Sebastian Fischer
Sebastian Fischer
what i noticed / forgot to mention is that the approach that i have suggested here uses a non-uniform distribution for the number of layers which might not be what...
> but ended up in a cul-de-sac as I need to explicitly define the number of active layers to make the code running and therefore they are not tunable. See...
I think it would be good to add a solution to this problem as a predefined graph so that it is available via `ppl("multi_block")` (probably a better name exists).
@tdhock the PR is WIP, but I was wondering whether you can give some feedback on whether this seems to be useful to you and whether the API is intuitive?
Hey @AbubakerSuliman, thanks for this contribution! Just to be sure: this option is already available in the aorsf package and we just missed to implement this here?
Please make sure the tests work!
Some example: ```r library(mlr3torch) library(mlr3tuning) library(mlr3learners) learner = lrn("classif.mlp", # define the tuning space via the to_tune() tokens # use either 16, 32, or 64 batch_size = to_tune(c(16, 32, 64)),...
batch size, network size, epochs, number of layers
Thanks for this bug report! This bug is caused because XGBoost models cannot simply be saved and loaded. In principle we have a general solution for this available, but it...
Ok, we need to do the same for the `$train_data`: ``` r library(survival) library(mlr3) library(mlr3proba) library(mlr3verse) library(mlr3extralearners) library(xgboost) library(dplyr) #> #> Attaching package: 'dplyr' #> The following object is masked...