170615 windows 下 tensorflow1.2.0rc2 模型的保存与恢复

1. save and restore 英文教程，极力推荐
2. save and restore 中文教程，翻译地道
3. save and restore 简书教程，老版可用
4. save and restore youtube教程，视频操作
5. save and restore youtube代码)，下载运行
6. save and restore python3.6 @ windows support - 官方说明
7. Stack Over 热心解答

首先感谢Ankit答疑和Hui的热心解答，今天总算弄懂如何保存与恢复模型了！官方的解释，连个具体的例子都没看到，中间只有个框架，然后就是各种是省略号，只想说工作真心做得不好！

推荐大家看完1，7后可运行下面两个程序：
注意： 在前述参考文章里面，都没有提到新建文件夹用于保存于加载模型，这个可能与系统有关，may be 在linux下模型可以直接在程序路径下保存与加载，但是在windows下必须要新建一个单独的文件夹才行。所以在保存与加载时要指明文件夹路径。

save.py

import tensorflow as tf#Prepare to feed input, i.e. feed_dict and placeholdersw1 = tf.placeholder("float", name="w1")w2 = tf.placeholder("float", name="w2")b1= tf.Variable(2.0,name="bias")feed_dict ={w1:4,w2:8}#Define a test operation that we will restorew3 = tf.add(w1,w2)w4 = tf.multiply(w3,b1,name="op_to_restore")sess = tf.Session()sess.run(tf.global_variables_initializer())#Create a saver object which will save all the variablessaver = tf.train.Saver()#Run the operation by feeding inputprint (sess.run(w4,feed_dict))#Prints 24 which is sum of (w1+w2)*b1 #Now, save the graphsaver.save(sess, 'op\\my_test_model',global_step=1000)

restore.py

pythonimport tensorflow as tfsess=tf.Session()    #First let's load meta graph and restore weightssaver = tf.train.import_meta_graph('op\\my_test_model-1000.meta')#saver.restore(sess,'op\\my_test_model-1000') # restore the data according to the saving namesaver.restore(sess,tf.train.latest_checkpoint('op\\')) # restore the data according to the latest check point# Now, let's access and create placeholders variables and# create feed-dict to feed new data#graph = tf.get_default_graph()w1 = graph.get_tensor_by_name("w1:0")w2 = graph.get_tensor_by_name("w2:0")feed_dict ={w1:13.0,w2:17.0}###Now, access the op that you want to run. op_to_restore = graph.get_tensor_by_name("op_to_restore:0")#print (sess.run(op_to_restore,feed_dict))##This will print 60 which is calculated ##using new values of w1 and w2 and saved value of b1. #Add more to the current graphadd_on_op = tf.multiply(op_to_restore,2)print (sess.run(add_on_op,feed_dict))#This will print 120.