./get_data.sh
A. MNIST classifier
python3 classify.py
B. Autoencoder
python3 ae.py
This is good, the err between numerical and analytical gradient is less than 1e-8.
iter 1000, loss = 422.25, gradcheck err 0.000000004 OK
iter 2000, loss = 335.09, gradcheck err 0.000000006 OK
iter 3000, loss = 395.82, gradcheck err 0.000000005 OK
iter 4000, loss = 307.89, gradcheck err 0.000000003 OK
iter 5000, loss = 291.37, gradcheck err 0.000000005 OK
iter 6000, loss = 251.52, gradcheck err 0.000000002 OK
iter 7000, loss = 239.43, gradcheck err 0.000000003 OK
do_gradcheck is set, all computation will be performed in float64
The error between the analytical solution (derivatives from backward) should be very close to the numerical one (less that 1e-6 err). Only a sample of parameters is going to be checked.