[RE]-Random-synaptic-feedback-weights-support-error-backpropagation-for-deep-learning

Trying to replicate (partially) the result of Feedback Alignment Algorithm[1].

Instruction

re1.py is for the simple linear network.

re2.py is for the nolinear network on MNIST dataset.

check2.py is for the evaluation of the network on MNIST dataset.

Result in figs/e1.png seems great.

Performance of figs/e2.png matches the result in the paper also, whereas the change of angle is not so close to that of the paper.

Reference

[1] T. P. Lillicrap, D. Cownden, D. B. Tweed, and C. J. Akerman, “Random synaptic feedback weights support error backpropagation for deep learning,” Nature Communications, vol. 7, pp. 1–10, 1AD.