brainpy.nn.base.Network

class brainpy.nn.base.Network(nodes=None, ff_edges=None, fb_edges=None, **kwargs)

Basic Network class for neural network building in BrainPy.

__init__(nodes=None, ff_edges=None, fb_edges=None, **kwargs)

Methods

__init__([nodes, ff_edges, fb_edges])
copy([name, shallow])	Returns a copy of the Node.
feedback(ff_output, **shared_kwargs)	The feedback computation function of a node.
forward(ff[, fb, forced_states, ...])	The main computation function of a network.
get_node(name)
init_fb_conn()	Initialize the feedback connections of the network.
init_fb_output([num_batch])	Set the initial node feedback state.
init_ff_conn()	Initialize the feedforward connections of the network.
init_state([num_batch])	Set the initial node state.
initialize([num_batch])	Initialize the whole network.
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.
plot_node_graph([fig_size, node_size, ...])	Plot the node graph based on NetworkX package
register_implicit_nodes(nodes)
register_implicit_vars(variables)
replace_graph(nodes, ff_edges[, fb_edges])	rtype Network
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.
update_graph(new_nodes, new_ff_edges[, ...])	Update current Model's with new nodes and edges, inplace (a copy is not performed).
vars([method, level, include_self])	Collect all variables in this node and the children nodes.

Attributes

data_pass	Offline fitting method.
entry_nodes	First Nodes in the graph held by the Model.
exit_nodes	Last Nodes in the graph held by the Model.
fb_edges	rtype Sequence[Tuple[Node]]
fb_output	rtype Optional[TypeVar(Tensor, JaxArray, ndarray)]
fb_receivers	Nodes which receive feedback connections.
fb_senders	Nodes which project feedback connections.
feedback_nodes	Nodes which project feedback connections.
feedback_shapes	Output data size.
feedforward_shapes	Input data size.
ff_edges	rtype Sequence[Tuple[Node]]
ff_receivers	Nodes which receive feedforward connections.
ff_senders	Nodes which project feedforward connections.
is_feedback_input_supported
is_feedback_supported
is_initialized	rtype bool
lnodes	rtype Tuple[Node]
name
nodes_has_feedback	Nodes which receive feedback connections.
output_shape	Output data size.
state	Node current internal state.
trainable	Returns True if at least one Node in the Model is trainable.