tests/testthat/_snaps/mlp.md

In tidymodels/parsnip: A Common API to Modeling and Analysis Functions

updating

Code
  mlp(mode = "classification", hidden_units = 2) %>% set_engine("nnet", Hess = FALSE) %>%
    update(hidden_units = tune(), Hess = tune())
Output
  Single Layer Neural Network Model Specification (classification)

  Main Arguments:
    hidden_units = tune()

  Engine-Specific Arguments:
    Hess = tune()

  Computational engine: nnet

bad input

Code
  mlp(mode = "time series")
Condition
  Error in `mlp()`:
  ! "time series" is not a known mode for model `mlp()`.

Code
  translate(mlp(mode = "classification") %>% set_engine("wat?"))
Condition
  Error in `set_engine()`:
  x Engine "wat?" is not supported for `mlp()`
  i See `show_engines("mlp")`.

check_args() works

Code
  spec <- mlp(penalty = -1) %>% set_engine("keras") %>% set_mode("classification")
  fit(spec, class ~ ., hpc)
Condition
  Error in `fit()`:
  ! `penalty` must be a number larger than or equal to 0 or `NULL`, not the number -1.

Code
  spec <- mlp(dropout = -1) %>% set_engine("keras") %>% set_mode("classification")
  fit(spec, class ~ ., hpc)
Condition
  Error in `fit()`:
  ! `dropout` must be a number between 0 and 1 or `NULL`, not the number -1.

Code
  spec <- mlp(dropout = 1, penalty = 3) %>% set_engine("keras") %>% set_mode(
    "classification")
  fit(spec, class ~ ., hpc)
Condition
  Error in `fit()`:
  ! Both weight decay and dropout should not be specified.

tunables

Code
  mlp() %>% set_engine("brulee") %>% tunable()
Output
  # A tibble: 12 x 5
     name          call_info        source     component component_id
     <chr>         <list>           <chr>      <chr>     <chr>       
   1 epochs        <named list [3]> model_spec mlp       main        
   2 hidden_units  <named list [3]> model_spec mlp       main        
   3 activation    <named list [3]> model_spec mlp       main        
   4 penalty       <named list [2]> model_spec mlp       main        
   5 mixture       <named list [2]> model_spec mlp       engine      
   6 dropout       <named list [2]> model_spec mlp       main        
   7 learn_rate    <named list [3]> model_spec mlp       main        
   8 momentum      <named list [3]> model_spec mlp       engine      
   9 batch_size    <named list [2]> model_spec mlp       engine      
  10 class_weights <named list [2]> model_spec mlp       engine      
  11 stop_iter     <named list [2]> model_spec mlp       engine      
  12 rate_schedule <named list [3]> model_spec mlp       engine

Code
  mlp() %>% set_engine("brulee_two_layer") %>% tunable()
Output
  # A tibble: 14 x 5
     name           call_info        source     component component_id
     <chr>          <list>           <chr>      <chr>     <chr>       
   1 epochs         <named list [3]> model_spec mlp       main        
   2 hidden_units   <named list [3]> model_spec mlp       main        
   3 hidden_units_2 <named list [3]> model_spec mlp       engine      
   4 activation     <named list [3]> model_spec mlp       main        
   5 activation_2   <named list [3]> model_spec mlp       engine      
   6 penalty        <named list [2]> model_spec mlp       main        
   7 mixture        <named list [2]> model_spec mlp       engine      
   8 dropout        <named list [2]> model_spec mlp       main        
   9 learn_rate     <named list [3]> model_spec mlp       main        
  10 momentum       <named list [3]> model_spec mlp       engine      
  11 batch_size     <named list [2]> model_spec mlp       engine      
  12 class_weights  <named list [2]> model_spec mlp       engine      
  13 stop_iter      <named list [2]> model_spec mlp       engine      
  14 rate_schedule  <named list [3]> model_spec mlp       engine

Code
  mlp() %>% set_engine("nnet") %>% tunable()
Output
  # A tibble: 3 x 5
    name         call_info        source     component component_id
    <chr>        <list>           <chr>      <chr>     <chr>       
  1 hidden_units <named list [2]> model_spec mlp       main        
  2 penalty      <named list [2]> model_spec mlp       main        
  3 epochs       <named list [2]> model_spec mlp       main

Code
  mlp() %>% set_engine("keras") %>% tunable()
Output
  # A tibble: 5 x 5
    name         call_info        source     component component_id
    <chr>        <list>           <chr>      <chr>     <chr>       
  1 hidden_units <named list [2]> model_spec mlp       main        
  2 penalty      <named list [2]> model_spec mlp       main        
  3 dropout      <named list [2]> model_spec mlp       main        
  4 epochs       <named list [2]> model_spec mlp       main        
  5 activation   <named list [2]> model_spec mlp       main

tidymodels/parsnip documentation built on Feb. 19, 2025, 2:10 a.m.