brainpy.nn.nodes.ANN.BatchNorm1d

class brainpy.nn.nodes.ANN.BatchNorm1d(axis=(0, 1), **kwargs)

1-D batch normalization. The data should be of (b, l, c), where b is the batch dimension, l is the layer dimension, and c is the channel dimension, or of ‘(b, c)’.

Parameters

• axis (int, tuple, list) – axes where the data will be normalized. The feature (channel) axis should be excluded.

• epsilon (float) – a value added to the denominator for numerical stability. Default: 1e-5

• use_bias (bool) – whether to translate data in refactoring. Default: True

• use_scale (bool) – whether to scale data in refactoring. Default: True

• beta_init (brainpy.init.Initializer) – an initializer generating the original translation matrix

• gamma_init (brainpy.init.Initializer) – an initializer generating the original scaling matrix

__init__(axis=(0, 1), **kwargs)

Methods

__init__([axis])
copy([name, shallow])	Returns a copy of the Node.
feedback(ff_output, **shared_kwargs)	The feedback computation function of a node.
forward(ff, **shared_kwargs)	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.
trainable	Returns if the Node can be trained.