# Class Module

Base neural network module class.

# Aliases:

• Class tf.compat.v1.Module
• Class tf.compat.v2.Module

# Used in the guide:

• Using the SavedModelformat tf.VariableA module is a named container for s, other tf.Modules and functions which apply to user input. For example a dense layer in a neural network might be implemented as a tf.Module:

  class Dense(tf.Module):
   def __init__(self, in_features, output_features, name=None):
     super(Dense, self).__init__(name=name)
     self.w = tf.Variable(
         tf.random.normal([input_features, output_features]), name='w')
     self.b = tf.Variable(tf.zeros([output_features]), name='b')

   def __call__(self, x):
     y = tf.matmul(x, self.w) + self.b
     return tf.nn.relu(y)

You can use the Dense layer as you would expect:

 d = Dense(input_features=64, output_features=10)
d(tf.ones([100, 64]))
#==> <tf.Tensor: ...>

tf.ModuleBy subclassing instead of object any tf.Variable or instances assigned to object properties can be collected using the variables, trainable_variables or submodules property:

 d.variables
#==> (<tf.Variable 'b:0' ...>, <tf.Variable 'w:0' ...>)

tf.ModuleSubclasses of can also take advantage of the _flatten method which can be used to implement tracking of any other types.

tf.ModuleAll classes have an associated tf.name_scope which can be used to group operations in TensorBoard and create hierarchies for variable names which can help with debugging. We suggest using the name scope when creating nested submodules/parameters or for forward methods whose graph you might want to inspect in TensorBoard. You can enter the name scope explicitly using with self.name_scope: or you can annotate methods (apart from init) with @.with_name_scope.

 class MLP(tf.Module):
  def __init__(self, input_size, sizes, name=None):
    super(MLP, self).__init__(name=name)
    self.layers = []
    with self.name_scope:
      for size in sizes:
        self.layers.append(Dense(input_size=input_size, output_size=size))
        input_size = size

  @tf.Module.with_name_scope
  def __call__(self, x):
    for layer in self.layers:
      x = layer(x)
    return x

# init

View source

 __init__(name=None)

Initialize self. See help(type(self)) for accurate signature.

# Properties

# name

Returns the name of this module as passed or determined in the ctor. NOTE: This is not the same as the self.name_scope.name which includes parent module names.

# name_scope

tf.name_scopeReturns a instance for this class.

# submodules

Sequence of all sub-modules. Submodules are modules which are properties of this module, or found as properties of modules which are properties of this module (and so on).

 a = tf.Module()
b = tf.Module()
c = tf.Module()
a.b = b
b.c = c
assert list(a.submodules) == [b, c]
assert list(b.submodules) == [c]
assert list(c.submodules) == []

# Returns:

A sequence of all submodules.

# trainable_variables

Sequence of variables owned by this module and it's submodules.

# Returns:

A sequence of variables for the current module (sorted by attribute name) followed by variables from all submodules recursively (breadth first).

# variables

Sequence of variables owned by this module and it's submodules.

# Returns:

A sequence of variables for the current module (sorted by attribute name) followed by variables from all submodules recursively (breadth first).

# Methods

# with_name_scope

View source

 @classmethod
with_name_scope(
    cls,
    method
)

Decorator to automatically enter the module name scope.

 class MyModule(tf.Module):
  @tf.Module.with_name_scope
  def __call__(self, x):
    if not hasattr(self, 'w'):
      self.w = tf.Variable(tf.random.normal([x.shape[1], 64]))
    return tf.matmul(x, self.w)

tf.VariableUsing the above module would produce s and tf.Tensors whose names included the module name:

 mod = MyModule()
mod(tf.ones([8, 32]))
# ==> <tf.Tensor: ...>
mod.w
# ==> <tf.Variable ...'my_module/w:0'>

# Args:

• method: The method to wrap.

# Returns:

The original method wrapped such that it enters the module's name scope.