pb", 'wb') as f: 
  f.write(output_graph_def.SerializeToString()) 
 
 
# Part9: 载入包含变量及其取值的模型 
 
import tensorflow as tf 
from tensorflow.python.platform import gfile 
 
with tf.Session() as sess: 
 model_filename = "Model/combined_model.pb" 
 with gfile.FastGFile(model_filename, 'rb') as f: 
  graph_def = tf.GraphDef() 
  graph_def.ParseFromString(f.read()) 
 
 result = tf.import_graph_def(graph_def, return_elements=["add:0"]) 
 print(sess.run(result)) # [array([ 3.], dtype=float32)]