| mlr_pipeops_nn_merge_prod {mlr3torch} | R Documentation |
Merge by Product
Description
Calculates the product of all input tensors.
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 $shapes_out().
Credit
Part of this documentation have been copied or adapted from the documentation of torch.
Parameters
No parameters.
Internals
Calls nn_merge_prod() when trained.
Super classes
mlr3pipelines::PipeOp -> mlr3torch::PipeOpTorch -> mlr3torch::PipeOpTorchMerge -> PipeOpTorchMergeProd
Methods
Public methods
Inherited methods
Method new()
Creates a new instance of this R6 class.
Usage
PipeOpTorchMergeProd$new(id = "nn_merge_prod", 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 clone()
The objects of this class are cloneable with this method.
Usage
PipeOpTorchMergeProd$clone(deep = FALSE)
Arguments
deepWhether to make a deep clone.
See Also
Other PipeOps:
mlr_pipeops_nn_avg_pool1d,
mlr_pipeops_nn_avg_pool2d,
mlr_pipeops_nn_avg_pool3d,
mlr_pipeops_nn_batch_norm1d,
mlr_pipeops_nn_batch_norm2d,
mlr_pipeops_nn_batch_norm3d,
mlr_pipeops_nn_block,
mlr_pipeops_nn_celu,
mlr_pipeops_nn_conv1d,
mlr_pipeops_nn_conv2d,
mlr_pipeops_nn_conv3d,
mlr_pipeops_nn_conv_transpose1d,
mlr_pipeops_nn_conv_transpose2d,
mlr_pipeops_nn_conv_transpose3d,
mlr_pipeops_nn_dropout,
mlr_pipeops_nn_elu,
mlr_pipeops_nn_flatten,
mlr_pipeops_nn_gelu,
mlr_pipeops_nn_glu,
mlr_pipeops_nn_hardshrink,
mlr_pipeops_nn_hardsigmoid,
mlr_pipeops_nn_hardtanh,
mlr_pipeops_nn_head,
mlr_pipeops_nn_layer_norm,
mlr_pipeops_nn_leaky_relu,
mlr_pipeops_nn_linear,
mlr_pipeops_nn_log_sigmoid,
mlr_pipeops_nn_max_pool1d,
mlr_pipeops_nn_max_pool2d,
mlr_pipeops_nn_max_pool3d,
mlr_pipeops_nn_merge,
mlr_pipeops_nn_merge_cat,
mlr_pipeops_nn_merge_sum,
mlr_pipeops_nn_prelu,
mlr_pipeops_nn_relu,
mlr_pipeops_nn_relu6,
mlr_pipeops_nn_reshape,
mlr_pipeops_nn_rrelu,
mlr_pipeops_nn_selu,
mlr_pipeops_nn_sigmoid,
mlr_pipeops_nn_softmax,
mlr_pipeops_nn_softplus,
mlr_pipeops_nn_softshrink,
mlr_pipeops_nn_softsign,
mlr_pipeops_nn_squeeze,
mlr_pipeops_nn_tanh,
mlr_pipeops_nn_tanhshrink,
mlr_pipeops_nn_threshold,
mlr_pipeops_torch_ingress,
mlr_pipeops_torch_ingress_categ,
mlr_pipeops_torch_ingress_ltnsr,
mlr_pipeops_torch_ingress_num,
mlr_pipeops_torch_loss,
mlr_pipeops_torch_model,
mlr_pipeops_torch_model_classif,
mlr_pipeops_torch_model_regr
Examples
# Construct the PipeOp
pipeop = po("nn_merge_prod")
pipeop
# The available parameters
pipeop$param_set