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A zero-mean Ornstein-Uhlenbeck process.

    initial_value, damping=0.15, stddev=0.2, seed=None,


  • initial_value: Initial value of the process.
  • damping: The rate at which the noise trajectory is damped towards the mean. We must have 0 <= damping <= 1, where a value of 0 gives an undamped random walk and a value of 1 gives uncorrelated Gaussian noise. Hence in most applications a small non-zero value is appropriate.
  • stddev: Standard deviation of the Gaussian component.
  • seed: Seed for random number generation.
  • scope: Scope of the variables.


  • 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.

  • 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) == []
  • trainable_variables: Sequence of trainable variables owned by this module and its submodules.

  • variables: Sequence of variables owned by this module and its submodules.



View source


Call self as a function.


    cls, method

Decorator to automatically enter the module name scope.

class MyModule(tf.Module):
  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)

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

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


  • method: The method to wrap.


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