出版社：Vilnius University, University of Latvia, Latvia University of Agriculture, Institute of Mathematics and Informatics of University of Latvia
摘要：In this article, the importance of correct representation of input data for recurrent neural
network is experimentally analysed on the basis of the task for recognizing handwritten digits and
task for incrementing an integer. In order to solve this task, the same information in a different
form is provided for the neural network and quality of classification is evaluated. It was received,
that a simple permutation of inputs has caused the decrease of quality from several percentage
points (for short sequences, e.g. incrementing 32-bit integer in binary) up to 15% for long ones
(784 steps). In addition, the phenomena that models examining the depiction of handwritten digits,
presented in a horizontal way converge on average faster than analogue models with vertical digit
representation.
关键词：recurrent neural networks; handwriting recognition; training models
