使用Tensorflow進行深度學習訓練的時候，需要對訓練好的網路模型和各種引數進行儲存，以便在此基礎上繼續訓練或者使用。介紹這方面的部落格有很多，我發現寫的最好的是這一篇官方英文介紹：

我對這篇文章進行了整理和彙總。

首先是模型的儲存。直接上程式碼：

#!/usr/bin/env python
#-*- coding:utf-8 -*-
############################
#File Name: tut1_save.py
#Author: Wang 
#Mail: [email protected]
#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)
 

1. 建立saver的時候可以指明要儲存的tensor，如果不指明，就會全部存下來。在這裡也可以指明最大儲存數量和checkpoint的記錄時間。具體細節看英文部落格。

2. saver.save()函式裡面可以設定global_step和write_meta_graph，meta儲存的是網路結構，只在開始執行程式的時候儲存一次即可，後續可以通過設定write_meta_graph = False加以限制。

3. 這個程式執行結束後，會在程式目錄下生成四個檔案，分別是.meta(儲存網路結構)、.data和.index(儲存訓練好的引數)、checkpoint(記錄最新的模型)。

下面是如何載入已經儲存的網路模型。這裡有兩種方法，第一種是saver.restore(sess, 'aaaa.ckpt')，這種方法的本質是讀取全部引數，並載入到已經定義好的網路結構上，因此相當於給網路的weights和biases賦值並執行tf.global_variables_initializer()。這種方法的缺點是使用前必須重寫網路結構，而且網路結構要和儲存的引數完全對上。第二種就比較高端了，直接把網路結構載入進來(.meta)，上程式碼：

#!/usr/bin/env python
#-*- coding:utf-8 -*-
############################
#File Name: tut2_import.py
#Author: Wang 
#Mail: 
 
[email protected]
#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: [email protected]
#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()