How to use Scope in Tensorflow

This article introduces the knowledge of "how to use Scope in Tensorflow". In the operation of actual cases, many people will encounter such a dilemma, so let the editor lead you to learn how to deal with these situations. I hope you can read it carefully and be able to achieve something!

There are two different context managers in Tensorflow to manage tensor and variable names. The first is tf.name_scope and the second is tf.get_variable. Let me explain it in detail with some examples.

1. Use tf.name_scope

Variables and tensors in Tensorflow have named attributes and are used to identify them in symbolic diagrams. If we do not specify a name attribute when we create a variable or tensor, Tensorflow automatically assigns a name.

We can override the default name by specifying an explicit name.

Note: there are two ways to define a new variable in Tensorflow: create a tf.Variable object or call tf.get_variable. Calling tf.get_variable with a new name creates a new variable, but if there is a variable with the same name, this throws a ValueError exception, which tells us that a variable with the same name is not allowed to be redeclared.

From the above code, we can see that tf.name_scope affects variable names created by tf.Variable, but does not affect variable names created by tf.get_variable.

2. Use tf.variable_scope

Unlike tf.name_scope,tf.variable_scope, the name of the variable created by tf.get_variable is modified.

But sometimes we really want to reuse a previously declared variable, what should we do? Tf.variable_scope provides the ability to do this by setting the reuse flag bit to True.

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