tf.Module

Base neural network module class.

View aliases

Compat aliases for migration

tf.compat.v1.Module

tf.Module(
    name=None
)

Used in the notebooks

Used in the guide	Used in the tutorials
Introduction to modules, layers, and models Better performance with tf.function Distributed training with Core APIs and DTensor Logistic regression for binary classification with Core APIs Multilayer perceptrons for digit recognition with Core APIs	Distributed training with DTensors DeepDream Simple audio recognition: Recognizing keywords Neural machine translation with attention Neural machine translation with a Transformer and Keras

A module is a named container for tf.Variables, 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, input_dim, output_size, name=None):
    super().__init__(name=name)
    self.w = tf.Variable(
      tf.random.normal([input_dim, output_size]), name=&#x27;w')
    self.b = tf.Variable(tf.zeros([output_size]), name=&#x27;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_dim=3, output_size=2)
d(tf.ones([1, 3]))
<tf.Tensor: shape=(1, 2), dtype=float32, numpy=..., dtype=float32)>

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

d.variables
    (<tf.Variable &#x27;b:0' shape=(2,) dtype=float32, numpy=...,
    dtype=float32)>,
    <tf.Variable &#x27;w:0' shape=(3, 2) dtype=float32, numpy=..., dtype=float32)>)

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

All tf.Module 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 @tf.Module.with_name_scope.

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

module = MLP(input_size=5, sizes=[5, 5])
module.variables
(<tf.Variable &#x27;mlp/b:0' shape=(5,) dtype=float32, numpy=..., dtype=float32)>,
<tf.Variable &#x27;mlp/w:0' shape=(5, 5) dtype=float32, numpy=...,
   dtype=float32)>,
<tf.Variable &#x27;mlp/b:0' shape=(5,) dtype=float32, numpy=..., dtype=float32)>,
<tf.Variable &#x27;mlp/w:0' shape=(5, 5) dtype=float32, numpy=...,
   dtype=float32)>)

Attributes
`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`	Returns a `tf.name_scope` instance for this class.
`non_trainable_variables`	Sequence of non-trainable variables owned by this module and its submodules. Note: this method uses reflection to find variables on the current instance and submodules. For performance reasons you may wish to cache the result of calling this method if you don't expect the return value to change.
`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` `list(a.submodules) == [b, c]` `True` `list(b.submodules) == [c]` `True` `list(c.submodules) == []` `True`
`trainable_variables`	Sequence of trainable variables owned by this module and its submodules. Note: this method uses reflection to find variables on the current instance and submodules. For performance reasons you may wish to cache the result of calling this method if you don't expect the return value to change.
`variables`	Sequence of variables owned by this module and its submodules. Note: this method uses reflection to find variables on the current instance and submodules. For performance reasons you may wish to cache the result of calling this method if you don't expect the return value to change.

Methods

`with_name_scope`

View source

@classmethod
with_name_scope(
    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, &#x27;w'):
      self.w = tf.Variable(tf.random.normal([x.shape[1], 3]))
    return tf.matmul(x, self.w)

Using the above module would produce tf.Variables and tf.Tensors whose names included the module name:

mod = MyModule()
mod(tf.ones([1, 2]))
<tf.Tensor: shape=(1, 3), dtype=float32, numpy=..., dtype=float32)>
mod.w
<tf.Variable &#x27;my_module/Variable:0' shape=(2, 3) dtype=float32,
numpy=..., dtype=float32)>

Args
`method`	The method to wrap.

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