mlr_pipeops_nn_merge_cat {mlr3torch}

Merge by Concatenation

Description

Concatenates multiple tensors on a given dimension. No broadcasting rules are applied here, you must reshape the tensors before to have the same shape.

Input and Output Channels

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

PipeOpTorchMerges has either a vararg input channel if the constructor argument innum is not set, or input channels "input1", ..., "input<innum>". There is one output channel "output". For an explanation see PipeOpTorch.

State

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

Credit

Part of this documentation have been copied or adapted from the documentation of torch.

Parameters

• dim :: integer(1)
  The dimension along which to concatenate the tensors.

Internals

Calls nn_merge_cat() when trained.

Super classes

mlr3pipelines::PipeOp -> mlr3torch::PipeOpTorch -> mlr3torch::PipeOpTorchMerge -> PipeOpTorchMergeCat

Methods

Public methods

• PipeOpTorchMergeCat$new()

• PipeOpTorchMergeCat$speak()

• PipeOpTorchMergeCat$clone()

Inherited methods

• mlr3pipelines::PipeOp$help()
• mlr3pipelines::PipeOp$predict()
• mlr3pipelines::PipeOp$print()
• mlr3pipelines::PipeOp$train()
• mlr3torch::PipeOpTorch$shapes_out()

Method new()

Creates a new instance of this R6 class.

Usage

PipeOpTorchMergeCat$new(id = "nn_merge_cat", innum = 0, param_vals = list())

Arguments

id

(character(1))
Identifier of the resulting object.

innum

(integer(1))
The number of inputs. Default is 0 which means there is one vararg input channel.

param_vals

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

Method speak()

What does the cat say?

Usage

PipeOpTorchMergeCat$speak()

Method clone()

The objects of this class are cloneable with this method.

Usage

PipeOpTorchMergeCat$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

See Also

Other PipeOps: 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_layer_norm, 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_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_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

Examples

# Construct the PipeOp
pipeop = po("nn_merge_cat")
pipeop
# The available parameters
pipeop$param_set