On Dissipativity of Cross-Entropy Loss in Training ResNets

Fuente: arXiv
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Main Authors: Püttschneider, Jens, Faulwasser, Timm
Format: Preprint
Published: 2024
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author Püttschneider, Jens
Faulwasser, Timm
author_facet Püttschneider, Jens
Faulwasser, Timm
contents The training of ResNets and neural ODEs can be formulated and analyzed from the perspective of optimal control. This paper proposes a dissipative formulation of the training of ResNets and neural ODEs for classification problems by including a variant of the cross-entropy as a regularization in the stage cost. Based on the dissipative formulation of the training, we prove that the trained ResNet exhibit the turnpike phenomenon. We then illustrate that the training exhibits the turnpike phenomenon by training on the two spirals and MNIST datasets. This can be used to find very shallow networks suitable for a given classification task.
format Preprint
id arxiv_https___arxiv_org_abs_2405_19013
institution arXiv
publishDate 2024
record_format arxiv
spellingShingle On Dissipativity of Cross-Entropy Loss in Training ResNets
Püttschneider, Jens
Faulwasser, Timm
Machine Learning
Systems and Control
Optimization and Control
The training of ResNets and neural ODEs can be formulated and analyzed from the perspective of optimal control. This paper proposes a dissipative formulation of the training of ResNets and neural ODEs for classification problems by including a variant of the cross-entropy as a regularization in the stage cost. Based on the dissipative formulation of the training, we prove that the trained ResNet exhibit the turnpike phenomenon. We then illustrate that the training exhibits the turnpike phenomenon by training on the two spirals and MNIST datasets. This can be used to find very shallow networks suitable for a given classification task.
title On Dissipativity of Cross-Entropy Loss in Training ResNets
topic Machine Learning
Systems and Control
Optimization and Control
url https://arxiv.org/abs/2405.19013