brainpy.optim.SGD

class brainpy.optim.SGD(lr, train_vars=None, weight_decay=None, name=None)

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, weight_decay=None, name=None)

Methods

__init__(lr[, train_vars, weight_decay, name])
check_grads(grads)
cpu()	Move all variable into the CPU device.
cuda()	Move all variables into the GPU device.
load_state_dict(state_dict[, warn])	Copy parameters and buffers from state_dict into this module and its descendants.
load_states(filename[, verbose])	Load the model states.
nodes([method, level, include_self])	Collect all children nodes.
register_implicit_nodes(*nodes[, node_cls])
register_implicit_vars(*variables, ...)
register_train_vars([train_vars])
register_vars([train_vars])
save_states(filename[, variables])	Save the model states.
state_dict()	Returns a dictionary containing a whole state of the module.
to(device)	Moves all variables into the given device.
tpu()	Move all variables into the TPU device.
train_vars([method, level, include_self])	The shortcut for retrieving all trainable variables.
tree_flatten()	Flattens the object as a PyTree.
tree_unflatten(aux, dynamic_values)	New in version 2.3.1.
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.