brainpy.optimizers.SGD#

class brainpy.optimizers.SGD(lr, train_vars=None, name=None)[source]#

Stochastic gradient descent optimizer.

SGD performs a parameter update for training examples \(x\) and label \(y\):

\[\theta = \theta - \eta \cdot \nabla_\theta J(\theta; x; y)\]
Parameters

lr (float, Scheduler) – learning rate.

__init__(lr, train_vars=None, name=None)[source]#

Methods

__init__(lr[, train_vars, 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, **named_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

Name of the model.