jensandClaude Sonnet 4.6
3698c85ed4
Previously a single shared-weight Entity processed every time step. Now: - Shared mode (1 layer via make_layer): original behaviour unchanged - Unrolled mode (N layers via make_unrolled): layers[t] owns W_t, b_v_t, b_h_t New API: make_unrolled(T, sensory_size, h_size, ...) → list[Layer] next_entity() → Entity for the upcoming step() call current_entity() → Entity from the most recent step() call is_shared → bool moby_rnn.ipynb: switch Build model cell to make_unrolled(T=100); update predict_next() to use rnn.next_entity() for position-correct Gibbs sampling README_moby_rnn.md: redraw temporal-unrolling ASCII art showing per-position weights W_t; update parameter count and checkpoint file listing Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
GRBM
Paper
https://medium.com/@rtdcunha/gaussian-bernoulli-restricted-boltzmann-machines-4a68b8765485 https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0171015&type=printable
Code
https://github.com/DSL-Lab/GRBM/tree/main
CRBM
Paper
https://www.ee.nthu.edu.tw/hchen/pubs/iee2003.pdf
Regularization
https://benihime91.github.io/blog/machinelearning/deeplearning/python3.x/tensorflow2.x/2020/10/08/adamW.html https://medium.com/analytics-vidhya/l1-vs-l2-regularization-which-is-better-d01068e6658c https://jamesmccaffreyblog.com/2019/05/09/the-difference-between-neural-network-l2-regularization-and-weight-decay