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python - 恢复 TensorFlow 模型的方式差异

问题描述

我已经看到并尝试了两种方法，但无法理解它有什么区别。以下是我使用的两种方法：
方法 1： 

saver = tf.train.import_meta_graph(tf.train.latest_checkpoint(model_path)+".meta")

sess  = tf.Session()
sess.run(tf.global_variables_initializer())   
sess.run(tf.local_variables_initializer()) 
if(tf.train.checkpoint_exists(tf.train.latest_checkpoint(model_path))):
    saver.restore(sess, tf.train.latest_checkpoint(model_path))
    print(tf.train.latest_checkpoint(model_path) + "Session Loaded for Testing")

方法二： 

saver = tf.train.Saver()
sess  =tf.Session()
sess.run(tf.global_variables_initializer())    
if(tf.train.checkpoint_exists(tf.train.latest_checkpoint(model_path))):
        saver.restore(sess, tf.train.latest_checkpoint(model_path))
        print(tf.train.latest_checkpoint(model_path) + "Session Loaded for Testing")

我想知道的是：

以上两种方法有什么区别？
加载模型的最佳方法是什么？

请让我知道您对此有何建议。

标签： pythonpython-3.xtensorflowmodel

解决方案

我会尽量简明扼要，所以这是我对此事的 2 美分。我将评论您代码的重要行以指出我的想法。

# Importing the meta graph is same as building the same graph from scratch
# creating the same variables, creating the same placeholders and ect.
# Basically you are only importing the graph definition
saver = tf.train.import_meta_graph(tf.train.latest_checkpoint(model_path)+".meta")

sess  = tf.Session()
# Absolutely no need to initialize the variables here. They will be initialized
# when the you restore the learned variables.
sess.run(tf.global_variables_initializer())   
sess.run(tf.local_variables_initializer()) 
if(tf.train.checkpoint_exists(tf.train.latest_checkpoint(model_path))):
    saver.restore(sess, tf.train.latest_checkpoint(model_path))
    print(tf.train.latest_checkpoint(model_path) + "Session Loaded for Testing")

至于第二种方法：

# You can't create a saver object like this, you will get an error "No variables to save", which is true.
# You haven't created any variables. The workaround for doing this is:
# saver = tf.train.Saver(defer_build=True) and then after building the graph
# ....Graph building code goes here....
# saver.build()
saver = tf.train.Saver()
sess = tf.Session()
# Absolutely no need to initialize the variables here. They will be initialized
# when the you restore the learned variables. 
sess.run(tf.global_variables_initializer())    
if(tf.train.checkpoint_exists(tf.train.latest_checkpoint(model_path))):
    saver.restore(sess, tf.train.latest_checkpoint(model_path))
    print(tf.train.latest_checkpoint(model_path) + "Session Loaded for Testing")

所以第一种方法没有错，但第二种方法完全不正确。不要误会我的意思，但我不喜欢他们中的任何一个。不过，这只是个人口味。另一方面，我想做的是以下内容：

# Have a class that creates the model and instantiate an object of that class
my_trained_model = MyModel()
# This is basically the same as what you are doing with
# saver = tf.train.import_meta_graph(tf.train.latest_checkpoint(model_path)+".meta")
# Then, once I have the graph build, I will create a saver object
saver = tf.train.Saver()
# Then I will create a session
with tf.Session() as sess:
    # Restore the trained variables here
    saver.restore(sess, model_checkpoint_path)
    # Now I can do whatever I want with the my_trained_model object

我希望这对你有帮助。

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