Constrained Feedforward Neural Network Training via Reachability Analysis

• URL: http://arxiv.org/abs/2107.07696v1
• Date: Fri, 16 Jul 2021 04:03:01 GMT
• Title: Constrained Feedforward Neural Network Training via Reachability Analysis
• Authors: Long Kiu Chung, Adam Dai, Derek Knowles, Shreyas Kousik, Grace X. Gao
• Abstract summary: It remains an open challenge to train a neural network to obey safety constraints. This work proposes a constrained method to simultaneously train and verify a feedforward neural network with rectified linear unit (ReLU) nonlinearities.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Neural networks have recently become popular for a wide variety of uses, but have seen limited application in safety-critical domains such as robotics near and around humans. This is because it remains an open challenge to train a neural network to obey safety constraints. Most existing safety-related methods only seek to verify that already-trained networks obey constraints, requiring alternating training and verification. Instead, this work proposes a constrained method to simultaneously train and verify a feedforward neural network with rectified linear unit (ReLU) nonlinearities. Constraints are enforced by computing the network's output-space reachable set and ensuring that it does not intersect with unsafe sets; training is achieved by formulating a novel collision-check loss function between the reachable set and unsafe portions of the output space. The reachable and unsafe sets are represented by constrained zonotopes, a convex polytope representation that enables differentiable collision checking. The proposed method is demonstrated successfully on a network with one nonlinearity layer and approximately 50 parameters.

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