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2022-05-23 20:27:42.036473
Namespace(data='mri', algorithm='ALS-RS', rank='8,8,2,2', 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, 8, 2, 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: 304158644160.0954 rmse: 128.74521617707495 rre: 0.8561263795265952 time: 0.30717055400000004
loss: 215116088094.34222 rmse: 108.27232551339067 rre: 0.7199863171398354 time: 0.30327269899999987
loss: 182494172666.02924 rmse: 99.72533862432917 rre: 0.6631507999961976 time: 0.21685010900000012
loss: 153363439696.5898 rmse: 91.42015204986463 rre: 0.6079231999002858 time: 0.22787719299999987
loss: 121305826316.4088 rmse: 81.30586026617426 rre: 0.5406654619946166 time: 0.48851724399999963
rre_diff: 0.4357153142666935

step: 1
loss: 113872659887.98462 rmse: 78.775424404183 rre: 0.523838639550424 time: 0.3114572899999999
loss: 104640253243.23523 rmse: 75.51451023490944 rre: 0.5021543026516658 time: 0.292824403
loss: 97209050570.18222 rmse: 72.78374027644955 rre: 0.48399530407077285 time: 0.2111233979999998
loss: 95985203154.44934 rmse: 72.32412085424278 rre: 0.4809389395426122 time: 0.21494156600000025
loss: 94341495632.7375 rmse: 71.70218620149564 rre: 0.47680321568141487 time: 0.4838320060000001
rre_diff: 0.06386224631320175

step: 2
loss: 93430417191.62935 rmse: 71.35512365096461 rre: 0.4744953287270233 time: 0.3010603249999999
loss: 92527989032.0111 rmse: 71.00968412368562 rre: 0.47219823450778664 time: 0.2950516400000005
loss: 91938701664.908 rmse: 70.78320158324867 rre: 0.47069217717122824 time: 0.20840754199999978
loss: 91512223430.30043 rmse: 70.6188388959481 rre: 0.4695992027168271 time: 0.2165254000000001
loss: 92531995705.57887 rmse: 71.01122154791513 rre: 0.4722084580289277 time: 0.5011935039999997
Warning: The loss function increased!
rre_diff: 0.00459475765248718

step: 3
loss: 91813621510.49997 rmse: 70.73503586556896 rre: 0.4703718861132855 time: 0.30827013899999933
loss: 91286554721.61116 rmse: 70.53171228855216 rre: 0.46901983061147656 time: 0.2957264790000007
loss: 91215310245.77939 rmse: 70.50418372365377 rre: 0.4688367719216095 time: 0.20719671499999848
loss: 90752549339.48969 rmse: 70.32511254511833 rre: 0.4676459893487934 time: 0.21861044199999924
loss: 91882889088.82774 rmse: 70.7617133932989 rre: 0.4705492855997156 time: 0.5263413289999992
Warning: The loss function increased!
rre_diff: 0.0016591724292120968

step: 4
loss: 91285968498.06 rmse: 70.53148581808587 rre: 0.46901832463443033 time: 0.3040087570000001
loss: 91032544524.09874 rmse: 70.43351461575718 rre: 0.4683668384414818 time: 0.2936812539999991
loss: 91006998151.86348 rmse: 70.42363107880489 rre: 0.46830111517067513 time: 0.21105251900000077
loss: 90537616307.66447 rmse: 70.2417862371571 rre: 0.46709188836956905 time: 0.2116726540000009
loss: 92008158449.39775 rmse: 70.80993377164266 rre: 0.47086994013863115 time: 1.1596067869999978
Warning: The loss function increased!
rre_diff: -0.0003206545389155546

