ARCHITECTURE FOR AN EXPLAINABLE NEURAL NETWORK

摘要

An architecture for an explainable neural network may implement a number of layers to produce an output. The input layer may be processed by both a conditional network and a prediction network. The conditional network may include a conditional layer, an aggregation layer, and a switch output layer. The prediction network may include a feature generation and transformation layer, a fit layer, and a value output layer. The results of the switch output layer and value output layer may be combined to produce the final output layer. A number of different possible activation functions may be applied to the final output layer depending on the application. The explainable neural network may be implementable using both general purpose computing hardware and also application specific circuitry including optimized hardware only implementations. Various embodiments of XNNs are described that extend the functionality to different application areas and industries.