brainpy.optimizers.MomentumNesterov

class brainpy.optimizers.MomentumNesterov(lr, train_vars=None, momentum=0.9, name=None)

Nesterov accelerated gradient optimizer 2.

\[\begin{split}\begin{align} \begin{split} v_t &= \gamma v_{t-1} + \eta \nabla_\theta J( \theta - \gamma v_{t-1} ) \\ \theta &= \theta - v_t \end{split} \end{align}\end{split}\]

References

2

Nesterov, Y. (1983). A method for unconstrained convex minimization problem with the rate of convergence o(1/k2). Doklady ANSSSR (translated as Soviet.Math.Docl.), vol. 269, pp. 543– 547.

__init__(lr, train_vars=None, momentum=0.9, name=None)

Methods

__init__(lr[, train_vars, momentum, name])
check_grads(grads)
load_states(filename[, verbose])	Load the model states.
nodes([method, level, include_self])	Collect all children nodes.
register_implicit_nodes(nodes)
register_implicit_vars(variables)
register_vars([train_vars])
save_states(filename[, variables])	Save the model states.
train_vars([method, level, include_self])	The shortcut for retrieving all trainable variables.
unique_name([name, type_])	Get the unique name for this object.
update(grads)
vars([method, level, include_self])	Collect all variables in this node and the children nodes.

Attributes

name