mtkwt / pytorch-hessianfree

PyTorch implementation of Hessian Free optimisation

pytorch-hessianfree

PyTorch implementation of Hessian Free optimisation

Implemented some parts of Training Deep and Recurrent Networks with Hessian-Free Optimization by Martens and Sutskever (2012):

• Preconditioner for CG, includes empirical Fisher diagonal (Section 20.11)
• Gauss-Newton matrix and Hessian matrix (Section 20.5 & 20.6)
• Martens' CG stopping criteria (Section 20.4)
• CG backtracking (Section 20.8.7)
• Tikhonov damping with Levenberg-Marquardt like heuristic (Section 20.8.1 & 20.8.5)
• Line-searching (Section 20.8.5)
• Different batches for calculating curvature and gradient, via callable vector b (A x = b) (Section 20.12)

Still yet to do:

• Scale-Sensitive damping (Section 20.8.3)

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Not fully tested, use with caution!