brainpy.BPTT

class brainpy.BPTT(target, loss_fun, optimizer=None, loss_has_aux=False, logger=<_io.TextIOWrapper name='<stdout>' mode='w' encoding='UTF-8'>, seed=None, shuffle_data=None, **kwargs)

The trainer implementing the back-propagation through time (BPTT) algorithm for training dyamical systems.

For more parameters, users should refer to DSRunner.

Parameters

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

• loss_fun (str, callable) –

  The loss function.

  • If it is a string, it should be the function chosen from brainpy.losses module.

  • Otherwise, a callable function which receives argument of (predicts, targets) should be provided.

  Note

  If monitors has been set in the trainer, the predicts contains two parts: the network history prediction outputs, and the monitored values.

  see BrainPy examples for more information.

• loss_has_aux (bool) –

  To indicate whether the loss function returns auxiliary data expect the loss. Moreover, all auxiliary data should be a dict, whose key is used for logging item name and its data is used for the corresponding value. For example,

  def loss_fun(predicts, targets):
     return loss, {'acc': acc, 'spike_num': spike_num}
  

• optimizer (Optimizer) – The optimizer used for training. Should be an instance of Optimizer.

• numpy_mon_after_run (bool) – Make the monitored results as NumPy arrays.

• logger (Any) – A file-like object (stream). Used to output the running results. Default is the current sys.stdout.

• data_first_axis (str) – To indicate whether the first axis is the batch size (data_first_axis='B') or the time length (data_first_axis='T').

__init__(target, loss_fun, optimizer=None, loss_has_aux=False, logger=<_io.TextIOWrapper name='<stdout>' mode='w' encoding='UTF-8'>, seed=None, shuffle_data=None, **kwargs)

Methods

__init__(target, loss_fun[, optimizer, ...])
cpu()	Move all variable into the CPU device.
cuda()	Move all variables into the GPU device.
fit(train_data[, test_data, num_epoch, ...])	Fit the target model according to the given training data.
get_hist_metric([phase, metric, which])	Get history losses.
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.
test_losses
train_losses