tf.compat.v1.make_template

View source on GitHub

Given an arbitrary function, wrap it so that it does variable sharing.

tf.compat.v1.make_template(
    name_,
    func_,
    create_scope_now_=False,
    unique_name_=None,
    custom_getter_=None,
    **kwargs
)

Description

Used in the notebooks

This wraps func_ in a Template and partially evaluates it. Templates are functions that create variables the first time they are called and reuse them thereafter. In order for func_ to be compatible with a Template it must have the following properties:

• The function should create all trainable variables and any variables that should be reused by calling tf.compat.v1.get_variable. If a trainable variable is created using tf.Variable, then a ValueError will be thrown. Variables that are intended to be locals can be created by specifying tf.Variable(..., trainable=false).
• The function may use variable scopes and other templates internally to create and reuse variables, but it shouldn't use tf.compat.v1.global_variables to capture variables that are defined outside of the scope of the function.
• Internal scopes and variable names should not depend on any arguments that are not supplied to make_template. In general you will get a ValueError telling you that you are trying to reuse a variable that doesn't exist if you make a mistake.

In the following example, both z and w will be scaled by the same y. It is important to note that if we didn't assign scalar_name and used a different name for z and w that a ValueError would be thrown because it couldn't reuse the variable.

def my_op(x, scalar_name):
  var1 = tf.compat.v1.get_variable(scalar_name,
                         shape=[],
                         initializer=tf.compat.v1.constant_initializer(1))
  return x * var1

scale_by_y = tf.compat.v1.make_template('scale_by_y', my_op, scalar_name='y')

z = scale_by_y(input1)
w = scale_by_y(input2)

As a safe-guard, the returned function will raise a ValueError after the first call if trainable variables are created by calling tf.Variable.

If all of these are true, then 2 properties are enforced by the template:

1. Calling the same template multiple times will share all non-local variables.
2. Two different templates are guaranteed to be unique, unless you reenter the same variable scope as the initial definition of a template and redefine it. An examples of this exception:

def my_op(x, scalar_name):
  var1 = tf.compat.v1.get_variable(scalar_name,
                         shape=[],
                         initializer=tf.compat.v1.constant_initializer(1))
  return x * var1

with tf.compat.v1.variable_scope('scope') as vs:
  scale_by_y = tf.compat.v1.make_template('scale_by_y', my_op,
  scalar_name='y')
  z = scale_by_y(input1)
  w = scale_by_y(input2)

# Creates a template that reuses the variables above.
with tf.compat.v1.variable_scope(vs, reuse=True):
  scale_by_y2 = tf.compat.v1.make_template('scale_by_y', my_op,
  scalar_name='y')
  z2 = scale_by_y2(input1)
  w2 = scale_by_y2(input2)

Depending on the value of create_scope_now_, the full variable scope may be captured either at the time of first call or at the time of construction. If this option is set to True, then all Tensors created by repeated calls to the template will have an extra trailing _N+1 to their name, as the first time the scope is entered in the Template constructor no Tensors are created.