DDESONN - A Deep Dynamic Experimental Self-Organizing Neural Network
Framework
Provides a fully native R deep learning framework for
constructing, training, evaluating, and inspecting Deep Dynamic
Ensemble Self Organizing Neural Networks at research scale. The
core engine is an object oriented R6 class-based implementation
with explicit control over layer layout, dimensional flow,
forward propagation, back propagation, and transparent
optimizer state updates. The framework does not rely on
external deep learning back ends, enabling direct inspection of
model state, reproducible numerical behavior, and fine grained
architectural control without requiring compiled dependencies
or graphics processing unit specific run times. Users can
define dimension agnostic single layer or deep multi-layer
networks without hard coded architecture limits, with per layer
configuration vectors for activation functions, derivatives,
dropout behavior, and initialization strategies automatically
aligned to network depth through controlled replication or
truncation. Reproducible workflows can be executed through high
level helpers for fit, run, and predict across binary
classification, multi-class classification, and regression
modes. Training pipelines support optional self organization,
adaptive learning rate behavior, and structured ensemble
orchestration in which candidate models are evaluated under
user specified performance metrics and selectively promoted or
pruned to refine a primary ensemble, enabling controlled
ensemble evolution over successive runs. Ensemble evaluation
includes fused prediction strategies in which member outputs
may be combined through weighted averaging, arithmetic
averaging, or voting mechanisms to generate consolidated
metrics for research level comparison and reproducible per-seed
assessment. The framework supports multiple optimization
approaches, including stochastic gradient descent, adaptive
moment estimation, and look ahead methods, alongside
configurable regularization controls such as L1, L2, and mixed
penalties with separate weight and bias update logic.
Evaluation features provide threshold tuning, relevance
scoring, receiver operating characteristic and precision recall
curve generation, area under curve computation, regression
error diagnostics, and report ready metric outputs. The package
also includes artifact path management, debug state utilities,
structured run level metadata persistence capturing seeds,
configuration states, thresholds, metrics, ensemble
transitions, fused evaluation artifacts, and model identifiers,
as well as reproducible scripts and vignettes documenting end
to end experiments. Kingma and Ba (2015)
<doi:10.48550/arXiv.1412.6980> "Adam: A Method for Stochastic
Optimization". Hinton et al. (2012)
<https://www.cs.toronto.edu/~tijmen/csc321/slides/lecture_slides_lec6.pdf>
"Neural Networks for Machine Learning (RMSprop lecture notes)".
Duchi et al. (2011) <https://jmlr.org/papers/v12/duchi11a.html>
"Adaptive Subgradient Methods for Online Learning and
Stochastic Optimization". Zeiler (2012)
<doi:10.48550/arXiv.1212.5701> "ADADELTA: An Adaptive Learning
Rate Method". Zhang et al. (2019)
<doi:10.48550/arXiv.1907.08610> "Lookahead Optimizer: k steps
forward, 1 step back". You et al. (2019)
<doi:10.48550/arXiv.1904.00962> "Large Batch Optimization for
Deep Learning: Training BERT in 76 minutes (LAMB)". McMahan et
al. (2013) <https://research.google.com/pubs/archive/41159.pdf>
"Ad Click Prediction: a View from the Trenches
(FTRL-Proximal)". Klambauer et al. (2017)
<https://proceedings.neurips.cc/paper/6698-self-normalizing-neural-networks.pdf>
"Self-Normalizing Neural Networks (SELU)". Maas et al. (2013)
<https://ai.stanford.edu/~amaas/papers/relu_hybrid_icml2013_final.pdf>
"Rectifier Nonlinearities Improve Neural Network Acoustic
Models (Leaky ReLU / rectifiers)".
