brainpy.OnlineTrainer#

class brainpy.OnlineTrainer(target, fit_method=None, **kwargs)[source]#

Online trainer for models with recurrent dynamics.

For more parameters, users should refer to DSRunner.

Parameters:

  • target (DynamicalSystem) – The target model to train.

  • fit_method (OnlineAlgorithm, Callable, dict, str) –

    The fitting method applied to the target model.

    • It can be a string, which specify the shortcut name of the training algorithm. Like, fit_method='rls' means using the RLS method. All supported fitting methods can be obtained through get_supported_online_methods().

    • It can be a dict, whose “name” item specifies the name of the training algorithm, and the others parameters specify the initialization parameters of the algorithm. For example, fit_method={'name': 'rls', 'alpha': 0.1}.

    • It can be an instance of brainpy.algorithms.OnlineAlgorithm. For example, fit_meth=bp.algorithms.RLS(alpha=1e-5).

    • It can also be a callable function.

  • kwargs (Any) – Other general parameters please see DSRunner.

__init__(target, fit_method=None, **kwargs)[source]#

Methods

__init__(target[, fit_method])

cpu()

Move all variable into the CPU device.

cuda()

Move all variables into the GPU device.

fit(train_data[, reset_state, shared_args])

rtype:

TypeVar(Output)

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.