Output head for classification and regresssion.
Details
When the method $shapes_out()
does not have access to the task, it returns c(NA, NA)
.
When this PipeOp
is trained however, the model descriptor has the correct output shape.
nn_module
Calls torch::nn_linear()
with the input and output features inferred from the input shape / task.
For
binary classification, the output dimension is 1.
multiclass classification, the output dimension is the number of classes.
regression, the output dimension is 1.
Input and Output Channels
One input channel called "input"
and one output channel called "output"
.
For an explanation see PipeOpTorch
.
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_ft_cls
,
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_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
Super classes
mlr3pipelines::PipeOp
-> mlr3torch::PipeOpTorch
-> PipeOpTorchHead
Methods
Method new()
Creates a new instance of this R6 class.
Usage
PipeOpTorchHead$new(id = "nn_head", 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.
Examples
# Construct the PipeOp
pipeop = po("nn_head")
pipeop
#> PipeOp: <nn_head> (not trained)
#> values: <list()>
#> Input channels <name [train type, predict type]>:
#> input [ModelDescriptor,Task]
#> Output channels <name [train type, predict type]>:
#> output [ModelDescriptor,Task]
# The available parameters
pipeop$param_set
#> <ParamSet(1)>
#> id class lower upper nlevels default value
#> <char> <char> <num> <num> <num> <list> <list>
#> 1: bias ParamLgl NA NA 2 TRUE [NULL]