Deep learning as optimal control problems: Models and numerical methods

Martin Benning; Elena Celledoni; Matthias J. Ehrhardt; Brynjulf Owren; Carola-Bibiane Schönlieb

doi:10.3934/jcd.2019009

Article Contents

2019, Volume 6, Issue 2: 171-198. Doi: 10.3934/jcd.2019009

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Deep learning as optimal control problems: Models and numerical methods

1.
School of Mathematical Sciences, Queen Mary University of London, London E1 4NS, UK
2.
Department of Mathematical Sciences, NTNU, 7491 Trondheim, Norway
3.
Institute for Mathematical Innovation, University of Bath, Bath BA2 7JU, UK
4.
Department of Applied Mathematics and Theoretical Physics, University of Cambridge, Cambridge CB3 0WA, UK

^* Corresponding author: Carola-Bibiane Schönlieb
^* Corresponding author: Carola-Bibiane Schönlieb

Received: April 2019

Revised: September 2019

Published: November 2019

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Abstract

We consider recent work of [18] and [9], where deep learning neural networks have been interpreted as discretisations of an optimal control problem subject to an ordinary differential equation constraint. We review the first order conditions for optimality, and the conditions ensuring optimality after discretisation. This leads to a class of algorithms for solving the discrete optimal control problem which guarantee that the corresponding discrete necessary conditions for optimality are fulfilled. The differential equation setting lends itself to learning additional parameters such as the time discretisation. We explore this extension alongside natural constraints (e.g. time steps lie in a simplex). We compare these deep learning algorithms numerically in terms of induced flow and generalisation ability.

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Mathematics Subject Classification: Primary: 65L10, 65K10, 68Q32; Secondary: 49J15.

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Figure 1. The four data sets used in the numerical study

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Figure 2. Learned transformation and classifier for data set donut1d (top) and squares (bottom)

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Figure 3. Snap shots of transformation of features for data set spiral

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Figure 4. Learned transformation with fixed classifier for data set donut1d (top) and spiral (bottom)

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Figure 5. Robustness on random initialisation for transformed data donut2d and linear classifier for two different initialisations

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Figure 6. Function values over the course of the gradient descent iterations for data sets donut1d, donut2d, spiral, squares (left to right and top to bottom). The solid line represents training and the dashed line test data

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Figure 7. Classification accuracy over the course of the gradient descent iterations for data setsdonut1d, donut2d, spiral, squares (left to right and top to bottom). The solid line represents training and the dashed line test data

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Figure 8. Estimated time steps by ResNet/Euler, ODENet and ODENet+simplex for for data sets donut1d, donut2d, spiral, squares (left to right and top to bottom). ODENet+simplex consistently picks two to three time steps and set the rest to zero

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Figure 9. Learned prediction and transformation for different Runge-Kutta methods and data sets spiral (top), donut2d (centre) and squares (bottom). All results are for 15 layers

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Figure 10. Snap shots of the transition from initial to final state through the network with the data set spiral

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Figure 11. Snap shots of the transition from initial to final state through the network with the data set donut2d

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Figure 12. Snap shots of the transition from initial to final state through the network with the data set squares

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Figure 13. Function values over the course of the gradient descent iterations for data sets donut1d, donut2d, spiral, squares (left to right and top to bottom)

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Figure 14. Accuracy (left) and time steps (right) for MNIST100 dataset [28]

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Figure 15. Features of testing examples from MNIST100 dataset [28] and transformed features by four networks under comparison: Net, ResNet, ODENet, ODENet+Simplex (from top to bottom). All networks have 20 layers

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Table 1. Four explicit Runge–Kutta methods: ResNet/Euler, Improved Euler, Kutta(3) and Kutta(4)

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