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