Читать книгу Advanced Analytics and Deep Learning Models - Группа авторов - Страница 67

Here, they trained their stacked autoencoder with the three-hidden layers and five-hidden layers, and also, they applied sigmoid and hyperbolic tangents. In sigmoid transfer function, the data are transformed in between 0 and 1. In hyperbolic tangents, the data are transformed in between −1 and 1. They used mini batch GD Optimizer and Adam Optimizer for regularization. Dropout regularization is added to each hidden layer with probability p = 0.5. Research was done for dissimilar parameters. Finest parameter values are shown in the research work. In this proposed approach, 90% of data are utilized for training purposes along with the rest samples for testing purposes [4].