eegmp_preproc/data/outputs/history/log_sub-022_ica1_16-Nov-2021.txt

Input data size [72,1082000] = 72 channels, 1082000 frames
Finding 72 ICA components using extended ICA.
Kurtosis will be calculated initially every 1 blocks using 6000 data points.
Decomposing 208 frames per ICA weight ((5184)^2 = 1082000 weights, Initial learning rate will be 0.001, block size 70.
Learning rate will be multiplied by 0.98 whenever angledelta >= 60 deg.
More than 32 channels: default stopping weight change 1E-7
Training will end when wchange < 1e-07 or after 512 steps.
Online bias adjustment will be used.
Removing mean of each channel ...
Final training data range: -3.70788 to 3.57007
Computing the sphering matrix...
Starting weights are the identity matrix ...
Sphering the data ...
Beginning ICA training ... first training step may be slow ...
Lowering learning rate to 0.0009 and starting again.
Lowering learning rate to 0.00081 and starting again.
Lowering learning rate to 0.000729 and starting again.
Lowering learning rate to 0.0006561 and starting again.
Lowering learning rate to 0.00059049 and starting again.
Lowering learning rate to 0.000531441 and starting again.
Lowering learning rate to 0.000478297 and starting again.
Lowering learning rate to 0.000430467 and starting again.
Lowering learning rate to 0.00038742 and starting again.
Lowering learning rate to 0.000348678 and starting again.
Lowering learning rate to 0.000313811 and starting again.
Lowering learning rate to 0.00028243 and starting again.
Lowering learning rate to 0.000254187 and starting again.
Lowering learning rate to 0.000228768 and starting again.
Lowering learning rate to 0.000205891 and starting again.
Lowering learning rate to 0.000185302 and starting again.
Lowering learning rate to 0.000166772 and starting again.
Lowering learning rate to 0.000150095 and starting again.
Lowering learning rate to 0.000135085 and starting again.
Lowering learning rate to 0.000121577 and starting again.
Lowering learning rate to 0.000109419 and starting again.
Lowering learning rate to 9.84771e-05 and starting again.
Lowering learning rate to 8.86294e-05 and starting again.
Lowering learning rate to 7.97664e-05 and starting again.
Lowering learning rate to 7.17898e-05 and starting again.
Lowering learning rate to 6.46108e-05 and starting again.
Lowering learning rate to 5.81497e-05 and starting again.
Lowering learning rate to 5.23348e-05 and starting again.
Lowering learning rate to 4.71013e-05 and starting again.
Lowering learning rate to 4.23912e-05 and starting again.
Lowering learning rate to 3.8152e-05 and starting again.
Lowering learning rate to 3.43368e-05 and starting again.
Lowering learning rate to 3.09032e-05 and starting again.
Lowering learning rate to 2.78128e-05 and starting again.
Lowering learning rate to 2.50316e-05 and starting again.
Lowering learning rate to 2.25284e-05 and starting again.
Lowering learning rate to 2.02756e-05 and starting again.
Lowering learning rate to 1.8248e-05 and starting again.
Lowering learning rate to 1.64232e-05 and starting again.
step 1 - lrate 0.000016, wchange 2645991517999849.50000000, angledelta  0.0 deg
Lowering learning rate to 1.18247e-05 and starting again.
step 1 - lrate 0.000012, wchange 128325734859.78060913, angledelta  0.0 deg
Lowering learning rate to 8.51379e-06 and starting again.
step 1 - lrate 0.000009, wchange 99730290.31537521, angledelta  0.0 deg
step 2 - lrate 0.000009, wchange 9609913766693288.00000000, angledelta  0.0 deg