mlr_pipeops_nn_layer_norm function

Layer Normalization

Applies Layer Normalization for last certain number of dimensions.

nn_module

Calls torch::nn_layer_norm() when trained. The parameter normalized_shape is inferred as the dimensions of the last dims dimensions of the input shape.

Parameters

• dims :: integer(1)

  The number of dimensions over which will be normalized (starting from the last dimension).

• elementwise_affine :: logical(1)

  Whether to learn affine-linear parameters initialized to 1 for weights and to 0 for biases. The default is TRUE.

• eps :: numeric(1)

  A value added to the denominator for numerical stability.

State

The state is the value calculated by the public method $shapes_out().

Input and Output Channels

One input channel called "input" and one output channel called "output". For an explanation see PipeOpTorch.

Examples

# Construct the PipeOp
pipeop = po("nn_layer_norm", dims = 1)
pipeop
# The available parameters
pipeop$param_set

See Also

Other PipeOps: mlr_pipeops_nn_adaptive_avg_pool1d, mlr_pipeops_nn_adaptive_avg_pool2d, mlr_pipeops_nn_adaptive_avg_pool3d, mlr_pipeops_nn_avg_pool1d, mlr_pipeops_nn_avg_pool2d, mlr_pipeops_nn_avg_pool3d, mlr_pipeops_nn_batch_norm1d, mlr_pipeops_nn_batch_norm2d, mlr_pipeops_nn_batch_norm3d, mlr_pipeops_nn_block, mlr_pipeops_nn_celu, mlr_pipeops_nn_conv1d, mlr_pipeops_nn_conv2d, mlr_pipeops_nn_conv3d, mlr_pipeops_nn_conv_transpose1d, mlr_pipeops_nn_conv_transpose2d, mlr_pipeops_nn_conv_transpose3d, mlr_pipeops_nn_dropout, mlr_pipeops_nn_elu, mlr_pipeops_nn_flatten, mlr_pipeops_nn_gelu, mlr_pipeops_nn_glu, mlr_pipeops_nn_hardshrink, mlr_pipeops_nn_hardsigmoid, mlr_pipeops_nn_hardtanh, mlr_pipeops_nn_head, mlr_pipeops_nn_leaky_relu, mlr_pipeops_nn_linear, mlr_pipeops_nn_log_sigmoid, mlr_pipeops_nn_max_pool1d, mlr_pipeops_nn_max_pool2d, mlr_pipeops_nn_max_pool3d, mlr_pipeops_nn_merge, mlr_pipeops_nn_merge_cat, mlr_pipeops_nn_merge_prod, mlr_pipeops_nn_merge_sum, mlr_pipeops_nn_prelu, mlr_pipeops_nn_relu, mlr_pipeops_nn_relu6, mlr_pipeops_nn_reshape, mlr_pipeops_nn_rrelu, mlr_pipeops_nn_selu, mlr_pipeops_nn_sigmoid, mlr_pipeops_nn_softmax, mlr_pipeops_nn_softplus, mlr_pipeops_nn_softshrink, mlr_pipeops_nn_softsign, mlr_pipeops_nn_squeeze, mlr_pipeops_nn_tanh, mlr_pipeops_nn_tanhshrink, mlr_pipeops_nn_threshold, mlr_pipeops_nn_unsqueeze, mlr_pipeops_torch_ingress, mlr_pipeops_torch_ingress_categ, mlr_pipeops_torch_ingress_ltnsr, mlr_pipeops_torch_ingress_num, mlr_pipeops_torch_loss, mlr_pipeops_torch_model, mlr_pipeops_torch_model_classif, mlr_pipeops_torch_model_regr

Super classes

mlr3pipelines::PipeOp -> mlr3torch::PipeOpTorch -> PipeOpTorchLayerNorm

Methods

Public methods

• list("PipeOpTorchLayerNorm$new()")

• list("PipeOpTorchLayerNorm$clone()")

Method new()

Creates a new instance of this R6 class.

Usage

PipeOpTorchLayerNorm$new(id = "nn_layer_norm", param_vals = list())

Arguments

• id: (character(1))

   Identifier of the resulting object.
  

• param_vals: (list())

   List of hyperparameter settings, overwriting the hyperparameter settings that would otherwise be set during construction.
  

Method clone()

The objects of this class are cloneable with this method.

Usage

PipeOpTorchLayerNorm$clone(deep = FALSE)

Arguments

• deep: Whether to make a deep clone.