brainpy.nn.base.RecurrentNode#

class brainpy.nn.base.RecurrentNode(name=None, input_shape=None, trainable=True, state_trainable=False)[source]#

Basic class for recurrent node.

The supports for the recurrent node are:

  • Self-connection when using plot_node_graph() function

  • Set trainable state with state_trainable=True.

Parameters

  • name (str) – The name of the node.

  • input_shape (int, sequence of int) – The shape of the input data.

  • state_trainable (bool) – Whether train the model state or not. Default is False.

  • trainable (bool) –

    Whether train the model or not. Default is True.

    Changed in version 2.1.8.1: The faultvalue of trainable changed from False to True in version 2.1.8.1.

__init__(name=None, input_shape=None, trainable=True, state_trainable=False)[source]#

Methods

__init__([name, input_shape, trainable, ...])

copy([name, shallow])

Returns a copy of the Node.

feedback(ff_output, **shared_kwargs)

The feedback computation function of a node.

forward(ff[, fb])

The feedforward computation function of a node.

init_fb_conn()

Initialize the feedback connections.

init_fb_output([num_batch])

Set the initial node feedback state.

init_ff_conn()

Initialize the feedforward connections.

init_state([num_batch])

Set the initial node state.

initialize([num_batch])

Initialize the node.

load_states(filename[, verbose])

Load the model states.

nodes([method, level, include_self])

Collect all children nodes.

offline_fit(targets, ffs[, fbs])

Offline training interface.

online_fit(target, ff[, fb])

Online training fitting interface.

online_init()

Online training initialization interface.

register_implicit_nodes(nodes)

register_implicit_vars(variables)

save_states(filename[, variables])

Save the model states.

set_fb_output(state)

Safely set the feedback state of the node.

set_feedback_shapes(fb_shapes)

set_feedforward_shapes(feedforward_shapes)

set_output_shape(shape)

set_state(state)

Safely set the state of the node.

train_vars([method, level, include_self])

The shortcut for retrieving all trainable variables.

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

data_pass

Offline fitting method.

fb_output

rtype

Optional[TypeVar(Tensor, JaxArray, ndarray)]

feedback_shapes

Output data size.

feedforward_shapes

Input data size.

is_feedback_input_supported

is_feedback_supported

is_initialized

rtype

bool

name

output_shape

Output data size.

state

Node current internal state.

state_trainable

Returns if the Node can be trained.

train_state

trainable

Returns if the Node can be trained.