brainpy.OfflineTrainer

class brainpy.OfflineTrainer(target, fit_method=None, **kwargs)

Offline trainer for models with recurrent dynamics.

For more parameters, users should refer to DSRunner.

Parameters

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

• fit_method (OfflineAlgorithm, 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='ridge' means using the Ridge regression method. All supported fitting methods can be obtained through get_supported_offline_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': 'ridge', 'alpha': 0.1}.

  • It can be an instance of brainpy.algorithms.OfflineAlgorithm. For example, fit_meth=bp.algorithms.RidgeRegression(alpha=0.1).

  • It can also be a callable function, which receives three arguments “targets”, “x” and “y”. For example, fit_method=lambda targets, x, y: numpy.linalg.lstsq(x, targets)[0].

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

__init__(target, fit_method=None, **kwargs)

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])	Fit the target model according to the given training and testing data.
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.
predict(inputs[, reset_state, shared_args, ...])	Prediction function.
register_implicit_nodes(*nodes[, node_cls])
register_implicit_vars(*variables, ...)
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)	New in version 2.3.1.
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.