mlr_pipeops_nn_max_pool2d function

2D Max Pooling

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

nn_module

Calls torch::nn_max_pool2d() during training.

State

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

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

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.

Examples

# Construct the PipeOp
pipeop = po("nn_max_pool2d")
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_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_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 -> mlr3torch::PipeOpTorchMaxPool -> PipeOpTorchMaxPool2D

Methods

Public methods

• list("PipeOpTorchMaxPool2D$new()")

• list("PipeOpTorchMaxPool2D$clone()")

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.