python - Tensorflow variable scope: reuse if variable exists

Question

I want a piece of code that creates a variable within a scope if it doesn't exist, and access the variable if it already exists. I need it to be the same code since it will be called multiple times.

However, Tensorflow needs me to specify whether I want to create or reuse the variable, like this:

with tf.variable_scope("foo"): #create the first time
    v = tf.get_variable("v", [1])

with tf.variable_scope("foo", reuse=True): #reuse the second time
    v = tf.get_variable("v", [1])

How can I get it to figure out whether to create or reuse it automatically? I.e., I want the above two blocks of code to be the same and have the program run.

Answer 1

A ValueError is raised in get_variable() when creating a new variable and shape is not declared, or when violating reuse during variable creation. Therefore, you can try this:

def get_scope_variable(scope_name, var, shape=None):
    with tf.variable_scope(scope_name) as scope:
        try:
            v = tf.get_variable(var, shape)
        except ValueError:
            scope.reuse_variables()
            v = tf.get_variable(var)
    return v

v1 = get_scope_variable('foo', 'v', [1])
v2 = get_scope_variable('foo', 'v')
assert v1 == v2

Note that the following also works:

v1 = get_scope_variable('foo', 'v', [1])
v2 = get_scope_variable('foo', 'v', [1])
assert v1 == v2

---

UPDATE. The new API supports auto-reusing now:

def get_scope_variable(scope, var, shape=None):
    with tf.variable_scope(scope, reuse=tf.AUTO_REUSE):
        v = tf.get_variable(var, shape)
    return v