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Avoiding roundoff error in backpropagating derivatives

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Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 1524))

Abstract

One significant source of roundoff error in backpropagation networks is the calculation of derivatives of unit outputs with respect to their total inputs. The roundoff error can lead result in high relative error in derivatives, and in particular, derivatives being calculated to be zero when in fact they are small but non-zero. This roundoff error is easily avoided with a simple programming trick which has a small memory overhead (one or two extra floating point numbers per unit) and an insignificant computational overhead.

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© 1998 Springer-Verlag Berlin Heidelberg

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Plate, T. (1998). Avoiding roundoff error in backpropagating derivatives. In: Orr, G.B., Müller, KR. (eds) Neural Networks: Tricks of the Trade. Lecture Notes in Computer Science, vol 1524. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-49430-8_12

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  • DOI: https://doi.org/10.1007/3-540-49430-8_12

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-65311-0

  • Online ISBN: 978-3-540-49430-0

  • eBook Packages: Springer Book Archive

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