mlr_pipeops_nn_max_pool2d {mlr3torch}

2D Max Pooling

Description

Applies a 2D max pooling over an input signal composed of several input planes.

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.

Internals

Calls torch::nn_max_pool2d() during training.

Input and Output Channels

If return_indices is FALSE during construction, there is one input channel 'input' and one output channel 'output'. If return_indices is TRUE, there are two output channels 'output' and 'indices'. For an explanation see PipeOpTorch.

Parameters

• kernel_size :: integer()
  The size of the window. Can be single number or a vector.

• stride :: (⁠integer(1))⁠
  The stride of the window. Can be a single number or a vector. Default: kernel_size

• padding :: integer()
  Implicit zero paddings on both sides of the input. Can be a single number or a tuple (padW,). Default: 0

• dilation :: integer()
  Controls the spacing between the kernel points; also known as the à trous algorithm. Default: 1

• ceil_mode :: logical(1)
  When True, will use ceil instead of floor to compute the output shape. Default: FALSE

Super classes

mlr3pipelines::PipeOp -> mlr3torch::PipeOpTorch -> mlr3torch::PipeOpTorchMaxPool -> PipeOpTorchMaxPool2D

Methods

Public methods

• PipeOpTorchMaxPool2D$new()

• PipeOpTorchMaxPool2D$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

PipeOpTorchMaxPool2D$new(
  id = "nn_max_pool2d",
  return_indices = FALSE,
  param_vals = list()
)

Arguments

id

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

return_indices

(logical(1))
Whether to return the indices. If this is TRUE, there are two output channels "output" and "indices".

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

PipeOpTorchMaxPool2D$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_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_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_max_pool2d")
pipeop
# The available parameters
pipeop$param_set