The Cramming, Softening and Integrating Learning Algorithm with ReLU activation function for Binary Input/Output Problems

• Main Authors: Tsai, Yu-Han, 蔡羽涵
• Other Authors: Tsaih, Rua-Huan
• Format: Others
• Language: zh-TW
• Published: 2019
• Online Access: http://ndltd.ncl.edu.tw/handle/ymzgt7

碩士 === 國立政治大學 === 資訊管理學系 === 107 === Rare Artificial Neural Networks studies address simultaneously the challenges of (1) systematically adjusting the amount of used hidden layer nodes within the learning process, (2) adopting ReLU activation function instead of tanh function for fast learning, and (3) guaranteeing learning all training data. This study will address these challenges through deriving the CSI (Cramming, Softening and Integrating) learning algorithm for the single-hidden layer feed-forward neural networks with ReLU activation function and the binary input/output, and further making the technical justification. For the purpose of verifying the proposed learning algorithm, this study conducts an empirical experiment using SPECT heart diagnosis data set from UCI Machine Learning repository. The learning algorithm is implemented via the advanced TensorFlow and GPU.