Difference between "tf.Variable

The following content is what i have quoted from

1. "CSDN - adrianna_xy"
   Original link - https://blog.csdn.net/u012223913/article/details/78533910

1. tf.Variable()
   W = tf.Variable(<initial-value>, name=<optional-name>)
   用于生成一个初始值为initial-value的变量。必须指定初始化值

2. tf.get_variable()
   W = tf.get_variable(name, shape=None, dtype=tf.float32, initializer=None,
   regularizer=None, trainable=True, collections=None)
   获取已存在的变量（要求不仅名字，而且初始化方法等各个参数都一样），如果不存在，就新建一个。
   可以用各种初始化方法，不用明确指定值。

3. 区别
   推荐使用tf.get_variable(), 因为：
   _初始化更方便
   比如用xavier_initializer:
   W = tf.get_variable("W", shape=[784, 256],
   initializer=tf.contrib.layers.xavier_initializer())
   _方便共享变量
   因为tf.get_variable() 会检查当前命名空间下是否存在同样name的变量，可以方便共享变量。而tf.Variable 每次都会新建一个变量。
   需要注意的是tf.get_variable() 要配合reuse和tf.variable_scope() 使用。

4. 举个栗子
   4.1 首先介绍一下tf.variable_scope().
   如果已经存在的变量没有设置为共享变量，TensorFlow 运行到第二个拥有相同名字的变量的时候，就会报错。为了解决这个问题，TensorFlow 提出了 tf.variable_scope 函数：它的主要作用是，在一个作用域 scope 内共享一些变量，举个简单的栗子：
   with tf.variable_scope("foo"):
   v = tf.get_variable("v", [1]) #v.name == "foo/v:0"
   简单来说就是给变量名前再加了个变量空间名。
   4.2 对比
   接下来看看怎么用tf.get_variable()实现共享变量：
   with tf.variable_scope("one"):
   a = tf.get_variable("v", [1]) #a.name == "one/v:0"
   with tf.variable_scope("one"):
   b = tf.get_variable("v", [1]) #创建两个名字一样的变量会报错 ValueError: Variable one/v already exists
   with tf.variable_scope("one", reuse = True): #注意reuse的作用。
   c = tf.get_variable("v", [1]) #c.name == "one/v:0" 成功共享，因为设置了reuse
   assert a==c #Assertion is true, they refer to the same object.
   然后看看如果用tf.Variable() 会有什么效果：
   with tf.variable_scope("two"):
   d = tf.get_variable("v", [1]) #d.name == "two/v:0"
   e = tf.Variable(1, name = "v", expected_shape = [1]) #e.name == "two/v_1:0"
   assert d==e #AssertionError: they are different objects
   可以看到，同样的命名空间（‘two’）和名字（v），但是d和e的值却不一样。
   Reference:
   https://stackoverflow.com/questions/37098546/difference-between-variable-and-get-variable-in-tensorflow
   https://www.tensorflow.org/versions/r1.2/api_docs/python/tf/variable_scope
   http://blog.csdn.net/u013645510/article/details/53769689

2. Chetan Ruparel - Which is preferred in TensorFlow, tf.Variable or tf.get_variable?

I would recommend to use tf.get_Variable as it will make refactoring code easy and plus it doesn't create any problems when using multiple GPU. tf.gets_Variable sees or gets the variable ,if not found it would create it . We can specify the initializer such as xavier_initializer.
tf.Variable always creates variable. It requires an initial value .To know more about tf.Variable ,go to Getting started with TensorFlow
tf.Variable is kinda old ,lower level and tf.get_Variable is newer and better
[1] Information, syntax and example for tf.Variable
Footnotes
[1] Getting started with TensorFlow