On June 18th, 2024, during the transition from version ~1.0.6 to 1.1.9, I encountered an unexpected issue. The code consistently returned the same output value for every sample, regardless of the X and Y inputs provided in the “How to Use GRSBA” example.
While a deterministic model without noise should produce identical results for the same input data, attempts to fix this in versions 1.1.10 to 2.6.0 inadvertently broke the code. This necessitated a “rollback” to version 2.6.1, which is nearly identical to 1.1.9, with a minor change to the result variable calculation. The exponentiation of the result was reverted to the original formula.
What is GRSBA?
GRSBA is a module designed to leverage GPU tensors and relativistic equations to accelerate tensor calculations. It’s applied to the final layer of a neural network to evaluate the predicted output.
Currently, in version 2.6.1, each code run yields slightly different results. This was not entirely expected or intended. While the deep neural network within GRSBA remains functional, the predictions are inconsistent for the same X and Y sample data.
The GRSBA Formula: Its Importance for Neural Networks
The fundamental concept behind the GRSBA formula is that for a neural network to operate optimally, it must perceive itself as part of a greater whole. This is the core principle the GRSBA formula aims to embody. Without this integration, a neural network cannot fulfill its intended purpose, highlighting the importance of GRSBA.
I apologize for any inconvenience caused to users who experienced issues running samples between updates 1.1.9 and 2.6.1. This type of oversight during updates and package management will not occur again.
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