The get_variable() function creates a new variable or returns one created earlier by get_variable(). It won’t return a variable created using tf.Variable(). Here’s a quick example:
>>> with tf.variable_scope("foo"):
... bar1 = tf.get_variable("bar", (2,3)) # create
...
>>> with tf.variable_scope("foo", reuse=True):
... bar2 = tf.get_variable("bar") # reuse
...
>>> with tf.variable_scope("", reuse=True): # root variable scope
... bar3 = tf.get_variable("foo/bar") # reuse (equivalent to the above)
...
>>> (bar1 is bar2) and (bar2 is bar3)
True
If you did not create the variable using tf.get_variable(), you have a couple options. First, you can use tf.global_variables() (as @mrry suggests):
>>> bar1 = tf.Variable(0.0, name="bar")
>>> bar2 = [var for var in tf.global_variables() if var.op.name=="bar"][0]
>>> bar1 is bar2
True
Or you can use tf.get_collection() like so:
>>> bar1 = tf.Variable(0.0, name="bar")
>>> bar2 = tf.get_collection(tf.GraphKeys.GLOBAL_VARIABLES, scope="bar")[0]
>>> bar1 is bar2
True
Edit
You can also use get_tensor_by_name():
>>> bar1 = tf.Variable(0.0, name="bar")
>>> graph = tf.get_default_graph()
>>> bar2 = graph.get_tensor_by_name("bar:0")
>>> bar1 is bar2
False, bar2 is a Tensor througn convert_to_tensor on bar1. but bar1 equal
bar2 in value.
Recall that a tensor is the output of an operation. It has the same name as the operation, plus :0. If the operation has multiple outputs, they have the same name as the operation plus :0, :1, :2, and so on.