###################################
2022-05-23 20:23:02.453822
Namespace(data='mri', algorithm='ALS-RS', rank='1,1,1,1', seed=0, alpha=1.0, max_num_samples=4096, max_num_steps=5, rre_gap_tol=0.0, verbose=False)
Loading MRI tensor...
Finished.
AlgorithmConfig(input_shape=(256, 256, 14, 20), rank=(1, 1, 1, 1), l2_regularization_strength=0.0, algorithm='ALS-RS', random_seed=0, epsilon=0.1, delta=0.01, downsampling_ratio=1.0, max_num_samples=4096, max_num_steps=5, rre_gap_tol=0.0, verbose=False)
step: 0
loss: 356642197665.2315 rmse: 139.41110922325325 rre: 0.9270521402591986 time: 0.254476092
loss: 278933539610.5697 rmse: 123.29100365995559 rre: 0.819857107905445 time: 0.2467587830000002
loss: 249464626517.018 rmse: 116.5964976590286 rre: 0.7753401669621026 time: 0.17709389500000006
loss: 177430534814.49054 rmse: 98.33207411588394 rre: 0.6538859081830717 time: 0.172218784
loss: 177434286842.9541 rmse: 98.33311379823314 rre: 0.6538928218340166 time: 0.23912730400000015
Warning: The loss function increased!
rre_diff: 0.34597872938402385

step: 1
loss: 174557126840.2491 rmse: 97.5326022014364 rre: 0.6485696019468246 time: 0.2505360369999998
loss: 174077583700.7212 rmse: 97.39853933707838 rre: 0.6476781144174243 time: 0.2431284680000001
loss: 174051139508.43002 rmse: 97.39114113142818 rre: 0.6476289180340062 time: 0.17090207800000012
loss: 174051101862.37537 rmse: 97.39113059891335 rre: 0.6476288479951802 time: 0.17174766800000008
loss: 174052732046.3352 rmse: 97.3915866864804 rre: 0.6476318808736755 time: 0.23821235400000074
Warning: The loss function increased!
rre_diff: 0.0062609409603411326

step: 2
loss: 174032697710.1705 rmse: 97.38598139687909 rre: 0.6475946069738363 time: 0.24955659399999952
loss: 174026783248.6632 rmse: 97.38432656264486 rre: 0.6475836027028915 time: 0.24060749299999973
loss: 174026120920.04828 rmse: 97.38414124494177 rre: 0.6475823703823523 time: 0.16719600299999993
loss: 174026120102.45013 rmse: 97.38414101617987 rre: 0.6475823688611377 time: 0.1625096769999992
loss: 174031972959.90436 rmse: 97.38577861719934 rre: 0.6475932585351275 time: 0.23581248600000038
Warning: The loss function increased!
rre_diff: 3.8622338548011115e-05

step: 3
loss: 174025921703.50296 rmse: 97.38408550463785 rre: 0.6475819997220202 time: 0.247392112
loss: 174025794556.6492 rmse: 97.38404992924133 rre: 0.6475817631537318 time: 0.2407987380000005
loss: 174025778706.952 rmse: 97.38404549453168 rre: 0.6475817336639222 time: 0.167688815
loss: 174025778692.2622 rmse: 97.38404549042151 rre: 0.6475817336365906 time: 0.1648866050000013
loss: 174037812901.97687 rmse: 97.38741257711217 rre: 0.647604123996587 time: 0.23598251700000006
Warning: The loss function increased!
rre_diff: -1.0865461459452774e-05

step: 4
loss: 174025774694.88712 rmse: 97.38404437196492 rre: 0.6475817261991088 time: 0.2477246019999999
loss: 174025771706.80496 rmse: 97.38404353590622 rre: 0.6475817206395087 time: 0.2414163459999994
loss: 174025771327.00604 rmse: 97.38404342963933 rre: 0.6475817199328581 time: 0.16883916799999987
loss: 174025771326.6871 rmse: 97.38404342955008 rre: 0.6475817199322648 time: 0.16366176400000043
loss: 174033213481.14026 rmse: 97.38612570546452 rre: 0.6475955665930785 time: 0.2358457739999995
Warning: The loss function increased!
rre_diff: 8.557403508469008e-06

