###################################
2022-05-17 20:45:32.812545
Namespace(data='mri', algorithm='ALS-DJSSW19', rank='4,2,2,1', seed=0, alpha=1.0, max_num_samples=1024, max_num_steps=5, rre_gap_tol=0.0, verbose=False)
Loading MRI tensor...
Finished.
AlgorithmConfig(input_shape=(256, 256, 14, 20), rank=(4, 2, 2, 1), l2_regularization_strength=0.0, algorithm='ALS-DJSSW19', random_seed=0, epsilon=0.1, delta=0.01, downsampling_ratio=1.0, max_num_samples=1024, max_num_steps=5, rre_gap_tol=0.0, verbose=False)
step: 0
loss: 335094352545.803 rmse: 135.13398168288148 rre: 0.8986102157773164 time: 0.25563976200000016
loss: 259622514476.5659 rmse: 118.94664446527773 rre: 0.7909681082272968 time: 0.24902216099999963
loss: 223333550784.95505 rmse: 110.32095290571118 rre: 0.7336092229414312 time: 0.17786964100000002
loss: 161556623328.7237 rmse: 93.83036435547552 rre: 0.6239505630626969 time: 0.203348992
loss: 156125028367.87128 rmse: 92.23957307008001 rre: 0.6133721631485988 time: 0.23925147299999994
rre_diff: 0.3846995937586254

step: 1
loss: 149626712253.97397 rmse: 90.29954991975266 rre: 0.6004714508331745 time: 0.25400136699999987
loss: 139231666028.1357 rmse: 87.10639271980169 rre: 0.5792376823559573 time: 0.24733046199999986
loss: 138534465552.53622 rmse: 86.88802703464144 rre: 0.5777856002592372 time: 0.17642919599999995
loss: 138534240867.85532 rmse: 86.88795657413738 rre: 0.577785131712925 time: 0.1941438480000004
loss: 141596521605.98123 rmse: 87.8430307661137 rre: 0.584136157672844 time: 0.2394118900000004
Warning: The loss function increased!
rre_diff: 0.029236005475754778

step: 2
loss: 136616996322.88351 rmse: 86.2846189166002 rre: 0.5737730736363832 time: 0.2544904790000002
loss: 135698938127.1631 rmse: 85.9942164352518 rre: 0.5718419632437423 time: 0.24722789499999998
loss: 135537863426.02869 rmse: 85.94316369388105 rre: 0.5715024741354672 time: 0.17614022700000032
loss: 135537854842.69536 rmse: 85.94316097257935 rre: 0.5715024560394304 time: 0.19639405400000065
loss: 137777149824.20758 rmse: 86.6502095208093 rre: 0.5762041679299373 time: 0.23934266600000065
Warning: The loss function increased!
rre_diff: 0.007931989742906653

step: 3
loss: 135258727303.88153 rmse: 85.85461941129105 rre: 0.5709136748128011 time: 0.25439562799999926
loss: 134989630548.45175 rmse: 85.76917316186575 rre: 0.5703454767054322 time: 0.24780224400000073
loss: 134926608476.52304 rmse: 85.74914946812237 rre: 0.5702123237003086 time: 0.17667389
loss: 134926606212.68904 rmse: 85.74914874876151 rre: 0.5702123189167227 time: 0.197954661999999
loss: 136660882133.67342 rmse: 86.2984765130569 rre: 0.5738652234982243 time: 0.2393086929999999
Warning: The loss function increased!
rre_diff: 0.002338944431713008

step: 4
loss: 134824167943.84538 rmse: 85.71659155909022 rre: 0.5699958210168511 time: 0.25475148099999956
loss: 134712468745.6774 rmse: 85.68107693815385 rre: 0.5697596568723078 time: 0.24732710900000043
loss: 134685681552.16924 rmse: 85.67255779468871 rre: 0.5697030065075949 time: 0.17666266600000036
loss: 134685680930.92734 rmse: 85.67255759710504 rre: 0.5697030051937084 time: 0.19525076399999897
loss: 136148595659.79773 rmse: 86.13657557143225 rre: 0.5727886190921668 time: 0.23883743100000032
Warning: The loss function increased!
rre_diff: 0.001076604406057502

