mlr_pipeops_nn_conv_transpose1d: Transpose 1D Convolution

In mlr3torch: Deep Learning with 'mlr3'

Transpose 1D Convolution

Description

Applies a 1D transposed convolution operator over an input signal composed of several input planes, sometimes also called "deconvolution".

nn_module

Calls nn_conv_transpose1d. The parameter in_channels is inferred as the second dimension of the input tensor.

Parameters

• out_channels :: integer(1)
  Number of output channels produce by the convolution.

• kernel_size :: integer()
  Size of the convolving kernel.

• stride :: integer()
  Stride of the convolution. Default: 1.

• padding :: ⁠ ⁠integer()'
  ‘dilation * (kernel_size - 1) - padding’ zero-padding will be added to both sides of the input. Default: 0.

• output_padding ::integer()
  Additional size added to one side of the output shape. Default: 0.

• groups :: integer()
  Number of blocked connections from input channels to output channels. Default: 1

• bias :: logical(1)
  If ‘True’, adds a learnable bias to the output. Default: ‘TRUE’.

• dilation :: integer()
  Spacing between kernel elements. Default: 1.

• padding_mode :: character(1)
  The padding mode. One of "zeros", "reflect", "replicate", or "circular". Default is "zeros".

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.

Super classes

mlr3pipelines::PipeOp -> mlr3torch::PipeOpTorch -> mlr3torch::PipeOpTorchConvTranspose -> PipeOpTorchConvTranspose1D

Methods

Public methods

• PipeOpTorchConvTranspose1D$new()

• PipeOpTorchConvTranspose1D$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

PipeOpTorchConvTranspose1D$new(id = "nn_conv_transpose1d", 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

PipeOpTorchConvTranspose1D$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

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_transpose2d, mlr_pipeops_nn_conv_transpose3d, mlr_pipeops_nn_dropout, mlr_pipeops_nn_elu, mlr_pipeops_nn_flatten, mlr_pipeops_nn_ft_cls, mlr_pipeops_nn_ft_transformer_block, mlr_pipeops_nn_geglu, 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_identity, 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_cat, mlr_pipeops_nn_merge_prod, mlr_pipeops_nn_merge_sum, mlr_pipeops_nn_prelu, mlr_pipeops_nn_reglu, 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_tokenizer_categ, mlr_pipeops_nn_tokenizer_num, 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

Examples

# Construct the PipeOp
pipeop = po("nn_conv_transpose1d", kernel_size = 3, out_channels = 2)
pipeop
# The available parameters
pipeop$param_set

mlr3torch documentation built on Aug. 26, 2025, 5:09 p.m.