brainpy.RidgeTrainer

class brainpy.RidgeTrainer(target, alpha=1e-07, **kwargs)

Trainer of ridge regression, also known as regression with Tikhonov regularization.

For more parameters, users should refer to DSRunner.

Parameters:

• target (TrainingSystem, DynamicalSystem) – The target model.

• alpha (float) – The regularization coefficient.

__init__(target, alpha=1e-07, **kwargs)

Methods

__init__(target[, alpha])
cpu()	Move all variable into the CPU device.
cuda()	Move all variables into the GPU device.
fit(train_data[, reset_state, shared_args])	Fit the target model according to the given training and testing data.
load_state_dict(state_dict[, warn, compatible])	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.
predict(inputs[, reset_state, shared_args, ...])	Prediction function.
register_implicit_nodes(*nodes[, node_cls])
register_implicit_vars(*variables[, var_cls])
reset_state()	Reset state of the DSRunner.
run(*args, **kwargs)	Same as predict().
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)	Unflatten the data to construct an object of this class.
unique_name([name, type_])	Get the unique name for this object.
vars([method, level, include_self, ...])	Collect all variables in this node and the children nodes.

Attributes

name	Name of the model.