Let’s begin by a short introduction to variable sharing. It is a mechanism in TensorFlow that allows for sharing variables accessed in different parts of the code without passing references to the variable around. The method tf.get_variable can be used with the name of the variable as argument to either create a new variable with such name or retrieve the one that was created before. This is different from using the tf.Variable constructor which will create a new variable every time it is called (and potentially add a suffix to the variable name if a variable with such name already exists). It is for the purpose of the variable sharing mechanism that a separate type of scope (variable scope) was introduced.
As a result, we end up having two different types of scopes:
name scope, created using tf.name_scope or tf.op_scope variable scope, created using tf.variable_scope or tf.variable_op_scope Both scopes have the same effect on all operations as well as variables created using tf.Variable, i.e. the scope will be added as a prefix to the operation or variable name.
1. tf.name_scope creates namespace for operators in the default graph. 2. tf.variable_scope creates namespace for both variables and operators in the default graph.
However, name scope is ignored by tf.get_variable. We can see that in the following example:
with tf.name_scope("my_scope"): v1 = tf.get_variable("var1", , dtype=tf.float32) v2 = tf.Variable(1, name="var2", dtype=tf.float32) a = tf.add(v1, v2) print(v1.name) # var1:0 print(v2.name) # my_scope/var2:0 print(a.name) # my_scope/Add:0
The only way to place a variable accessed using tf.get_variable in a scope is to use variable scope, as in the following example:
with tf.variable_scope("my_scope"): v1 = tf.get_variable("var1", , dtype=tf.float32) v2 = tf.Variable(1, name="var2", dtype=tf.float32) a = tf.add(v1, v2) print(v1.name) # my_scope/var1:0 print(v2.name) # my_scope/var2:0 print(a.name) # my_scope/Add:0
Finally, let’s look at the difference between the different methods for creating scopes. We can group them in two categories: