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2022-05-23 20:23:42.756819
Namespace(data='mri', algorithm='ALS-RS', rank='4,2,2,1', 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=(4, 2, 2, 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: 335094352545.803 rmse: 135.13398168288148 rre: 0.8986102157773164 time: 0.241879379
loss: 259622514476.5659 rmse: 118.94664446527773 rre: 0.7909681082272968 time: 0.2336558019999999
loss: 223333550784.95505 rmse: 110.32095290571118 rre: 0.7336092229414312 time: 0.180003052
loss: 161556623328.7237 rmse: 93.83036435547552 rre: 0.6239505630626969 time: 0.20260114399999996
loss: 154380742779.88525 rmse: 91.72285876078662 rre: 0.6099361306186094 time: 0.22669358199999978
rre_diff: 0.38813562628861475

step: 1
loss: 149626712253.9744 rmse: 90.29954991975279 rre: 0.6004714508331754 time: 0.23685973100000002
loss: 139231666028.13596 rmse: 87.10639271980178 rre: 0.5792376823559579 time: 0.22823226200000057
loss: 138534465552.53644 rmse: 86.88802703464151 rre: 0.5777856002592376 time: 0.1652000779999998
loss: 138534240867.85522 rmse: 86.88795657413735 rre: 0.5777851317129249 time: 0.1936900210000001
loss: 138515822668.25937 rmse: 86.88218048256964 rre: 0.5777467220188911 time: 0.2260605179999997
rre_diff: 0.032189408599718305

step: 2
loss: 136616996322.8837 rmse: 86.28461891660025 rre: 0.5737730736363835 time: 0.2371158840000005
loss: 135698938127.16301 rmse: 85.99421643525177 rre: 0.5718419632437421 time: 0.22802662600000012
loss: 135537863426.02869 rmse: 85.94316369388105 rre: 0.5715024741354672 time: 0.16767632699999968
loss: 135537854842.69518 rmse: 85.94316097257929 rre: 0.5715024560394301 time: 0.19846960300000127
loss: 136122006659.45251 rmse: 86.1281641836498 rre: 0.5727326852782139 time: 0.22768323300000048
Warning: The loss function increased!
rre_diff: 0.005014036740677241

step: 3
loss: 135258727303.88123 rmse: 85.85461941129095 rre: 0.5709136748128005 time: 0.2367584609999991
loss: 134989630548.4517 rmse: 85.76917316186574 rre: 0.5703454767054321 time: 0.22879276099999935
loss: 134926608476.52292 rmse: 85.74914946812234 rre: 0.5702123237003084 time: 0.16708784799999954
loss: 134926606212.68965 rmse: 85.74914874876171 rre: 0.570212318916724 time: 0.19302977200000093
loss: 135338622086.71812 rmse: 85.87997202139232 rre: 0.5710822638986098 time: 0.22649215099999864
Warning: The loss function increased!
rre_diff: 0.001650421379604028

step: 4
loss: 134824167943.84508 rmse: 85.71659155909012 rre: 0.5699958210168504 time: 0.23693746799999893
loss: 134712468745.6773 rmse: 85.68107693815382 rre: 0.5697596568723076 time: 0.22760482300000007
loss: 134685681552.1689 rmse: 85.6725577946886 rre: 0.5697030065075941 time: 0.16649541199999973
loss: 134685680930.92741 rmse: 85.67255759710507 rre: 0.5697030051937085 time: 0.19764965699999948
loss: 135110782752.38689 rmse: 85.80765313203909 rre: 0.5706013597473582 time: 0.2275471549999999
Warning: The loss function increased!
rre_diff: 0.00048090415125168207

