ΣPI is a versatile SDK designed to calculate Predictive Integrity (PI), a metric from the Integrated Predictive Workspace Theory (IPWT), which assesses a model’s cognitive state during training. Instead of solely focusing on loss metrics, ΣPI highlights early indicators of training instability, quantifies surprise from out-of-distribution (OOD) data, and offers insights into model overfitting and cognitive load.
PI scores, ranging from 0 to 1, are derived from three components: prediction error (ε), model uncertainty (τ), and surprise (S), providing a comprehensive view of the model’s learning health. A high PI score indicates stability and confidence, while a sudden drop may indicate potential issues.
Integrating ΣPI into a PyTorch training loop involves simple steps: initializing the monitor, calculating loss, and retrieving metrics like PI score and surprise, facilitating real-time monitoring and adjustment in model training. This tool aims to enhance understanding and performance in machine learning workflows.
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