Virtual Computational Chemistry Laboratory

Input data Output results Example List of key words



TRAINING

Keyword of Integer Type

This keyword indicatea method that will be used to optimize neural network weights. All these methods, with an exception of stiff and Levenberg-Marquardt algorithms, are heuristic algorithms that have a number of adjustable parameters. These parameters were selected by the  authors of the algorithms  to provide the fast convergence of the algorithms.

momentum {0}  -- The simplest algorithm that started new interest in artificial neural networks after article of Rumelhart et al., 1986. The momentum rate is 0.9 and eps is 0.2.
SuperSAB {1} -- programmed according to Tollenaere, T. SuperSAB: Fast Adaptive Back Propagation with Good Scaling Properties. Neural Networks. 1990, 3, 561-573. Momentum is 0.9, eps is 0.2, multiplication(+) is 1.05, multiplication(-) is 0.5, max eps is 10.
RPROP {2} --  Initial eps is 0.0001, minimal eps is 0.000001, multiplication(+) is 1.2, multiplication(-) is 0.5.
QuickProp {3} -- Falhman implementation updated from his original code by V.V. Kovalishyn. Eps is 0.55, decay is 0.0001, minimal derive is 0.1, maximal momentum is 1.75.
stiff {4}--  Second order optimisation algorithm that converts the weights optimisation to the problem of solution of the second order differential equations.
QuickProp1 {5} -- Original implementation of I. V. Tetko inspired by the algorithm of Falhman. The same parameters as in QuickProp are used.
Marquardt {6} -- Second -order Levenberg-Marquardt optimisation algorithm (requires O(n2) time, where n is the number of weights), see e.g. Shepherd, A. J. Second-Order Methods for Neural Networks; Springer-Verlag: London, 1997; 145.
 

The default value is {1}, the SuperSAB algorithm.

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