Feedback Alignment in PyTorch

This version is forked and fixed some conflictions that main implementation had with new pytorch version and upgraded to Python3

This is a simple implementation of Random synaptic feedback weights support error backpropagation for deep learning in PyTorch.

Base codes are adapted from official PyTorch tutorial.

It implements simple MLP with one hidden layer, without non-linear activation function.

Run train_fa_vs_bp_linear_model.py to compare performance between feedback alignment vs backpropagation.

Test Results

Loss Comparison on MNIST:

epoch	step	feed alignment loss	back propagation loss
0	1000	0.46798	0.35344
0	1875	0.42992	0.39659
1	1000	0.43433	0.37663
1	1875	0.29304	0.21735
2	1000	0.41269	0.38590
2	1875	0.23184	0.24429
3	1000	0.35857	0.25014
3	1875	0.08632	0.06498
4	1000	0.18751	0.13469
4	1875	0.45113	0.39115
5	1000	0.26102	0.16991
5	1875	0.16346	0.13278
6	1000	0.29775	0.14079
6	1875	0.50019	0.42758
7	1000	0.26730	0.20934
7	1875	0.18355	0.11935
8	1000	0.46082	0.27962
8	1875	0.16061	0.15758
9	1000	0.17815	0.10925
9	1875	0.23911	0.17510
10	1000	0.55483	0.36205
10	1875	0.09864	0.07822
11	1000	0.22050	0.06755
11	1875	0.30027	0.28331
12	1000	0.30410	0.22639
12	1875	0.23802	0.12401
13	1000	0.38504	0.31829
13	1875	0.31254	0.17021
14	1000	0.30799	0.16914
14	1875	0.08483	0.05910
15	1000	0.11392	0.07960
15	1875	0.33861	0.30033
16	1000	0.53456	0.25107
16	1875	0.29415	0.14761
17	1000	0.14059	0.07061
17	1875	0.10071	0.06098
18	1000	0.34540	0.13852
18	1875	0.32415	0.16791
19	1000	0.34725	0.24961
19	1875	0.34411	0.19213
20	1000	0.14642	0.09837
20	1875	0.06806	0.04413
21	1000	0.16839	0.04480
21	1875	0.15662	0.08321
22	1000	0.24548	0.09513
22	1875	0.24273	0.07480
23	1000	0.33001	0.14574
23	1875	0.27394	0.17840
24	1000	0.27252	0.10221
24	1875	0.39897	0.24693
25	1000	0.25453	0.12092
25	1875	0.51975	0.14524
26	1000	0.24217	0.09227