Jesper Wohlert wohlert
wohlert
/ baselayer.py
Last active
May 24, 2019 15:45
A metaprogramming approach with minimal overhead to perform Jacobian computation for arbitrary networks
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| """ | |
| Registers jacobian for basic PyTorch layers | |
| """ | |
| import torch | |
| from torch import nn | |
| from .metric import register_jacobian | |
| @register_jacobian(nn.Linear) |
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| import numpy as np | |
| class HamiltonianMC(object): | |
| """ | |
| Hamiltonian/hybrid Monte Carlo | |
| Uses Hamiltonian dynamics to make proposals using | |
| kinetic energy of the proposal. Employs leapfrog | |
| integration for time-reversibility and to ensure |
wohlert
/ Backpropagation.ipynb
Last active
October 30, 2018 15:58
Tutorial describing backpropagation in a MLP
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