Hi all,
I was running Caffe on my own dataset, which is a 8-class classification problem. My configuration is like the 8-layer model from "bvlc_reference_caffenet", only changing the training batch size to 128 and validation batch size to 50 because of power issue.
Now my problem is the accuracy of the network remains the same (12.5%) since the beginning, which is random guessing for a 8-class problem. I think the network is not learning anything.
I0507 19:42:05.711138 11638 solver.cpp:189] Iteration 7980, loss = 2.07975
I0507 19:42:05.711185 11638 solver.cpp:204] Train net output #0: loss = 2.07975 (* 1 = 2.07975 loss)
I0507 19:42:05.711200 11638 solver.cpp:464] Iteration 7980, lr = 0.01
I0507 19:42:10.959509 11638 solver.cpp:266] Iteration 8000, Testing net (#0)
I0507 19:43:35.754755 11638 solver.cpp:315] Test net output #0: accuracy = 0.12484
I0507 19:43:35.754869 11638 solver.cpp:315] Test net output #1: loss = 2.35999 (* 1 = 2.35999 loss)
I0507 19:43:35.850280 11638 solver.cpp:189] Iteration 8000, loss = 2.07875
I0507 19:43:35.850329 11638 solver.cpp:204] Train net output #0: loss = 2.07875 (* 1 = 2.07875 loss)
I0507 19:43:35.850345 11638 solver.cpp:464] Iteration 8000, lr = 0.01
However, when I use t-SNE to visualize my dataset as below, it seems like it should be separable.
But right now, the Caffe just doesn't work. So if any of you came across this kind of problem before, please share some ideas. Thank you so much.
Yi