# tf.train.Saver类提供了保存和恢复模型的方法

# tf.train.Saver的构造函数 提供了save和恢复的参数选项

# Saver对象提供了方法来运行这些计算节点，制定了写和读的路径

# Saver会恢复所有在你模型当中已经定义的变量

# 如果你加载一个模型没有通知如果构建该模型的计算图

# TensorFlow 在二进制检查点文件中保存变量, 粗略地说, 将变量名映射到张量值

# Saving variable

# 创建一个Saver使用tf.train.Saver()来管理所有的变量在模型中

# 列如下面的代码块解释了如何调用tf.train.Saver.save方法来保存变量到checkpoint文件中

import tensorflow as tf

# Create some variables

v1 = tf.get_variable("v1", shape=[3], initializer=tf.zeros_initializer)

v2 = tf.get_variable("v2", shape=[5], initializer=tf.zeros_initializer)

inc_v1 = v1.assign(v1 + 1)

dec_v2 = v2.assign(v2 - 1)

# Add an op to initialize the variables

init_op = tf.global_variables_initializer()

# Add ops to save and restore all the variables

saver = tf.train.Saver()

# Later,launch the model,initialize the variables,

# do some work, and save the variables to disk

with tf.Session() as sess:

    sess.run(init_op)

    # Do some work with the model

    inc_v1.op.run()

    dec_v2.op.run()

    # Save the variables to disk

    save_path = saver.save(sess, "tmp/model.ckpt")

    print("Model saved in path : %s " % save_path)

2018-02-17 11:21:22.621424: I C:\tf_jenkins\workspace\rel-win\M\windows\PY\35\tensorflow\core\platform\cpu_feature_guard.cc:137] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX AVX2

Model saved in path : tmp/model.ckpt