kindling (development version)

Ongoing features

• More visualization supports

  • NN architecture plot

kindling 0.3.2

Bug fixes

• "linear" used as an activations/output_activation value no longer crashes at training time. It was previously resolved to torch::nnf_linear() (an affine transform requiring its own weight/bias), instead of behaving as an identity/no-op activation. "linear" now consistently maps to identity() (#21).

Documentation

• Added missing @examples for act_funs(), args(), and early_stop().

• Fixed the {tidymodels} example: loading Ionosphere via box::use(mlbench[Ionosphere]) failed because {mlbench} does not export its datasets through NAMESPACE/LazyData. The example now uses data(Ionosphere, package = "mlbench") instead.

• Usage examples from README gets transferred to vignettes/kindling.Rmd.

kindling 0.3.1

New features

• autoplot() and plot() methods for nn_fit objects now visualize the training loss history, with optional validation loss and an early-stopping marker when early stopping fires.

• autoplot_diagnostics() and plot_diagnostics() produce prediction diagnostic plots for nn_fit objects: residuals vs fitted and actual vs fitted panels for regression, one panel per output for multi-output regression, and a confusion matrix heatmap for classification.

• {vip} is removed from {kindling}’s package dependencies, and moved to “Suggests” instead to avoid dependency redundancy.

kindling 0.3.0

New Experimental functions

• Generalized nn_module() expression generator to generate torch::nn_module() expression for the same sequential NN architectures

  • This is how you use to generate nn_module() for 1D-CNN (Convolutional Neural Networks) with 3 hidden layers:

  nn_module_generator(
      nn_name = "CNN1DClassifier",
      nn_layer = "nn_conv1d",
      layer_arg_fn = ~ if (.is_output) {
          list(.in, .out)
      } else {
          list(
              in_channels = .in,
              out_channels = .out,
              kernel_size = 3L,
              stride = 1L,
              padding = 1L 
          )
      },
      after_output_transform = ~ .$mean(dim = 2),
      last_layer_args = list(kernel_size = 1, stride = 2),
      hd_neurons = c(16, 32, 64),
      no_x = 1,
      no_y = 10,
      activations = "relu"
  )

• train_nn() to execute nn_module_generator()

  • nn_arch() must be supplied to inherit extra arguments from nn_module_generator() function.
  • Allows early stopping if early_stopping is supplied with early_stop().
  • Supported with several data types: matrix, data.frame, dataset ({torch} dataset), and a formula interface.
  • train_nnsnip() is now provided to bridge train_nn() with {tidymodels}

• You can supply customized activation function under act_funs() with new_act_fn().

  • Activation functions that especially don’t exist on torch::nnf_*().
  • Supply the argument with a function
  • The function supplied into new_act_fn() must return a torch tensor object.
  • Example: act_funs(new_act_fn(torch::torch_tanh)) or act_funs(new_act_fn(\(x) torch::torch_tanh(x)))
  • Use .name as a displayed name of the custom activation function.

Superset

• act_funs() as a DSL function now supports index-style parameter specification for parametric activation functions

  • Activation functions can now be modified using [ syntax (e.g. softplus[beta = 0.2])
  • The current args() (e.g. softplus = args(beta = 0.2)) is now superseded by that.

Bug Fixes

• No suffix generated for 13 by ordinal_gen(). Now fixed.

• hd_neurons for both ffnn_generator() and rnn_generator() accepts empty arguments, which implies there’s no hidden layers applied.

kindling 0.2.0

New features

• Added regularization support for neural network models

  • L1 regularization (Lasso) for feature selection via mixture = 1
  • L2 regularization (Ridge) for weight decay via mixture = 0
  • Elastic Net combining L1 and L2 penalties via 0 < mixture < 1
  • Controlled via penalty (regularization strength) and mixture (L1/L2 balance) parameters
  • Follows tidymodels conventions for consistency with glmnet and other packages

• n_hlayers() now fully supports tuning the number of hidden layers

• hidden_neurons() gains support for discrete values via the disc_values argument

  • e.g. disc_values = c(32L, 64L, 128L, 256L)) is now allowed
  • This allows tuning over specific common hidden unit sizes instead of (or in addition to) a continuous range

Implementation fixes

• Tuning methods and grid_depth() is now fixed

  • Parameter space for the number of hidden layers is now fixed and active
  • Corrected parameter space handling for n_hlayers (no more invalid sampling when x > 1)
  • Uses tidyr::expand_grid(), not purrr::cross*()
  • Fix randomization of parameter space which will produce NAs outside from {kindling}‘s own ’dials’
  • No more list columns when n_hlayers = 1

• The supported models now use hardhat::mold(), instead of model.frame() and model.matrix().

Documentation

• Add a vignette to showcase the comparison with other similar packages

• The package description has been clarified

• Vignette to showcase the comparison with other similar packages

• hidden_neurons parameter now supports discrete values specification

  • Users can specify exact neuron counts via values parameter (e.g., hidden_neurons(values = c(32, 64, 128)))
  • Maintains backward compatibility with range-based parameters (e.g., hidden_neurons(range = c(8L, 512L)) / hidden_neurons(c(8L, 512L)))

• Added \value documentation to kindling-nn-wrappers for CRAN compliance

• Documented argument handling and list-column unwrapping in tidymodels wrapper functions

• Clarified the relationship between grid_depth() and wrapper functions

kindling 0.1.0

• Initial CRAN release
• Higher-level interface for torch package to define, train, and tune neural networks
• Support for feedforward (multi-layer perceptron) and recurrent networks (RNN, LSTM, GRU)
• Integration with tidymodels ecosystem (parsnip, workflows, recipes, tuning)
• Variable importance plots and network visualization tools

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