PipeOp Torch Classifier — mlr_pipeops_torch_model

Builds a torch classifier and trains it.

Parameters

See LearnerTorch

Input and Output Channels

There is one input channel "input" that takes in ModelDescriptor during traing and a Task of the specified task_type during prediction. The output is NULL during training and a Prediction of given task_type during prediction.

State

A trained LearnerTorchModel.

Internals

A LearnerTorchModel is created by calling model_descriptor_to_learner() on the provided ModelDescriptor that is received through the input channel. Then the parameters are set according to the parameters specified in PipeOpTorchModel and its $train() method is called on the Task stored in the ModelDescriptor.

Super classes

mlr3pipelines::PipeOp -> mlr3pipelines::PipeOpLearner -> PipeOpTorchModel -> PipeOpTorchModelClassif

Methods

Inherited methods

`PipeOpTorchModelClassif$new()`

Creates a new instance of this R6 class.

Usage

PipeOpTorchModelClassif$new(id = "torch_model_classif", 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.

`PipeOpTorchModelClassif$clone()`

The objects of this class are cloneable with this method.

Usage

PipeOpTorchModelClassif$clone(deep = FALSE)

Arguments

deep: Whether to make a deep clone.

Examples

# simple logistic regression

# configure the model descriptor
md = as_graph(po("torch_ingress_num") %>>%
  nn("head") %>>%
  po("torch_loss", "cross_entropy") %>>%
  po("torch_optimizer", "adam"))$train(tsk("iris"))[[1L]]

print(md)
#> <ModelDescriptor: 2 ops>
#> * Ingress:  torch_ingress_num.input: [(NA,4)]
#> * Task:  iris [classif]
#> * Callbacks:  N/A
#> * Optimizer:  Adaptive Moment Estimation
#> * Loss:  Cross Entropy
#> * pointer:  head.output [(NA,3)]

# build the learner from the model descriptor and train it
po_model = po("torch_model_classif", batch_size = 50, epochs = 1)
po_model$train(list(md))
#> $output
#> NULL
#> 
po_model$state
#> $model
#> <learner_torch_model> trained for 1 epoch
#> * Network:  <nn_graph> with 15 parameters
#> * Callbacks:  -
#> * Fields:  network, internal_valid_scores, loss_fn, optimizer, epochs,
#>   callbacks, seed, task_col_info
#> 
#> $param_vals
#> $param_vals$epochs
#> [1] 1
#> 
#> $param_vals$device
#> [1] "auto"
#> 
#> $param_vals$num_threads
#> [1] 1
#> 
#> $param_vals$seed
#> [1] "random"
#> 
#> $param_vals$eval_freq
#> [1] 1
#> 
#> $param_vals$measures_train
#> list()
#> 
#> $param_vals$measures_valid
#> list()
#> 
#> $param_vals$patience
#> [1] 0
#> 
#> $param_vals$min_delta
#> [1] 0
#> 
#> $param_vals$restore_best_weights
#> [1] FALSE
#> 
#> $param_vals$batch_size
#> [1] 50
#> 
#> $param_vals$shuffle
#> [1] TRUE
#> 
#> $param_vals$tensor_dataset
#> [1] FALSE
#> 
#> $param_vals$jit_trace
#> [1] FALSE
#> 
#> 
#> $log
#> Empty data.table (0 rows and 3 cols): stage,class,condition
#> 
#> $train_time
#> elapsed 
#>    0.07 
#> 
#> $task_hash
#> [1] "abc694dd29a7a8ce"
#> 
#> $feature_names
#> [1] "Petal.Length" "Petal.Width"  "Sepal.Length" "Sepal.Width" 
#> 
#> $validate
#> NULL
#> 
#> $mlr3_version
#> [1] ‘1.7.1’
#> 
#> $internal_tuned_values
#> named list()
#> 
#> $data_prototype
#> Empty data.table (0 rows and 5 cols): Species,Petal.Length,Petal.Width,Sepal.Length,Sepal.Width
#> 
#> $train_task
#> 
#> ── <TaskClassif> (150x5): Iris Flowers ─────────────────────────────────────────
#> • Target: Species
#> • Properties: multiclass
#> • Features (4):
#>   • dbl (4): Petal.Length, Petal.Width, Sepal.Length, Sepal.Width
#> • Target classes: setosa, versicolor, virginica
#> 
#> attr(,"class")
#> [1] "learner_state" "list"