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2022-05-23 20:26:52.184100
Namespace(data='mri', algorithm='ALS-RS', rank='8,4,4,2', seed=0, alpha=1.0, max_num_samples=16384, max_num_steps=5, rre_gap_tol=0.0, verbose=False)
Loading MRI tensor...
Finished.
AlgorithmConfig(input_shape=(256, 256, 14, 20), rank=(8, 4, 4, 2), l2_regularization_strength=0.0, algorithm='ALS-RS', random_seed=0, epsilon=0.1, delta=0.01, downsampling_ratio=1.0, max_num_samples=16384, max_num_steps=5, rre_gap_tol=0.0, verbose=False)
step: 0
loss: 307461856972.789 rmse: 129.4424254296692 rre: 0.8607626623409795 time: 0.2896632990000001
loss: 219441816835.0495 rmse: 109.35552103407917 rre: 0.7271893207696636 time: 0.2912390010000001
loss: 193726819372.24225 rmse: 102.74859527281144 rre: 0.6832547684829563 time: 0.20317207700000006
loss: 138546109586.29956 rmse: 86.89167849371329 rre: 0.5778098816308329 time: 0.24800210799999967
loss: 110174879256.14201 rmse: 77.48583378721277 rre: 0.5152631555148875 time: 0.45058533300000025
rre_diff: 0.4585528827427151

step: 1
loss: 100864680124.28972 rmse: 74.13965773826239 rre: 0.49301184652561725 time: 0.2905515679999997
loss: 92816622700.2063 rmse: 71.12035240341586 rre: 0.47293415337504435 time: 0.28741750300000035
loss: 91088414368.16635 rmse: 70.45512505137691 rre: 0.46851054291887134 time: 0.20167593800000017
loss: 90436525421.5035 rmse: 70.20256062058006 rre: 0.46683104694880034 time: 0.25311048500000055
loss: 89178056773.66258 rmse: 69.71239788570524 rre: 0.46357157634439317 time: 0.4872863769999993
rre_diff: 0.051691579170494384

step: 2
loss: 88331851030.3953 rmse: 69.38086094606903 rre: 0.461366931168143 time: 0.2930748239999996
loss: 87563450503.01787 rmse: 69.07842905014894 rre: 0.45935582790702206 time: 0.2898451129999984
loss: 87215152778.38445 rmse: 68.9409068721456 rre: 0.4584413367293718 time: 0.20105527199999962
loss: 86460437050.18057 rmse: 68.64196891948689 rre: 0.45645346739556447 time: 0.25455623300000063
loss: 87233550360.15764 rmse: 68.94817785115904 rre: 0.4584896870846178 time: 0.49297705600000086
Warning: The loss function increased!
rre_diff: 0.005081889259775374

step: 3
loss: 86748117633.09367 rmse: 68.75607060295302 rre: 0.45721221761607284 time: 0.3000656199999998
loss: 86301123055.73724 rmse: 68.5786991233 rre: 0.45603273765389885 time: 0.28175139699999896
loss: 86162523485.72173 rmse: 68.52360831341608 rre: 0.4556663963092524 time: 0.1966582250000002
loss: 85699590799.10767 rmse: 68.33927909299699 rre: 0.45444064895487063 time: 0.2467636080000002
loss: 86563309241.67294 rmse: 68.68279250788272 rre: 0.45672493496515987 time: 0.8442556299999993
Warning: The loss function increased!
rre_diff: 0.001764752119457924

step: 4
loss: 86049617342.75917 rmse: 68.47869741600574 rre: 0.45536774906516125 time: 0.2864891259999993
loss: 85650920573.82428 rmse: 68.31987082950648 rre: 0.45431158842082975 time: 0.2805751739999991
loss: 85565333060.52455 rmse: 68.28572765104364 rre: 0.4540845440565349 time: 0.19627794699999868
loss: 85138989826.65317 rmse: 68.11539281561721 rre: 0.45295185617661926 time: 0.23127086899999938
loss: 86051181853.88866 rmse: 68.47931993589938 rre: 0.4553718886807412 time: 1.150048396999999
Warning: The loss function increased!
rre_diff: 0.0013530462844186797

