tf.make_template( name_, func_, create_scope_now_=False, unique_name_=None, custom_getter_=None, **kwargs )
See the guide: Variables > Sharing Variables
Given an arbitrary function, wrap it so that it does variable sharing.
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.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
- The function may use variable scopes and other templates internally to
create and reuse variables, but it shouldn't use
tf.global_variablesto 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
w will be scaled by the same
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.get_variable(scalar_name, shape=, initializer=tf.constant_initializer(1)) return x * var1 scale_by_y = tf.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
If all of these are true, then 2 properties are enforced by the template:
- Calling the same template multiple times will share all non-local variables.
- 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.get_variable(scalar_name, shape=, initializer=tf.constant_initializer(1)) return x * var1 with tf.variable_scope('scope') as vs: scale_by_y = tf.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.variable_scope(vs, reuse=True): scale_by_y2 = tf.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.
name_: A name for the scope created by this template. If necessary, the name will be made unique by appending
_Nto the name.
func_: The function to wrap.
create_scope_now_: Boolean controlling whether the scope should be created when the template is constructed or when the template is called. Default is False, meaning the scope is created when the template is called.
unique_name_: When used, it overrides name_ and is not made unique. If a template of the same scope/unique_name already exists and reuse is false, an error is raised. Defaults to None.
custom_getter_: Optional custom getter for variables used in
func_. See the
custom_getterdocumentation for more information.
**kwargs: Keyword arguments to apply to
A function to encapsulate a set of variables which should be created once
and reused. An enclosing scope will be created either when
is called or when the result is called, depending on the value of
create_scope_now_. Regardless of the value, the first time the template
is called it will enter the scope with no reuse, and call
func_ to create
variables, which are guaranteed to be unique. All subsequent calls will
re-enter the scope and reuse those variables.