LearnerTorch.md
In mlr3torch: Deep Learning with 'mlr3'

printer

Code
  lrn("classif.mlp", callbacks = list(t_clbk("history"), t_clbk("progress")))
Output

  -- <LearnerTorchMLP> (classif.mlp): Multi Layer Perceptron ---------------------
  * Model: -
  * Parameters: device=auto, num_threads=1, num_interop_threads=1, seed=random,
  eval_freq=1, measures_train=<list>, measures_valid=<list>, patience=0,
  min_delta=0, shuffle=TRUE, tensor_dataset=FALSE, jit_trace=FALSE,
  neurons=integer(0), p=0.5, activation=<nn_relu>, activation_args=<list>
  * Validate: NULL
  * Packages: mlr3, mlr3torch, torch, and progress
  * Predict Types: [response] and prob
  * Feature Types: integer, numeric, and lazy_tensor
  * Encapsulation: none (fallback: -)
  * Properties: internal_tuning, marshal, multiclass, twoclass, and validation
  * Other settings: use_weights = 'error'
  * Optimizer: adam
  * Loss: cross_entropy
  * Callbacks: history,progress