✨ NeuralNetworks

• 🛠️ Fixed:
• • (Changed) #51 Missing LamdaSELU in SELU-activation function & >= at fx > 0 in derivative 4246a8f
• • (v3.1.7) REDUCE_RAM_DELETE_OUTPUTS unproper NULL-ification of last-layer's outputs, resulting in potential undefined behavior. e92d3dc
• • Invalid (IDFLOAT instead of DFLOAT)-type when using single-bias in EEPROM\FRAM-FeedForward with int-quantization 7356089 b25819f
• • USE_PROGMEM compatibility of non-__AVR__ MCUs aa77d01 2f768af
• • A closing-parentheses related to Softmax somewhere...
• • Few compiler warnings 72ad515
• ✨ Added:
• • RNN support 443962f 475f283 172c88e 6852083 d9e791b 5bf44c9 9359793 8594613 8350fc4 5f38248 e473aa2
• • GRU support 0648099 b7cab3b d9e791b 5bf44c9 6b482b7 8594613 8350fc4 2fe31ef c92230b ab1a948 5b82cfa
• • LSTM support 8a5f26d d9e791b 5bf44c9 6b482b7 8594613 8350fc4 2fe31ef 0439db9 c92230b 5b82cfa
• • DENSE+RNN support a5b5467
• • REDUCED_SKETCH_SIZE_DOT_PROD in favor of unimplemented optimization-bit 0B00001 0bd8a1f
• • #24 Support for saving into EEPROM\FRAM when you USE_PROGMEM bffe09c
• • New examples 399d0b5
• ⚙️ Improved:
• • Replaced unimplemented 0B001 optimization-bit in favor of DISABLE_SIMD_SUPPORT 7ee208f
• • Safety, by adding a macro-condition that checks if MCU supports 64bit\8byte double-precision 9e7514e
• • (Changed) Activation functions & their variable are now static 622e1f7 f8f222b
• • #49 the use of multiple activation-functions 72010df 21b5e44
• • AlphaSELU & LamdaSELU precision 8dbf5ab
• • Source-code mostly Visually 2d5002d 7bf22ac 6d286a8 642c2e9
• • Source-code Macros 80ae017 d14806b acb92cc f06c20d cc23ab2 67c1138 d7d00d4 c3bb87e d14f301
• • Source-code 06b9ebd c3bda35 96b5f34 7300d7e f815118 a114a5c df0ef21 f1e92de
• • examples 54a759a 630f4e8 ef7fa9e 7fe5db9 0bd8a1f 399d0b5
• ⚠️ Changed:
• • Library is now under the GPLv3 LICENSE a5e9b36
• • FS-support now gets enabled via _OPTIMIZE_3 0B00000010 fd20e89

Note

Special thanks to Vibhutesh Kumar Singh for using my library for his latest paper "Memory-Efficient Neural Network Deployment Methodology for Low-RAM Microcontrollers Using Quantization and Layer-Wise Model Partitioning", our excellent collaboration led to the actual support for int-quantization [...] Moreover, I'd like to thanks kritonix.ai - a startup company - for giving me the motivation to continue developing this library, with the promise of bringing me on board once the company stabilizes. Last but not least I'd like to thanks Jiajun Guan for also using the library for his Master's Thesis "Neural Network for Monitoring Infant Feeding Process in the SmartBottle Device"