Since the RProp algorithm uses if/else conditional statements while determining update values some special helper matrix-functions and helper matrices must be introduced. These functions will allow us to express the conditional statements more elegantly, only using matrix operations. Some matrices needed to make the above functions work: D: decision matrix, M: meta-gradient matrix, U: update […]
In a multi-layered neural network weights and neural connections can be treated as matrices, the neurons of one layer can form the columns, and the neurons of the other layer can form the rows of the matrix. The figure below shows a network and its parameter matrices. The meanings of vectors and matrices above: ninl […]
The RProp algorithm is a supervised learning method for training multi layered neural networks, first published in 1994 by Martin Riedmiller. The idea behind it is that the sizes of the partial derivatives might have dangerous effects on the weight updates. It implements an internal adaptive algorithm which focuses only on the signs of the […]
Preconditioning weights The outcome and speed of a learning process is influenced by the initial state of the network. However, it is impossible to tell which condition will be the most ideal. The commonly accepted way is initializing weights by uniformly distributed random numbers on the (0,1) interval. Preconditioning data Very often the training set […]
