brainpy.layers.InstanceNorm#

class brainpy.layers.InstanceNorm(num_channels, epsilon=1e-05, affine=True, bias_initializer=ZeroInit, scale_initializer=OneInit(value=1.0), mode=None, 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_initializer (Initializer, ArrayType, Callable) – an initializer generating the original translation matrix
scale_initializer (Initializer, ArrayType, Callable) – an initializer generating the original scaling matrix

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

Methods

`__init__`(num_channels[, epsilon, affine, ...])
`clear_input`()
`cpu`()	Move all variable into the CPU device.
`cuda`()	Move all variables into the GPU device.
`get_delay_data`(identifier, delay_step, *indices)	Get delay data according to the provided delay steps.
`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.
`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[, node_cls])
`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.
`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.
`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`	Global delay data, which stores the delay variables and corresponding delay targets.
`mode`	Mode of the model, which is useful to control the multiple behaviors of the model.
`name`	Name of the model.