python使用tensorflow保存、加载和使用模型的方法

#!/usr/bin/env python/292279.html">python #-*- coding:utf-8 -*- ############################ #File Name: tut1_save.py #Author: Wang  #Mail: wang19920419@hotmail.com #Created Time:2017-08-30 11:04:25 ############################  import tensorflow as tf  # prepare to feed input, i.e. feed_dict and placeholders w1 = tf.Variable(tf.random_normal(shape = [2]), name = 'w1') # name is very important in restoration w2 = tf.Variable(tf.random_normal(shape = [2]), name = 'w2') b1 = tf.Variable(2.0, name = 'bias1') feed_dict = {w1:[10,3], w2:[5,5]}  # define a test operation that will be restored w3 = tf.add(w1, w2) # without name, w3 will not be stored w4 = tf.multiply(w3, b1, name = "op_to_restore")  #saver = tf.train.Saver() saver = tf.train.Saver(max_to_keep = 4, keep_checkpoint_every_n_hours = 1) sess = tf.Session() sess.run(tf.global_variables_initializer()) print sess.run(w4, feed_dict) #saver.save(sess, 'my_test_model', global_step = 100) saver.save(sess, 'my_test_model') #saver.save(sess, 'my_test_model', global_step = 100, write_meta_graph = False) 

#!/usr/bin/env python #-*- coding:utf-8 -*- ############################ #File Name: tut2_import.py #Author: Wang  #Mail: wang19920419@hotmail.com #Created Time:2017-08-30 14:16:38 ############################  import tensorflow as tf sess = tf.Session() new_saver = tf.train.import_meta_graph('my_test_model.meta') new_saver.restore(sess, tf.train.latest_checkpoint('./')) print sess.run('w1:0') 

#!/usr/bin/env python #-*- coding:utf-8 -*- ############################ #File Name: tut3_reuse.py #Author: Wang #Mail: wang19920419@hotmail.com #Created Time:2017-08-30 14:33:35 ############################  import tensorflow as tf  sess = tf.Session()  # First, load meta graph and restore weights saver = tf.train.import_meta_graph('my_test_model.meta') saver.restore(sess, tf.train.latest_checkpoint('./'))  # Second, 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:[-1,1], w2:[4,6]}  # Access the op that want to run op_to_restore = graph.get_tensor_by_name('op_to_restore:0')  print sess.run(op_to_restore, feed_dict)   # ouotput: [6. 14.] 

import tensorflow as tf sess=tf.Session()   #First let's load meta graph and restore weights saver = tf.train.import_meta_graph('my_test_model-1000.meta') saver.restore(sess,tf.train.latest_checkpoint('./')) # 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")  #Add more to the current graph add_on_op = tf.multiply(op_to_restore,2)  print sess.run(add_on_op,feed_dict) #This will print 120. 

...... ...... saver = tf.train.import_meta_graph('vgg.meta') # Access the graph graph = tf.get_default_graph() ## Prepare the feed_dict for feeding data for fine-tuning   #Access the appropriate output for fine-tuning fc7= graph.get_tensor_by_name('fc7:0')  #use this if you only want to change gradients of the last layer fc7 = tf.stop_gradient(fc7) # It's an identity function fc7_shape= fc7.get_shape().as_list()  new_outputs=2 weights = tf.Variable(tf.truncated_normal([fc7_shape[3], num_outputs], stddev=0.05)) biases = tf.Variable(tf.constant(0.05, shape=[num_outputs])) output = tf.matmul(fc7, weights) + biases pred = tf.nn.softmax(output)  # Now, you run this with fine-tuning data in sess.run()