versions/1.6.0/api/r/docs/_sources/api/mx.nd.adam.update.rst

``mx.nd.adam.update``
==========================================

Description
----------------------

Update function for Adam optimizer. Adam is seen as a generalization
of AdaGrad.

Adam update consists of the following steps, where g represents gradient and m, v
are 1st and 2nd order moment estimates (mean and variance).

.. math::

 g_t = \nabla J(W_{t-1})\\
 m_t = \beta_1 m_{t-1} + (1 - \beta_1) g_t\\
 v_t = \beta_2 v_{t-1} + (1 - \beta_2) g_t^2\\
 W_t = W_{t-1} - \alpha \frac{ m_t }{ \sqrt{ v_t } + \epsilon }

It updates the weights using::

	 m = beta1*m + (1-beta1)*grad
	 v = beta2*v + (1-beta2)*(grad**2)
	 w += - learning_rate * m / (sqrt(v) + epsilon)
	 
	 However, if grad's storage type is ``row_sparse``, ``lazy_update`` is True and the storage
	 type of weight is the same as those of m and v,
only the row slices whose indices appear in grad.indices are updated (for w, m and v)::

	 for row in grad.indices:
	 m[row] = beta1*m[row] + (1-beta1)*grad[row]
	 v[row] = beta2*v[row] + (1-beta2)*(grad[row]**2)
	 w[row] += - learning_rate * m[row] / (sqrt(v[row]) + epsilon)
	 
	 
	 


Arguments
------------------

+----------------------------------------+------------------------------------------------------------+
| Argument                               | Description                                                |
+========================================+============================================================+
| ``weight``                             | NDArray-or-Symbol.                                         |
|                                        |                                                            |
|                                        | Weight                                                     |
+----------------------------------------+------------------------------------------------------------+
| ``grad``                               | NDArray-or-Symbol.                                         |
|                                        |                                                            |
|                                        | Gradient                                                   |
+----------------------------------------+------------------------------------------------------------+
| ``mean``                               | NDArray-or-Symbol.                                         |
|                                        |                                                            |
|                                        | Moving mean                                                |
+----------------------------------------+------------------------------------------------------------+
| ``var``                                | NDArray-or-Symbol.                                         |
|                                        |                                                            |
|                                        | Moving variance                                            |
+----------------------------------------+------------------------------------------------------------+
| ``lr``                                 | float, required.                                           |
|                                        |                                                            |
|                                        | Learning rate                                              |
+----------------------------------------+------------------------------------------------------------+
| ``beta1``                              | float, optional, default=0.899999976.                      |
|                                        |                                                            |
|                                        | The decay rate for the 1st moment estimates.               |
+----------------------------------------+------------------------------------------------------------+
| ``beta2``                              | float, optional, default=0.999000013.                      |
|                                        |                                                            |
|                                        | The decay rate for the 2nd moment estimates.               |
+----------------------------------------+------------------------------------------------------------+
| ``epsilon``                            | float, optional, default=9.99999994e-09.                   |
|                                        |                                                            |
|                                        | A small constant for numerical stability.                  |
+----------------------------------------+------------------------------------------------------------+
| ``wd``                                 | float, optional, default=0.                                |
|                                        |                                                            |
|                                        | Weight decay augments the objective function with a        |
|                                        | regularization term that penalizes large weights. The      |
|                                        | penalty scales with the square of the magnitude of each    |
|                                        | weight.                                                    |
+----------------------------------------+------------------------------------------------------------+
| ``rescale.grad``                       | float, optional, default=1.                                |
|                                        |                                                            |
|                                        | Rescale gradient to grad = rescale_grad*grad.              |
+----------------------------------------+------------------------------------------------------------+
| ``clip.gradient``                      | float, optional, default=-1.                               |
|                                        |                                                            |
|                                        | Clip gradient to the range of [-clip_gradient,             |
|                                        | clip_gradient] If clip_gradient <= 0, gradient clipping is |
|                                        | turned off. grad = max(min(grad, clip_gradient),           |
|                                        | -clip_gradient).                                           |
+----------------------------------------+------------------------------------------------------------+
| ``lazy.update``                        | boolean, optional, default=1.                              |
|                                        |                                                            |
|                                        | If true, lazy updates are applied if gradient's stype is   |
|                                        | row_sparse and all of w, m and v have the same             |
|                                        | stype                                                      |
+----------------------------------------+------------------------------------------------------------+

Value
----------

``out`` The result mx.ndarray


Link to Source Code: http://github.com/apache/incubator-mxnet/blob/1.6.0/src/operator/optimizer_op.cc#L688