mxnet.gluon.nn.LayerNorm¶

class mxnet.gluon.nn.LayerNorm(axis=-1, epsilon=1e-05, center=True, scale=True, beta_initializer='zeros', gamma_initializer='ones', in_channels=0, prefix=None, params=None)[source]¶

Applies layer normalization to the n-dimensional input array. This operator takes an n-dimensional input array and normalizes the input using the given axis:

\[out = \frac{x - mean[data, axis]}{ \sqrt{Var[data, axis] + \epsilon}} * gamma + beta\]

Parameters

axis (int, default -1) – The axis that should be normalized. This is typically the axis of the channels.
epsilon (float, default 1e-5) – Small float added to variance to avoid dividing by zero.
center (bool, default True) – If True, add offset of beta to normalized tensor. If False, beta is ignored.
scale (bool, default True) – If True, multiply by gamma. If False, gamma is not used.
beta_initializer (str or Initializer, default ‘zeros’) – Initializer for the beta weight.
gamma_initializer (str or Initializer, default ‘ones’) – Initializer for the gamma weight.
in_channels (int, default 0) – Number of channels (feature maps) in input data. If not specified, initialization will be deferred to the first time forward is called and in_channels will be inferred from the shape of input data.

Inputs:

data: input tensor with arbitrary shape.

Outputs:

out: output tensor with the same shape as data.

References

Layer Normalization

Examples

>>> # Input of shape (2, 5)
>>> x = mx.nd.array([[1, 2, 3, 4, 5], [1, 1, 2, 2, 2]])
>>> # Layer normalization is calculated with the above formula
>>> layer = LayerNorm()
>>> layer.initialize(ctx=mx.cpu(0))
>>> layer(x)
[[-1.41421    -0.707105    0.          0.707105    1.41421   ]
 [-1.2247195  -1.2247195   0.81647956  0.81647956  0.81647956]]
<NDArray 2x5 @cpu(0)>

__init__(axis=-1, epsilon=1e-05, center=True, scale=True, beta_initializer='zeros', gamma_initializer='ones', in_channels=0, prefix=None, params=None)[source]¶: Initialize self. See help(type(self)) for accurate signature.

Methods

`__init__`([axis, epsilon, center, scale, …])	Initialize self.
`apply`(fn)	Applies `fn` recursively to every child block as well as self.
`cast`(dtype)	Cast this Block to use another data type.
`collect_params`([select])	Returns a `ParameterDict` containing this `Block` and all of its children’s Parameters(default), also can returns the select `ParameterDict` which match some given regular expressions.
`export`(path[, epoch])	Export HybridBlock to json format that can be loaded by SymbolBlock.imports, mxnet.mod.Module or the C++ interface.
`forward`(x, *args)	Defines the forward computation.
`hybrid_forward`(F, data, gamma, beta)	Overrides to construct symbolic graph for this Block.
`hybridize`([active])	Activates or deactivates `HybridBlock` s recursively.
`infer_shape`(*args)	Infers shape of Parameters from inputs.
`infer_type`(*args)	Infers data type of Parameters from inputs.
`initialize`([init, ctx, verbose, force_reinit])	Initializes `Parameter` s of this `Block` and its children.
`load_parameters`(filename[, ctx, …])	Load parameters from file previously saved by save_parameters.
`load_params`(filename[, ctx, allow_missing, …])	[Deprecated] Please use load_parameters.
`name_scope`()	Returns a name space object managing a child `Block` and parameter names.
`register_child`(block[, name])	Registers block as a child of self.
`register_forward_hook`(hook)	Registers a forward hook on the block.
`register_forward_pre_hook`(hook)	Registers a forward pre-hook on the block.
`save_parameters`(filename)	Save parameters to file.
`save_params`(filename)	[Deprecated] Please use save_parameters.
`summary`(*inputs)	Print the summary of the model’s output and parameters.

Attributes

`name`	Name of this `Block`, without ‘_’ in the end.
`params`	Returns this `Block`’s parameter dictionary (does not include its children’s parameters).
`prefix`	Prefix of this `Block`.