brainpy.dyn.layers.InstanceNorm#

class brainpy.dyn.layers.InstanceNorm(num_channels, epsilon=1e-05, affine=True, bias_init=ZeroInit, scale_init=OneInit(value=1.0), mode=TrainingMode, name=None)[source]#

Instance normalization layer.

This layer normalizes the data within each feature. It can be regarded as a group normalization layer in which group_size equals to 1.

Parameters
  • num_channels (int) – The number of channels expected in input.

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

  • affine (bool) – A boolean value that when set to True, this module has learnable per-channel affine parameters initialized to ones (for weights) and zeros (for biases). Default: True.

  • bias_init (Initializer) – an initializer generating the original translation matrix

  • scale_init (Initializer) – an initializer generating the original scaling matrix

__init__(num_channels, epsilon=1e-05, affine=True, bias_init=ZeroInit, scale_init=OneInit(value=1.0), mode=TrainingMode, name=None)[source]#

Methods

__init__(num_channels[, epsilon, affine, ...])

clear_input()

get_delay_data(identifier, delay_step, *indices)

Get delay data according to the provided delay steps.

load_states(filename[, verbose])

Load the model states.

nodes([method, level, include_self])

Collect all children nodes.

offline_fit(target, fit_record)

offline_init()

online_fit(target, fit_record)

online_init()

register_delay(identifier, delay_step, ...)

Register delay variable.

register_implicit_nodes(*nodes, **named_nodes)

register_implicit_vars(*variables, ...)

reset([batch_size])

Reset function which reset the whole variables in the model.

reset_local_delays([nodes])

Reset local delay variables.

reset_state([batch_size])

Reset function which reset the states in the model.

save_states(filename[, variables])

Save the model states.

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(sha, x)

The function to specify the updating rule.

update_local_delays([nodes])

Update local delay variables.

vars([method, level, include_self])

Collect all variables in this node and the children nodes.

Attributes

global_delay_data

mode

Mode of the model, which is useful to control the multiple behaviors of the model.

name

Name of the model.