Very nice work! But I'm wondering what the experimental settings are that can replicate the results shown in your paper (which is ~90% MCC performance).

If I understand it the right way, I run the following command which is exactly the settings that you described in the paper (i.e. M=40, L=1000, DimOfHiddenLayers={50, 100, 200}, #layers={3, 4, 5, 6}, lr={1e-2, 1e-3}, n=d=5, NumberOfMixingLayers=3.)

nohup python main.py -x "1000_40_5_5_3_1_gauss_xtanh_u_f" -g 50 -d 4 -l 1e-3 -c > log2.txt 2>&1 &
nohup python main.py -x "1000_40_5_5_3_1_gauss_xtanh_u_f" -g 50 -d 5 -l 1e-3 -c > log3.txt 2>&1 &
nohup python main.py -x "1000_40_5_5_3_1_gauss_xtanh_u_f" -g 50 -d 6 -l 1e-3 -c > log4.txt 2>&1 &
nohup python main.py -x "1000_40_5_5_3_1_gauss_xtanh_u_f" -g 100 -d 3 -l 1e-3 -c > log5.txt 2>&1 &
nohup python main.py -x "1000_40_5_5_3_1_gauss_xtanh_u_f" -g 100 -d 4 -l 1e-3 -c > log6.txt 2>&1 &
nohup python main.py -x "1000_40_5_5_3_1_gauss_xtanh_u_f" -g 100 -d 5 -l 1e-3 -c > log7.txt 2>&1 &
nohup python main.py -x "1000_40_5_5_3_1_gauss_xtanh_u_f" -g 100 -d 6 -l 1e-3 -c > log8.txt 2>&1 &
nohup python main.py -x "1000_40_5_5_3_1_gauss_xtanh_u_f" -g 200 -d 3 -l 1e-3 -c > log9.txt 2>&1 &
nohup python main.py -x "1000_40_5_5_3_1_gauss_xtanh_u_f" -g 200 -d 4 -l 1e-3 -c > log10.txt 2>&1 &
nohup python main.py -x "1000_40_5_5_3_1_gauss_xtanh_u_f" -g 200 -d 5 -l 1e-3 -c > log11.txt 2>&1 &
nohup python main.py -x "1000_40_5_5_3_1_gauss_xtanh_u_f" -g 200 -d 6 -l 1e-3 -c > log12.txt 2>&1 &

They all gave me the poor results (~40% MCC), which is far from ~90%, however. Do you mind providing the exact experimental settings for reproducing the results you described in the paper?

Thanks a lot!