[run_0] training getter supervised:
0 train: [0] = 0.4205
0 train: [0] = 88.1%
0 val: [0] = 0.209
0 val: [0] = 94.2%
1 train: [0] = 0.1864
1 train: [0] = 94.6%
1 val: [0] = 0.1401
1 val: [0] = 96.1%
2 train: [0] = 0.1324
2 train: [0] = 96.0%
2 val: [0] = 0.126
2 val: [0] = 96.3%
3 train: [0] = 0.104
3 train: [0] = 96.9%
3 val: [0] = 0.1146
3 val: [0] = 96.7%
4 train: [0] = 0.08553
4 train: [0] = 97.4%
4 val: [0] = 0.09689
4 val: [0] = 97.2%
5 train: [0] = 0.07095
5 train: [0] = 97.8%
5 val: [0] = 0.09782
5 val: [0] = 97.1%
6 train: [0] = 0.05986
6 train: [0] = 98.1%
6 val: [0] = 0.0999
6 val: [0] = 97.2%
7 train: [0] = 0.05156
7 train: [0] = 98.4%
7 val: [0] = 0.1027
7 val: [0] = 97.1%
8 train: [0] = 0.0428
8 train: [0] = 98.7%
8 val: [0] = 0.08703
8 val: [0] = 97.5%
9 train: [0] = 0.03908
9 train: [0] = 98.8%
9 val: [0] = 0.09885
9 val: [0] = 97.5%
10 train: [0] = 0.03177
10 train: [0] = 99.0%
10 val: [0] = 0.09502
10 val: [0] = 97.5%
11 train: [0] = 0.02971
11 train: [0] = 99.1%
11 val: [0] = 0.1046
11 val: [0] = 97.4%
12 train: [0] = 0.02526
12 train: [0] = 99.2%
12 val: [0] = 0.09821
12 val: [0] = 97.5%
13 train: [0] = 0.02371
13 train: [0] = 99.2%
13 val: [0] = 0.1043
13 val: [0] = 97.4%
14 train: [0] = 0.01894
14 train: [0] = 99.4%
14 val: [0] = 0.1024
14 val: [0] = 97.6%
15 train: [0] = 0.01628
15 train: [0] = 99.5%
15 val: [0] = 0.1169
15 val: [0] = 97.4%
16 train: [0] = 0.01858
16 train: [0] = 99.4%
16 val: [0] = 0.1209
16 val: [0] = 97.3%
17 train: [0] = 0.01505
17 train: [0] = 99.5%
17 val: [0] = 0.1389
17 val: [0] = 97.1%
18 train: [0] = 0.01465
18 train: [0] = 99.5%
18 val: [0] = 0.1214
18 val: [0] = 97.4%
19 train: [0] = 0.01252
19 train: [0] = 99.6%
19 val: [0] = 0.1269
19 val: [0] = 97.6%
[run_0] test accuracy = 97.3%
[run_0] training autoencoder for step 0:
0 train: [0] = 0.114335, [1] = 0.208096, [2] = 0.839225
0 val: [0] = 0.0832022, [1] = 0.132909, [2] = 0.792424
1 train: [0] = 0.0828433, [1] = 0.133924, [2] = 0.79699
1 val: [0] = 0.0812166, [1] = 0.132375, [2] = 0.791949
2 train: [0] = 0.0810908, [1] = 0.134219, [2] = 0.794352
2 val: [0] = 0.0765916, [1] = 0.133654, [2] = 0.781308
3 train: [0] = 0.0747229, [1] = 0.124602, [2] = 0.705717
3 val: [0] = 0.0707681, [1] = 0.118312, [2] = 0.662624
4 train: [0] = 0.0677972, [1] = 0.112738, [2] = 0.633773
4 val: [0] = 0.063325, [1] = 0.104713, [2] = 0.585189
5 train: [0] = 0.0635294, [1] = 0.103699, [2] = 0.576971
5 val: [0] = 0.0611854, [1] = 0.100948, [2] = 0.558583
6 train: [0] = 0.0609966, [1] = 0.0997589, [2] = 0.550673
6 val: [0] = 0.0581327, [1] = 0.0964263, [2] = 0.532416
7 train: [0] = 0.0569736, [1] = 0.0941414, [2] = 0.513914
7 val: [0] = 0.0540349, [1] = 0.0903594, [2] = 0.489487
8 train: [0] = 0.0539179, [1] = 0.0896929, [2] = 0.481991
8 val: [0] = 0.0516447, [1] = 0.0869124, [2] = 0.465755
9 train: [0] = 0.0513678, [1] = 0.0867312, [2] = 0.459856
9 val: [0] = 0.0487686, [1] = 0.0838115, [2] = 0.43993
10 train: [0] = 0.0487102, [1] = 0.0832612, [2] = 0.434843
10 val: [0] = 0.0465341, [1] = 0.0805134, [2] = 0.418527
11 train: [0] = 0.0466199, [1] = 0.0805452, [2] = 0.416322
11 val: [0] = 0.0447426, [1] = 0.0785154, [2] = 0.402609
12 train: [0] = 0.0447526, [1] = 0.0783373, [2] = 0.401193
12 val: [0] = 0.042971, [1] = 0.0762224, [2] = 0.388368
13 train: [0] = 0.0431245, [1] = 0.0762583, [2] = 0.387985
13 val: [0] = 0.0417441, [1] = 0.0748334, [2] = 0.378033
14 train: [0] = 0.0416503, [1] = 0.0745341, [2] = 0.376439
14 val: [0] = 0.0403428, [1] = 0.0730535, [2] = 0.366981
15 train: [0] = 0.0404526, [1] = 0.0731608, [2] = 0.366198
15 val: [0] = 0.0392199, [1] = 0.0716271, [2] = 0.360491
16 train: [0] = 0.0394885, [1] = 0.07197, [2] = 0.357116
16 val: [0] = 0.0383099, [1] = 0.0707486, [2] = 0.348843
17 train: [0] = 0.0386835, [1] = 0.0709017, [2] = 0.349049
17 val: [0] = 0.037609, [1] = 0.0697679, [2] = 0.341941
18 train: [0] = 0.0380538, [1] = 0.0699968, [2] = 0.342453
18 val: [0] = 0.0370991, [1] = 0.0687503, [2] = 0.336378
19 train: [0] = 0.0374742, [1] = 0.0691706, [2] = 0.336649
19 val: [0] = 0.0365881, [1] = 0.0681885, [2] = 0.33149
20 train: [0] = 0.0369571, [1] = 0.0684732, [2] = 0.331909
20 val: [0] = 0.0361953, [1] = 0.0676475, [2] = 0.327139
21 train: [0] = 0.0364877, [1] = 0.0678513, [2] = 0.327682
21 val: [0] = 0.0355742, [1] = 0.0669303, [2] = 0.323149
22 train: [0] = 0.0359818, [1] = 0.0672175, [2] = 0.323383
22 val: [0] = 0.0351111, [1] = 0.0663227, [2] = 0.319476
23 train: [0] = 0.0353467, [1] = 0.0664351, [2] = 0.317432
23 val: [0] = 0.0344597, [1] = 0.0654681, [2] = 0.312285
24 train: [0] = 0.0347475, [1] = 0.0656146, [2] = 0.312204
24 val: [0] = 0.03394, [1] = 0.0647438, [2] = 0.308327
25 train: [0] = 0.0341711, [1] = 0.064787, [2] = 0.307084
25 val: [0] = 0.0336017, [1] = 0.064324, [2] = 0.304584
26 train: [0] = 0.0337156, [1] = 0.0641345, [2] = 0.3032
26 val: [0] = 0.0329354, [1] = 0.0631224, [2] = 0.299076
27 train: [0] = 0.0332684, [1] = 0.0635188, [2] = 0.299202
27 val: [0] = 0.0326222, [1] = 0.0629345, [2] = 0.295629
28 train: [0] = 0.0328349, [1] = 0.0629118, [2] = 0.295253
28 val: [0] = 0.0322348, [1] = 0.0623126, [2] = 0.29342
29 train: [0] = 0.032475, [1] = 0.0624021, [2] = 0.29206
29 val: [0] = 0.0317871, [1] = 0.0616395, [2] = 0.288403
30 train: [0] = 0.0321078, [1] = 0.0618489, [2] = 0.288578
30 val: [0] = 0.0315771, [1] = 0.0612316, [2] = 0.286005
31 train: [0] = 0.0317864, [1] = 0.0613662, [2] = 0.285569
31 val: [0] = 0.0314855, [1] = 0.0610699, [2] = 0.283785
32 train: [0] = 0.0314069, [1] = 0.0608615, [2] = 0.282213
32 val: [0] = 0.0307176, [1] = 0.060082, [2] = 0.279166
33 train: [0] = 0.0309834, [1] = 0.0602862, [2] = 0.278224
33 val: [0] = 0.0305945, [1] = 0.0598577, [2] = 0.277533
34 train: [0] = 0.0306848, [1] = 0.0598331, [2] = 0.275493
34 val: [0] = 0.0301748, [1] = 0.0592578, [2] = 0.273214
35 train: [0] = 0.0303572, [1] = 0.0593723, [2] = 0.272456
35 val: [0] = 0.0298964, [1] = 0.058939, [2] = 0.27036
36 train: [0] = 0.0300125, [1] = 0.0589055, [2] = 0.269477
36 val: [0] = 0.0295715, [1] = 0.0584136, [2] = 0.26918
37 train: [0] = 0.0297126, [1] = 0.0584897, [2] = 0.266926
37 val: [0] = 0.0293963, [1] = 0.0583215, [2] = 0.265629
38 train: [0] = 0.0294138, [1] = 0.0581035, [2] = 0.264545
38 val: [0] = 0.029126, [1] = 0.0579906, [2] = 0.264221
39 train: [0] = 0.0291454, [1] = 0.0577209, [2] = 0.262355
39 val: [0] = 0.0288952, [1] = 0.0576294, [2] = 0.261764
40 train: [0] = 0.0288409, [1] = 0.0573233, [2] = 0.25975
40 val: [0] = 0.0289205, [1] = 0.0578405, [2] = 0.263444
41 train: [0] = 0.028563, [1] = 0.0569656, [2] = 0.257626
41 val: [0] = 0.0281583, [1] = 0.0566671, [2] = 0.256764
42 train: [0] = 0.0282492, [1] = 0.056529, [2] = 0.254872
42 val: [0] = 0.0278991, [1] = 0.056251, [2] = 0.254852
43 train: [0] = 0.0280077, [1] = 0.056198, [2] = 0.253055
43 val: [0] = 0.0276761, [1] = 0.0559001, [2] = 0.252853
44 train: [0] = 0.027748, [1] = 0.0558, [2] = 0.2507
44 val: [0] = 0.0275192, [1] = 0.0556157, [2] = 0.250226
45 train: [0] = 0.0274597, [1] = 0.055421, [2] = 0.2483
45 val: [0] = 0.0272428, [1] = 0.0552181, [2] = 0.24883
46 train: [0] = 0.02726, [1] = 0.0551244, [2] = 0.246541
46 val: [0] = 0.0270156, [1] = 0.0550249, [2] = 0.246534
47 train: [0] = 0.0270598, [1] = 0.0548241, [2] = 0.244707
47 val: [0] = 0.0270773, [1] = 0.0551057, [2] = 0.246523
48 train: [0] = 0.0268487, [1] = 0.0545268, [2] = 0.242914
48 val: [0] = 0.0265951, [1] = 0.0544013, [2] = 0.243842
49 train: [0] = 0.0266944, [1] = 0.0542904, [2] = 0.241573
49 val: [0] = 0.0265485, [1] = 0.0543352, [2] = 0.242888
50 train: [0] = 0.0264403, [1] = 0.0539554, [2] = 0.239184
50 val: [0] = 0.0262496, [1] = 0.0538571, [2] = 0.240046
51 train: [0] = 0.0262227, [1] = 0.0536731, [2] = 0.237388
51 val: [0] = 0.0261194, [1] = 0.0538626, [2] = 0.239038
52 train: [0] = 0.026037, [1] = 0.0534184, [2] = 0.235677
52 val: [0] = 0.0258358, [1] = 0.0534624, [2] = 0.236453
53 train: [0] = 0.0258502, [1] = 0.0531762, [2] = 0.23407
53 val: [0] = 0.025726, [1] = 0.0531617, [2] = 0.23575
54 train: [0] = 0.0256731, [1] = 0.0529527, [2] = 0.23246
54 val: [0] = 0.025557, [1] = 0.0530577, [2] = 0.234561
55 train: [0] = 0.0254829, [1] = 0.0526978, [2] = 0.230562
55 val: [0] = 0.0253538, [1] = 0.0527544, [2] = 0.231584
56 train: [0] = 0.025323, [1] = 0.0524745, [2] = 0.228982
56 val: [0] = 0.0251996, [1] = 0.0525154, [2] = 0.230273
57 train: [0] = 0.0251506, [1] = 0.0522323, [2] = 0.227253
57 val: [0] = 0.0249897, [1] = 0.0521939, [2] = 0.227925
58 train: [0] = 0.0249955, [1] = 0.0520046, [2] = 0.225628
58 val: [0] = 0.0248585, [1] = 0.0518942, [2] = 0.226236
59 train: [0] = 0.0248566, [1] = 0.0517855, [2] = 0.223943
59 val: [0] = 0.0247285, [1] = 0.0518088, [2] = 0.224742
60 train: [0] = 0.0247093, [1] = 0.05156, [2] = 0.222301
60 val: [0] = 0.0246239, [1] = 0.051628, [2] = 0.223398
61 train: [0] = 0.0245782, [1] = 0.0513534, [2] = 0.220807
61 val: [0] = 0.0245808, [1] = 0.0515108, [2] = 0.221996
62 train: [0] = 0.0244536, [1] = 0.0511634, [2] = 0.21941
62 val: [0] = 0.0243437, [1] = 0.0512051, [2] = 0.220094
63 train: [0] = 0.0243462, [1] = 0.0509825, [2] = 0.218168
63 val: [0] = 0.0243028, [1] = 0.0510688, [2] = 0.219418
64 train: [0] = 0.0242026, [1] = 0.0507674, [2] = 0.216627
64 val: [0] = 0.0241002, [1] = 0.0507729, [2] = 0.21762
65 train: [0] = 0.0240994, [1] = 0.050611, [2] = 0.215476
65 val: [0] = 0.0240625, [1] = 0.0508718, [2] = 0.218527
66 train: [0] = 0.023956, [1] = 0.0503975, [2] = 0.213961
66 val: [0] = 0.0239752, [1] = 0.0506551, [2] = 0.215857
67 train: [0] = 0.023858, [1] = 0.0502404, [2] = 0.212819
67 val: [0] = 0.0238299, [1] = 0.0504361, [2] = 0.21549
68 train: [0] = 0.0237661, [1] = 0.0500951, [2] = 0.21195
68 val: [0] = 0.0238006, [1] = 0.0502659, [2] = 0.21491
69 train: [0] = 0.0236627, [1] = 0.0499419, [2] = 0.210854
69 val: [0] = 0.0237339, [1] = 0.0501695, [2] = 0.212984
70 train: [0] = 0.0235569, [1] = 0.0497837, [2] = 0.209661
70 val: [0] = 0.0235996, [1] = 0.050124, [2] = 0.211964
71 train: [0] = 0.0234559, [1] = 0.0496448, [2] = 0.208663
71 val: [0] = 0.0233988, [1] = 0.0497672, [2] = 0.210334
72 train: [0] = 0.023353, [1] = 0.0494846, [2] = 0.207535
72 val: [0] = 0.0234525, [1] = 0.0498646, [2] = 0.210433
73 train: [0] = 0.0232843, [1] = 0.0494061, [2] = 0.207063
73 val: [0] = 0.0232643, [1] = 0.0496744, [2] = 0.208938
74 train: [0] = 0.0231544, [1] = 0.0492262, [2] = 0.205751
74 val: [0] = 0.0231857, [1] = 0.0494505, [2] = 0.209053
75 train: [0] = 0.0230591, [1] = 0.0491045, [2] = 0.204956
75 val: [0] = 0.0231357, [1] = 0.0494695, [2] = 0.207609
76 train: [0] = 0.022928, [1] = 0.0489401, [2] = 0.203723
76 val: [0] = 0.0230801, [1] = 0.0491916, [2] = 0.207186
77 train: [0] = 0.0228228, [1] = 0.0487835, [2] = 0.202601
77 val: [0] = 0.0228949, [1] = 0.0491176, [2] = 0.205308
78 train: [0] = 0.02273, [1] = 0.0486517, [2] = 0.201748
78 val: [0] = 0.0228307, [1] = 0.0489707, [2] = 0.205101
79 train: [0] = 0.022621, [1] = 0.0484936, [2] = 0.200584
79 val: [0] = 0.0226337, [1] = 0.0487601, [2] = 0.203052
[run_0] training putter for step 0:
0 train: [0] = 0.08164, [1] = 0.1993, [2] = 0.0003253, [3] = 0.08242, [4] = 0.07538, [5] = 0.01395, [6] = 0.01315
0 train: [0] = 94.9%, [1] = 100.0%
0 val: [0] = 0.07082, [1] = 0.001041, [2] = 0.0007232, [3] = 0.07156, [4] = 0.07676, [5] = 0.02673, [6] = 0.01315
0 val: [0] = 100.0%, [1] = 100.0%
1 train: [0] = 0.05684, [1] = 0.0008485, [2] = 0.001106, [3] = 0.06091, [4] = 0.074, [5] = 0.01397, [6] = 0.01315
1 train: [0] = 100.0%, [1] = 100.0%
1 val: [0] = 0.04745, [1] = 0.0004375, [2] = 0.001493, [3] = 0.05259, [4] = 0.08363, [5] = 0.02696, [6] = 0.01315
1 val: [0] = 100.0%, [1] = 100.0%
2 train: [0] = 0.05597, [1] = 0.0086, [2] = 0.00169, [3] = 0.06122, [4] = 0.07433, [5] = 0.01395, [6] = 0.01315
2 train: [0] = 99.8%, [1] = 100.0%
2 val: [0] = 0.04767, [1] = 0.0001234, [2] = 0.001389, [3] = 0.05216, [4] = 0.07208, [5] = 0.02668, [6] = 0.01315
2 val: [0] = 100.0%, [1] = 100.0%
3 train: [0] = 0.04659, [1] = 0.0004166, [2] = 0.00133, [3] = 0.05131, [4] = 0.07269, [5] = 0.01395, [6] = 0.01315
3 train: [0] = 100.0%, [1] = 100.0%
3 val: [0] = 0.04343, [1] = 8.509e-05, [2] = 0.001242, [3] = 0.04829, [4] = 0.07547, [5] = 0.0267, [6] = 0.01315
3 val: [0] = 100.0%, [1] = 100.0%
4 train: [0] = 0.04245, [1] = 0.0002331, [2] = 0.001596, [3] = 0.0485, [4] = 0.07299, [5] = 0.01404, [6] = 0.01315
4 train: [0] = 100.0%, [1] = 100.0%
4 val: [0] = 0.03937, [1] = 0.0002879, [2] = 0.001888, [3] = 0.04589, [4] = 0.0743, [5] = 0.0267, [6] = 0.01315
4 val: [0] = 100.0%, [1] = 100.0%
5 train: [0] = 0.04835, [1] = 0.007746, [2] = 0.002232, [3] = 0.05533, [4] = 0.07374, [5] = 0.01395, [6] = 0.01315
5 train: [0] = 99.8%, [1] = 100.0%
5 val: [0] = 0.04305, [1] = 0.0001521, [2] = 0.001694, [3] = 0.04848, [4] = 0.08403, [5] = 0.02709, [6] = 0.01315
5 val: [0] = 100.0%, [1] = 100.0%
6 train: [0] = 0.04183, [1] = 0.0002612, [2] = 0.001758, [3] = 0.04746, [4] = 0.07246, [5] = 0.01395, [6] = 0.01315
6 train: [0] = 100.0%, [1] = 100.0%
6 val: [0] = 0.03943, [1] = 0.0002376, [2] = 0.001655, [3] = 0.0446, [4] = 0.07428, [5] = 0.02697, [6] = 0.01315
6 val: [0] = 100.0%, [1] = 100.0%
7 train: [0] = 0.03958, [1] = 0.0003096, [2] = 0.001718, [3] = 0.04543, [4] = 0.0768, [5] = 0.01395, [6] = 0.01315
7 train: [0] = 100.0%, [1] = 100.0%
7 val: [0] = 0.03773, [1] = 0.0002337, [2] = 0.001571, [3] = 0.04337, [4] = 0.07651, [5] = 0.02686, [6] = 0.01315
7 val: [0] = 100.0%, [1] = 100.0%
8 train: [0] = 0.04285, [1] = 0.001604, [2] = 0.002005, [3] = 0.04962, [4] = 0.07469, [5] = 0.01397, [6] = 0.01315
8 train: [0] = 100.0%, [1] = 100.0%
8 val: [0] = 0.038, [1] = 0.0001411, [2] = 0.001469, [3] = 0.04346, [4] = 0.07223, [5] = 0.0267, [6] = 0.01315
8 val: [0] = 100.0%, [1] = 100.0%
9 train: [0] = 0.03778, [1] = 0.0002626, [2] = 0.001577, [3] = 0.04383, [4] = 0.07325, [5] = 0.01395, [6] = 0.01315
9 train: [0] = 100.0%, [1] = 100.0%
9 val: [0] = 0.03583, [1] = 0.0002311, [2] = 0.001601, [3] = 0.04206, [4] = 0.07459, [5] = 0.02674, [6] = 0.01315
9 val: [0] = 100.0%, [1] = 100.0%
10 train: [0] = 0.03625, [1] = 0.000367, [2] = 0.001889, [3] = 0.04369, [4] = 0.07206, [5] = 0.01398, [6] = 0.01315
10 train: [0] = 100.0%, [1] = 100.0%
10 val: [0] = 0.03981, [1] = 0.002912, [2] = 0.002709, [3] = 0.04888, [4] = 0.0736, [5] = 0.02668, [6] = 0.01315
10 val: [0] = 99.9%, [1] = 100.0%
11 train: [0] = 0.04508, [1] = 0.0015, [2] = 0.002175, [3] = 0.05305, [4] = 0.07276, [5] = 0.01395, [6] = 0.01315
11 train: [0] = 100.0%, [1] = 100.0%
11 val: [0] = 0.03671, [1] = 0.000144, [2] = 0.001792, [3] = 0.04315, [4] = 0.07434, [5] = 0.02705, [6] = 0.01315
11 val: [0] = 100.0%, [1] = 100.0%
12 train: [0] = 0.03568, [1] = 0.000272, [2] = 0.001939, [3] = 0.04298, [4] = 0.07332, [5] = 0.01395, [6] = 0.01315
12 train: [0] = 100.0%, [1] = 100.0%
12 val: [0] = 0.03334, [1] = 0.0004868, [2] = 0.002024, [3] = 0.0406, [4] = 0.07495, [5] = 0.02734, [6] = 0.01315
12 val: [0] = 100.0%, [1] = 100.0%
13 train: [0] = 0.04092, [1] = 0.002439, [2] = 0.002185, [3] = 0.04864, [4] = 0.07361, [5] = 0.01395, [6] = 0.01315
13 train: [0] = 100.0%, [1] = 100.0%
13 val: [0] = 0.03462, [1] = 0.0003368, [2] = 0.001775, [3] = 0.0412, [4] = 0.07806, [5] = 0.02672, [6] = 0.01315
13 val: [0] = 100.0%, [1] = 100.0%
14 train: [0] = 0.03644, [1] = 0.000732, [2] = 0.002319, [3] = 0.04463, [4] = 0.07406, [5] = 0.01394, [6] = 0.01315
14 train: [0] = 100.0%, [1] = 100.0%
14 val: [0] = 0.03878, [1] = 0.0005738, [2] = 0.002244, [3] = 0.04615, [4] = 0.07666, [5] = 0.02689, [6] = 0.01315
14 val: [0] = 100.0%, [1] = 100.0%
15 train: [0] = 0.03576, [1] = 0.002165, [2] = 0.002341, [3] = 0.04382, [4] = 0.07362, [5] = 0.01395, [6] = 0.01315
15 train: [0] = 99.9%, [1] = 100.0%
15 val: [0] = 0.05427, [1] = 0.05728, [2] = 0.005469, [3] = 0.06764, [4] = 0.07669, [5] = 0.02675, [6] = 0.01315
15 val: [0] = 98.7%, [1] = 100.0%
16 train: [0] = 0.03945, [1] = 0.0006022, [2] = 0.002306, [3] = 0.04742, [4] = 0.07186, [5] = 0.01397, [6] = 0.01315
16 train: [0] = 100.0%, [1] = 100.0%
16 val: [0] = 0.03466, [1] = 0.000124, [2] = 0.001952, [3] = 0.04165, [4] = 0.07472, [5] = 0.02671, [6] = 0.01315
16 val: [0] = 100.0%, [1] = 100.0%
17 train: [0] = 0.03389, [1] = 0.0004377, [2] = 0.002194, [3] = 0.04123, [4] = 0.07409, [5] = 0.01395, [6] = 0.01315
17 train: [0] = 100.0%, [1] = 100.0%
17 val: [0] = 0.0326, [1] = 0.0001497, [2] = 0.002056, [3] = 0.03947, [4] = 0.07273, [5] = 0.0268, [6] = 0.01315
17 val: [0] = 100.0%, [1] = 100.0%
18 train: [0] = 0.03226, [1] = 0.0003623, [2] = 0.002106, [3] = 0.03921, [4] = 0.07329, [5] = 0.01395, [6] = 0.01315
18 train: [0] = 100.0%, [1] = 100.0%
18 val: [0] = 0.03119, [1] = 0.0002204, [2] = 0.002039, [3] = 0.03775, [4] = 0.07672, [5] = 0.02701, [6] = 0.01315
18 val: [0] = 100.0%, [1] = 100.0%
19 train: [0] = 0.03371, [1] = 0.0007624, [2] = 0.00241, [3] = 0.04156, [4] = 0.07571, [5] = 0.01395, [6] = 0.01315
19 train: [0] = 100.0%, [1] = 100.0%
19 val: [0] = 0.03103, [1] = 0.0002341, [2] = 0.001952, [3] = 0.03744, [4] = 0.07396, [5] = 0.02674, [6] = 0.01315
19 val: [0] = 100.0%, [1] = 100.0%
[run_0] training getter for step 0:
0 train: [0] = 0.03368, [1] = 0.1312, [2] = 0.001985, [3] = 0.03983, [4] = 0.07023, [5] = 0.3517, [6] = 0.8987
0 train: [0] = 97.7%, [1] = 76.7%
0 val: [0] = 0.03123, [1] = 0.0008736, [2] = 0.001891, [3] = 0.03727, [4] = 0.07713, [5] = 0.09883, [6] = 0.8943
0 val: [0] = 100.0%, [1] = 80.0%
1 train: [0] = 0.03173, [1] = 0.0007332, [2] = 0.001974, [3] = 0.03798, [4] = 0.07446, [5] = 0.08326, [6] = 1.072
1 train: [0] = 100.0%, [1] = 81.5%
1 val: [0] = 0.03118, [1] = 0.000385, [2] = 0.001909, [3] = 0.0373, [4] = 0.0754, [5] = 0.06033, [6] = 1.148
1 val: [0] = 100.0%, [1] = 82.0%
2 train: [0] = 0.03166, [1] = 0.0004648, [2] = 0.001973, [3] = 0.03795, [4] = 0.07591, [5] = 0.05616, [6] = 1.265
2 train: [0] = 100.0%, [1] = 83.5%
2 val: [0] = 0.03128, [1] = 0.0005264, [2] = 0.001905, [3] = 0.03742, [4] = 0.07374, [5] = 0.04829, [6] = 1.341
2 val: [0] = 100.0%, [1] = 85.0%
3 train: [0] = 0.03163, [1] = 0.0001907, [2] = 0.001959, [3] = 0.03795, [4] = 0.07463, [5] = 0.04387, [6] = 1.411
3 train: [0] = 100.0%, [1] = 85.9%
3 val: [0] = 0.03114, [1] = 2.073e-05, [2] = 0.001949, [3] = 0.03731, [4] = 0.07557, [5] = 0.03636, [6] = 1.262
3 val: [0] = 100.0%, [1] = 86.0%
4 train: [0] = 0.03167, [1] = 0.002378, [2] = 0.001986, [3] = 0.03803, [4] = 0.07677, [5] = 0.03851, [6] = 1.431
4 train: [0] = 99.9%, [1] = 86.7%
4 val: [0] = 0.0311, [1] = 9.171e-06, [2] = 0.001913, [3] = 0.03728, [4] = 0.07122, [5] = 0.03542, [6] = 1.269
4 val: [0] = 100.0%, [1] = 87.0%
5 train: [0] = 0.03157, [1] = 3.158e-05, [2] = 0.001963, [3] = 0.03792, [4] = 0.07333, [5] = 0.03214, [6] = 1.369
5 train: [0] = 100.0%, [1] = 87.9%
5 val: [0] = 0.03108, [1] = 2.716e-06, [2] = 0.001882, [3] = 0.03718, [4] = 0.07406, [5] = 0.02874, [6] = 1.342
5 val: [0] = 100.0%, [1] = 88.0%
6 train: [0] = 0.03157, [1] = 1.077e-05, [2] = 0.00198, [3] = 0.03793, [4] = 0.07488, [5] = 0.02767, [6] = 1.524
6 train: [0] = 100.0%, [1] = 87.6%
6 val: [0] = 0.03123, [1] = 8.763e-05, [2] = 0.001928, [3] = 0.03737, [4] = 0.06907, [5] = 0.02541, [6] = 1.763
6 val: [0] = 100.0%, [1] = 87.0%
7 train: [0] = 0.03158, [1] = 4.345e-05, [2] = 0.001963, [3] = 0.03792, [4] = 0.07464, [5] = 0.02456, [6] = 1.55
7 train: [0] = 100.0%, [1] = 88.6%
7 val: [0] = 0.03107, [1] = 4.05e-06, [2] = 0.001906, [3] = 0.03726, [4] = 0.07627, [5] = 0.02248, [6] = 1.322
7 val: [0] = 100.0%, [1] = 89.0%
8 train: [0] = 0.03164, [1] = 0.0027, [2] = 0.00197, [3] = 0.03799, [4] = 0.07439, [5] = 0.02515, [6] = 1.796
8 train: [0] = 99.9%, [1] = 88.0%
8 val: [0] = 0.03123, [1] = 3.496e-05, [2] = 0.001926, [3] = 0.03737, [4] = 0.07698, [5] = 0.024, [6] = 1.842
8 val: [0] = 100.0%, [1] = 90.0%
9 train: [0] = 0.03149, [1] = 6.769e-06, [2] = 0.001972, [3] = 0.03792, [4] = 0.0753, [5] = 0.02167, [6] = 1.795
9 train: [0] = 100.0%, [1] = 89.1%
9 val: [0] = 0.0311, [1] = 1.332e-07, [2] = 0.001925, [3] = 0.03725, [4] = 0.07845, [5] = 0.01985, [6] = 1.628
9 val: [0] = 100.0%, [1] = 89.0%
10 train: [0] = 0.03161, [1] = 0.001308, [2] = 0.001985, [3] = 0.03801, [4] = 0.07772, [5] = 0.0218, [6] = 1.6
10 train: [0] = 100.0%, [1] = 88.9%
10 val: [0] = 0.0311, [1] = 1.647e-07, [2] = 0.001925, [3] = 0.03723, [4] = 0.07127, [5] = 0.02016, [6] = 1.723
10 val: [0] = 100.0%, [1] = 88.0%
11 train: [0] = 0.03151, [1] = 0.0001987, [2] = 0.001974, [3] = 0.0379, [4] = 0.07338, [5] = 0.01856, [6] = 1.68
11 train: [0] = 100.0%, [1] = 88.9%
11 val: [0] = 0.03122, [1] = 0.0001329, [2] = 0.00193, [3] = 0.03742, [4] = 0.07404, [5] = 0.02003, [6] = 1.354
11 val: [0] = 100.0%, [1] = 90.0%
12 train: [0] = 0.03152, [1] = 1.44e-05, [2] = 0.001978, [3] = 0.03788, [4] = 0.07655, [5] = 0.01827, [6] = 1.54
12 train: [0] = 100.0%, [1] = 88.9%
12 val: [0] = 0.03118, [1] = 0.0005238, [2] = 0.001898, [3] = 0.03736, [4] = 0.07521, [5] = 0.01752, [6] = 1.668
12 val: [0] = 100.0%, [1] = 87.0%
13 train: [0] = 0.03153, [1] = 0.001428, [2] = 0.001972, [3] = 0.03789, [4] = 0.07586, [5] = 0.01755, [6] = 1.699
13 train: [0] = 100.0%, [1] = 88.1%
13 val: [0] = 0.03106, [1] = 5.942e-07, [2] = 0.001932, [3] = 0.03726, [4] = 0.07716, [5] = 0.01465, [6] = 1.621
13 val: [0] = 100.0%, [1] = 89.0%
14 train: [0] = 0.03153, [1] = 2.281e-05, [2] = 0.001967, [3] = 0.03789, [4] = 0.07662, [5] = 0.01625, [6] = 1.622
14 train: [0] = 100.0%, [1] = 89.7%
14 val: [0] = 0.03109, [1] = 3.242e-05, [2] = 0.001919, [3] = 0.03722, [4] = 0.07958, [5] = 0.01498, [6] = 1.527
14 val: [0] = 100.0%, [1] = 90.0%
15 train: [0] = 0.03153, [1] = 0.0008711, [2] = 0.001987, [3] = 0.03792, [4] = 0.07651, [5] = 0.01644, [6] = 1.639
15 train: [0] = 100.0%, [1] = 88.3%
15 val: [0] = 0.03111, [1] = 0.0003083, [2] = 0.001923, [3] = 0.03727, [4] = 0.07518, [5] = 0.01668, [6] = 1.956
15 val: [0] = 100.0%, [1] = 85.0%
16 train: [0] = 0.0315, [1] = 3.093e-06, [2] = 0.001979, [3] = 0.03787, [4] = 0.0759, [5] = 0.0151, [6] = 1.532
16 train: [0] = 100.0%, [1] = 90.2%
16 val: [0] = 0.031, [1] = 2.385e-09, [2] = 0.001947, [3] = 0.03722, [4] = 0.07792, [5] = 0.01367, [6] = 1.431
16 val: [0] = 100.0%, [1] = 92.0%
17 train: [0] = 0.03149, [1] = 9.096e-06, [2] = 0.001976, [3] = 0.03788, [4] = 0.07764, [5] = 0.01367, [6] = 1.47
17 train: [0] = 100.0%, [1] = 91.4%
17 val: [0] = 0.03102, [1] = 4.746e-10, [2] = 0.001936, [3] = 0.03722, [4] = 0.07558, [5] = 0.01291, [6] = 1.621
17 val: [0] = 100.0%, [1] = 91.0%
18 train: [0] = 0.03149, [1] = 0.0003857, [2] = 0.001968, [3] = 0.03787, [4] = 0.07462, [5] = 0.01389, [6] = 1.482
18 train: [0] = 100.0%, [1] = 90.2%
18 val: [0] = 0.03108, [1] = 5.784e-07, [2] = 0.001914, [3] = 0.03719, [4] = 0.07155, [5] = 0.01395, [6] = 1.621
18 val: [0] = 100.0%, [1] = 91.0%
19 train: [0] = 0.03147, [1] = 0.001079, [2] = 0.001977, [3] = 0.03789, [4] = 0.07554, [5] = 0.01315, [6] = 1.585
19 train: [0] = 100.0%, [1] = 89.4%
19 val: [0] = 0.03103, [1] = 3.298e-07, [2] = 0.00191, [3] = 0.03726, [4] = 0.07242, [5] = 0.01331, [6] = 1.537
19 val: [0] = 100.0%, [1] = 89.0%
[run_0] test accuracy = 88.5%
[run_0] training autoencoder for step 1:
0 train: [0] = 0.114498, [1] = 0.208323, [2] = 0.839833
0 val: [0] = 0.0836347, [1] = 0.133032, [2] = 0.792391
1 train: [0] = 0.0821969, [1] = 0.133835, [2] = 0.79344
1 val: [0] = 0.0763794, [1] = 0.131927, [2] = 0.773826
2 train: [0] = 0.0750405, [1] = 0.124027, [2] = 0.706591
2 val: [0] = 0.0715194, [1] = 0.1183, [2] = 0.666978
3 train: [0] = 0.07001, [1] = 0.115872, [2] = 0.653128
3 val: [0] = 0.066913, [1] = 0.112434, [2] = 0.632153
4 train: [0] = 0.0641505, [1] = 0.106851, [2] = 0.594467
4 val: [0] = 0.0608903, [1] = 0.101496, [2] = 0.562585
5 train: [0] = 0.0609305, [1] = 0.100734, [2] = 0.553758
5 val: [0] = 0.0586625, [1] = 0.0976442, [2] = 0.536384
6 train: [0] = 0.0587639, [1] = 0.0970788, [2] = 0.531545
6 val: [0] = 0.0562707, [1] = 0.0945058, [2] = 0.51712
7 train: [0] = 0.0547147, [1] = 0.0923531, [2] = 0.502046
7 val: [0] = 0.0510759, [1] = 0.0879559, [2] = 0.477128
8 train: [0] = 0.0505941, [1] = 0.0870904, [2] = 0.46612
8 val: [0] = 0.0485318, [1] = 0.084369, [2] = 0.449663
9 train: [0] = 0.0486823, [1] = 0.0840498, [2] = 0.445075
9 val: [0] = 0.0468142, [1] = 0.0818765, [2] = 0.432052
10 train: [0] = 0.0470034, [1] = 0.081895, [2] = 0.429904
10 val: [0] = 0.0450451, [1] = 0.0795292, [2] = 0.415908
11 train: [0] = 0.0454589, [1] = 0.0798722, [2] = 0.416025
11 val: [0] = 0.043623, [1] = 0.0775056, [2] = 0.403325
12 train: [0] = 0.0440166, [1] = 0.07784, [2] = 0.402206
12 val: [0] = 0.0423732, [1] = 0.075543, [2] = 0.390872
13 train: [0] = 0.0428234, [1] = 0.0760875, [2] = 0.390627
13 val: [0] = 0.0414523, [1] = 0.0742967, [2] = 0.380953
14 train: [0] = 0.0417481, [1] = 0.0745962, [2] = 0.3798
14 val: [0] = 0.0405099, [1] = 0.0731018, [2] = 0.370717
15 train: [0] = 0.0408675, [1] = 0.0734343, [2] = 0.371167
15 val: [0] = 0.0398096, [1] = 0.0722271, [2] = 0.363357
16 train: [0] = 0.0401689, [1] = 0.0724747, [2] = 0.364221
16 val: [0] = 0.0389524, [1] = 0.0711713, [2] = 0.356531
17 train: [0] = 0.039487, [1] = 0.0715889, [2] = 0.357718
17 val: [0] = 0.0386943, [1] = 0.0707537, [2] = 0.351588
18 train: [0] = 0.0389798, [1] = 0.0708603, [2] = 0.352595
18 val: [0] = 0.0379843, [1] = 0.0697955, [2] = 0.346769
19 train: [0] = 0.0384826, [1] = 0.0701875, [2] = 0.347743
19 val: [0] = 0.0376588, [1] = 0.0692043, [2] = 0.342249
20 train: [0] = 0.0380204, [1] = 0.0696022, [2] = 0.3435
20 val: [0] = 0.0372098, [1] = 0.0687559, [2] = 0.338232
21 train: [0] = 0.0376266, [1] = 0.0690667, [2] = 0.339562
21 val: [0] = 0.0369245, [1] = 0.0684749, [2] = 0.335286
22 train: [0] = 0.0372681, [1] = 0.0686065, [2] = 0.336167
22 val: [0] = 0.0367048, [1] = 0.0680737, [2] = 0.33269
23 train: [0] = 0.0368994, [1] = 0.0681477, [2] = 0.332733
23 val: [0] = 0.0362276, [1] = 0.0674993, [2] = 0.329179
24 train: [0] = 0.0365863, [1] = 0.0677609, [2] = 0.33012
24 val: [0] = 0.0360698, [1] = 0.0673776, [2] = 0.326986
25 train: [0] = 0.0362865, [1] = 0.0674015, [2] = 0.327587
25 val: [0] = 0.0357039, [1] = 0.0667492, [2] = 0.324489
26 train: [0] = 0.036014, [1] = 0.0670444, [2] = 0.325275
26 val: [0] = 0.0354077, [1] = 0.0665081, [2] = 0.321894
27 train: [0] = 0.0356429, [1] = 0.066646, [2] = 0.322442
27 val: [0] = 0.0350807, [1] = 0.066113, [2] = 0.319753
28 train: [0] = 0.0352559, [1] = 0.0662315, [2] = 0.319542
28 val: [0] = 0.0347301, [1] = 0.0659035, [2] = 0.317191
29 train: [0] = 0.0348652, [1] = 0.0658004, [2] = 0.316873
29 val: [0] = 0.034201, [1] = 0.0651966, [2] = 0.314257
30 train: [0] = 0.0345102, [1] = 0.0653443, [2] = 0.314534
30 val: [0] = 0.0338936, [1] = 0.0647218, [2] = 0.311625
31 train: [0] = 0.0340507, [1] = 0.0647941, [2] = 0.311091
31 val: [0] = 0.0334051, [1] = 0.0640648, [2] = 0.308114
32 train: [0] = 0.0336623, [1] = 0.064279, [2] = 0.308184
32 val: [0] = 0.0332007, [1] = 0.063962, [2] = 0.306299
33 train: [0] = 0.033285, [1] = 0.0638193, [2] = 0.305654
33 val: [0] = 0.0328284, [1] = 0.0633921, [2] = 0.303233
34 train: [0] = 0.0329699, [1] = 0.0633908, [2] = 0.303228
34 val: [0] = 0.0324845, [1] = 0.0629274, [2] = 0.301909
35 train: [0] = 0.0326745, [1] = 0.0629954, [2] = 0.30132
35 val: [0] = 0.0321152, [1] = 0.0625104, [2] = 0.298994
36 train: [0] = 0.0323118, [1] = 0.0625588, [2] = 0.29864
36 val: [0] = 0.0318544, [1] = 0.0622359, [2] = 0.296945
37 train: [0] = 0.0320252, [1] = 0.0622033, [2] = 0.296591
37 val: [0] = 0.0317866, [1] = 0.0620087, [2] = 0.295997
38 train: [0] = 0.0317587, [1] = 0.0618717, [2] = 0.294871
38 val: [0] = 0.0314866, [1] = 0.0616811, [2] = 0.293542
39 train: [0] = 0.0314734, [1] = 0.0615127, [2] = 0.292659
39 val: [0] = 0.0313236, [1] = 0.061522, [2] = 0.292778
40 train: [0] = 0.0312086, [1] = 0.0611706, [2] = 0.290661
40 val: [0] = 0.0309596, [1] = 0.0611866, [2] = 0.29056
41 train: [0] = 0.0309592, [1] = 0.0608513, [2] = 0.288882
41 val: [0] = 0.0305899, [1] = 0.0605414, [2] = 0.28797
42 train: [0] = 0.0307289, [1] = 0.0605377, [2] = 0.287092
42 val: [0] = 0.0304152, [1] = 0.0603002, [2] = 0.286053
43 train: [0] = 0.0304882, [1] = 0.0602085, [2] = 0.284995
43 val: [0] = 0.0303563, [1] = 0.0600492, [2] = 0.285377
44 train: [0] = 0.0302314, [1] = 0.0598508, [2] = 0.282778
44 val: [0] = 0.0299856, [1] = 0.0596708, [2] = 0.282279
45 train: [0] = 0.0300453, [1] = 0.059559, [2] = 0.281127
45 val: [0] = 0.0299032, [1] = 0.0596084, [2] = 0.281531
46 train: [0] = 0.0298031, [1] = 0.0592248, [2] = 0.278953
46 val: [0] = 0.0294951, [1] = 0.0589799, [2] = 0.278498
47 train: [0] = 0.0295818, [1] = 0.0589056, [2] = 0.276908
47 val: [0] = 0.0294482, [1] = 0.0590118, [2] = 0.278476
48 train: [0] = 0.0293718, [1] = 0.0585959, [2] = 0.274994
48 val: [0] = 0.0290813, [1] = 0.0584406, [2] = 0.274417
49 train: [0] = 0.0291836, [1] = 0.0583294, [2] = 0.2733
49 val: [0] = 0.0290601, [1] = 0.0584132, [2] = 0.273382
50 train: [0] = 0.0289975, [1] = 0.0580784, [2] = 0.271537
50 val: [0] = 0.0289411, [1] = 0.0582519, [2] = 0.271617
51 train: [0] = 0.0288, [1] = 0.0578143, [2] = 0.269649
51 val: [0] = 0.0286277, [1] = 0.0577421, [2] = 0.269347
52 train: [0] = 0.0286108, [1] = 0.0575563, [2] = 0.267918
52 val: [0] = 0.0283965, [1] = 0.0574131, [2] = 0.26907
53 train: [0] = 0.0284015, [1] = 0.0572804, [2] = 0.265999
53 val: [0] = 0.0281309, [1] = 0.0571881, [2] = 0.265981
54 train: [0] = 0.0282279, [1] = 0.0570411, [2] = 0.264288
54 val: [0] = 0.0281696, [1] = 0.0572283, [2] = 0.265718
55 train: [0] = 0.0280472, [1] = 0.056816, [2] = 0.262751
55 val: [0] = 0.02787, [1] = 0.0567163, [2] = 0.263201
56 train: [0] = 0.0278641, [1] = 0.0565595, [2] = 0.261106
56 val: [0] = 0.0277385, [1] = 0.0566499, [2] = 0.261125
57 train: [0] = 0.0276839, [1] = 0.0562932, [2] = 0.259121
57 val: [0] = 0.027506, [1] = 0.0562522, [2] = 0.258824
58 train: [0] = 0.0274827, [1] = 0.0560126, [2] = 0.257054
58 val: [0] = 0.0273697, [1] = 0.0561547, [2] = 0.258198
59 train: [0] = 0.0273328, [1] = 0.0557661, [2] = 0.255359
59 val: [0] = 0.0272095, [1] = 0.0557993, [2] = 0.255694
60 train: [0] = 0.0271807, [1] = 0.0555312, [2] = 0.253589
60 val: [0] = 0.0269583, [1] = 0.0553731, [2] = 0.254082
61 train: [0] = 0.026979, [1] = 0.0552293, [2] = 0.251387
61 val: [0] = 0.0267221, [1] = 0.0549964, [2] = 0.250598
62 train: [0] = 0.0268074, [1] = 0.0549761, [2] = 0.249393
62 val: [0] = 0.026853, [1] = 0.0553261, [2] = 0.251138
63 train: [0] = 0.0266459, [1] = 0.0547356, [2] = 0.24755
63 val: [0] = 0.0264252, [1] = 0.0546211, [2] = 0.246945
64 train: [0] = 0.0265114, [1] = 0.0545345, [2] = 0.246076
64 val: [0] = 0.0263979, [1] = 0.0545682, [2] = 0.245996
65 train: [0] = 0.0263371, [1] = 0.0542868, [2] = 0.244036
65 val: [0] = 0.0263219, [1] = 0.0543333, [2] = 0.245843
66 train: [0] = 0.0262402, [1] = 0.0541325, [2] = 0.242885
66 val: [0] = 0.0261796, [1] = 0.0541339, [2] = 0.24361
67 train: [0] = 0.0260756, [1] = 0.0538957, [2] = 0.241141
67 val: [0] = 0.0259919, [1] = 0.0539193, [2] = 0.242733
68 train: [0] = 0.0259504, [1] = 0.0537201, [2] = 0.239747
68 val: [0] = 0.0259329, [1] = 0.0538657, [2] = 0.241094
69 train: [0] = 0.0258514, [1] = 0.0535734, [2] = 0.238833
69 val: [0] = 0.0258632, [1] = 0.0537842, [2] = 0.240734
70 train: [0] = 0.0257256, [1] = 0.0534133, [2] = 0.237455
70 val: [0] = 0.0257407, [1] = 0.0536617, [2] = 0.239816
71 train: [0] = 0.0256285, [1] = 0.0532547, [2] = 0.236407
71 val: [0] = 0.0255287, [1] = 0.0532639, [2] = 0.237229
72 train: [0] = 0.0254421, [1] = 0.0530225, [2] = 0.234418
72 val: [0] = 0.0254166, [1] = 0.0531263, [2] = 0.235463
73 train: [0] = 0.0253523, [1] = 0.052912, [2] = 0.233648
73 val: [0] = 0.0252894, [1] = 0.052937, [2] = 0.235265
74 train: [0] = 0.0252569, [1] = 0.0527662, [2] = 0.232688
74 val: [0] = 0.0253596, [1] = 0.0530995, [2] = 0.235314
75 train: [0] = 0.0251275, [1] = 0.0526062, [2] = 0.231357
75 val: [0] = 0.0251396, [1] = 0.0526692, [2] = 0.232671
76 train: [0] = 0.0250071, [1] = 0.0524375, [2] = 0.230141
76 val: [0] = 0.0249848, [1] = 0.052608, [2] = 0.232047
77 train: [0] = 0.0249198, [1] = 0.0523195, [2] = 0.229285
77 val: [0] = 0.0250699, [1] = 0.052761, [2] = 0.23266
78 train: [0] = 0.0248088, [1] = 0.0521687, [2] = 0.228153
78 val: [0] = 0.0247949, [1] = 0.0523493, [2] = 0.230346
79 train: [0] = 0.0247149, [1] = 0.052048, [2] = 0.227405
79 val: [0] = 0.0247178, [1] = 0.0521635, [2] = 0.228975
[run_0] training putter for step 1:
0 train: [0] = 0.09505, [1] = 0.4026, [2] = 0.0002639, [3] = 0.09516, [4] = 0.07598, [5] = 0.01326, [6] = 2.478
0 train: [0] = 86.3%, [1] = 86.0%
0 val: [0] = 0.08557, [1] = 0.006997, [2] = 0.0005855, [3] = 0.08595, [4] = 0.0696, [5] = 0.01317, [6] = 2.478
0 val: [0] = 100.0%, [1] = 86.0%
1 train: [0] = 0.07807, [1] = 0.04379, [2] = 0.0007834, [3] = 0.07915, [4] = 0.07332, [5] = 0.01326, [6] = 2.478
1 train: [0] = 99.4%, [1] = 86.0%
1 val: [0] = 0.06926, [1] = 0.003881, [2] = 0.0008757, [3] = 0.07037, [4] = 0.07278, [5] = 0.01316, [6] = 2.478
1 val: [0] = 100.0%, [1] = 86.0%
2 train: [0] = 0.06397, [1] = 0.003236, [2] = 0.0008175, [3] = 0.06669, [4] = 0.07185, [5] = 0.01326, [6] = 2.478
2 train: [0] = 100.0%, [1] = 86.0%
2 val: [0] = 0.05825, [1] = 0.002798, [2] = 0.001162, [3] = 0.0628, [4] = 0.07442, [5] = 0.01348, [6] = 2.478
2 val: [0] = 100.0%, [1] = 86.0%
3 train: [0] = 0.06666, [1] = 0.01342, [2] = 0.001208, [3] = 0.07051, [4] = 0.07502, [5] = 0.01326, [6] = 2.478
3 train: [0] = 99.8%, [1] = 86.0%
3 val: [0] = 0.05889, [1] = 0.002779, [2] = 0.001406, [3] = 0.06389, [4] = 0.0732, [5] = 0.0133, [6] = 2.478
3 val: [0] = 100.0%, [1] = 86.0%
4 train: [0] = 0.06548, [1] = 0.01216, [2] = 0.001183, [3] = 0.06885, [4] = 0.07375, [5] = 0.01326, [6] = 2.478
4 train: [0] = 99.9%, [1] = 86.0%
4 val: [0] = 0.05859, [1] = 0.002418, [2] = 0.001058, [3] = 0.06249, [4] = 0.06931, [5] = 0.01316, [6] = 2.478
4 val: [0] = 100.0%, [1] = 86.0%
5 train: [0] = 0.06558, [1] = 0.02513, [2] = 0.002019, [3] = 0.07031, [4] = 0.07463, [5] = 0.01326, [6] = 2.478
5 train: [0] = 99.7%, [1] = 86.0%
5 val: [0] = 0.06584, [1] = 0.002556, [2] = 0.002233, [3] = 0.07036, [4] = 0.06853, [5] = 0.01316, [6] = 2.478
5 val: [0] = 100.0%, [1] = 86.0%
6 train: [0] = 0.05974, [1] = 0.002515, [2] = 0.002088, [3] = 0.06525, [4] = 0.07391, [5] = 0.01336, [6] = 2.478
6 train: [0] = 100.0%, [1] = 86.0%
6 val: [0] = 0.05543, [1] = 0.002372, [2] = 0.002214, [3] = 0.06022, [4] = 0.07058, [5] = 0.01318, [6] = 2.478
6 val: [0] = 100.0%, [1] = 86.0%
7 train: [0] = 0.06372, [1] = 0.01039, [2] = 0.002378, [3] = 0.06916, [4] = 0.07316, [5] = 0.01326, [6] = 2.478
7 train: [0] = 99.9%, [1] = 86.0%
7 val: [0] = 0.06631, [1] = 0.002452, [2] = 0.001254, [3] = 0.06941, [4] = 0.07458, [5] = 0.01317, [6] = 2.478
7 val: [0] = 100.0%, [1] = 86.0%
8 train: [0] = 0.05882, [1] = 0.002276, [2] = 0.001766, [3] = 0.06435, [4] = 0.07722, [5] = 0.01328, [6] = 2.478
8 train: [0] = 100.0%, [1] = 86.0%
8 val: [0] = 0.05449, [1] = 0.002062, [2] = 0.002071, [3] = 0.06008, [4] = 0.07081, [5] = 0.01316, [6] = 2.478
8 val: [0] = 100.0%, [1] = 86.0%
9 train: [0] = 0.06205, [1] = 0.005747, [2] = 0.001917, [3] = 0.06696, [4] = 0.07515, [5] = 0.01328, [6] = 2.478
9 train: [0] = 100.0%, [1] = 86.0%
9 val: [0] = 0.06112, [1] = 0.002371, [2] = 0.001296, [3] = 0.06554, [4] = 0.07307, [5] = 0.01316, [6] = 2.478
9 val: [0] = 100.0%, [1] = 86.0%
10 train: [0] = 0.06659, [1] = 0.01401, [2] = 0.001709, [3] = 0.07072, [4] = 0.0753, [5] = 0.01328, [6] = 2.478
10 train: [0] = 99.9%, [1] = 86.0%
10 val: [0] = 0.06193, [1] = 0.002564, [2] = 0.001388, [3] = 0.06558, [4] = 0.07162, [5] = 0.01316, [6] = 2.478
10 val: [0] = 100.0%, [1] = 86.0%
11 train: [0] = 0.06309, [1] = 0.009347, [2] = 0.001847, [3] = 0.06779, [4] = 0.07597, [5] = 0.01326, [6] = 2.478
11 train: [0] = 99.9%, [1] = 86.0%
11 val: [0] = 0.06914, [1] = 0.002624, [2] = 0.001756, [3] = 0.07269, [4] = 0.07421, [5] = 0.01328, [6] = 2.478
11 val: [0] = 100.0%, [1] = 86.0%
12 train: [0] = 0.0602, [1] = 0.00284, [2] = 0.002352, [3] = 0.06541, [4] = 0.07387, [5] = 0.01326, [6] = 2.478
12 train: [0] = 100.0%, [1] = 86.0%
12 val: [0] = 0.05566, [1] = 0.002031, [2] = 0.002422, [3] = 0.06016, [4] = 0.07583, [5] = 0.01316, [6] = 2.478
12 val: [0] = 100.0%, [1] = 86.0%
13 train: [0] = 0.05464, [1] = 0.001966, [2] = 0.002226, [3] = 0.05943, [4] = 0.07491, [5] = 0.01328, [6] = 2.478
13 train: [0] = 100.0%, [1] = 86.0%
13 val: [0] = 0.05065, [1] = 0.002018, [2] = 0.002013, [3] = 0.0552, [4] = 0.07201, [5] = 0.01322, [6] = 2.478
13 val: [0] = 100.0%, [1] = 86.0%
14 train: [0] = 0.05681, [1] = 0.01973, [2] = 0.002121, [3] = 0.06171, [4] = 0.07545, [5] = 0.01326, [6] = 2.478
14 train: [0] = 99.9%, [1] = 86.0%
14 val: [0] = 0.05875, [1] = 0.01135, [2] = 0.001945, [3] = 0.06338, [4] = 0.07073, [5] = 0.01327, [6] = 2.478
14 val: [0] = 99.9%, [1] = 86.0%
15 train: [0] = 0.05804, [1] = 0.002683, [2] = 0.001891, [3] = 0.06274, [4] = 0.07457, [5] = 0.01333, [6] = 2.478
15 train: [0] = 100.0%, [1] = 86.0%
15 val: [0] = 0.05123, [1] = 0.001846, [2] = 0.001841, [3] = 0.05572, [4] = 0.07331, [5] = 0.01328, [6] = 2.478
15 val: [0] = 100.0%, [1] = 86.0%
16 train: [0] = 0.05133, [1] = 0.00242, [2] = 0.001952, [3] = 0.05641, [4] = 0.07555, [5] = 0.01326, [6] = 2.478
16 train: [0] = 100.0%, [1] = 86.0%
16 val: [0] = 0.06184, [1] = 0.002475, [2] = 0.002209, [3] = 0.06851, [4] = 0.07475, [5] = 0.01325, [6] = 2.478
16 val: [0] = 100.0%, [1] = 86.0%
17 train: [0] = 0.06073, [1] = 0.004982, [2] = 0.002584, [3] = 0.06751, [4] = 0.07375, [5] = 0.01326, [6] = 2.478
17 train: [0] = 100.0%, [1] = 86.0%
17 val: [0] = 0.07277, [1] = 0.4201, [2] = 0.004908, [3] = 0.08029, [4] = 0.07547, [5] = 0.01334, [6] = 2.478
17 val: [0] = 96.1%, [1] = 86.0%
18 train: [0] = 0.05742, [1] = 0.003533, [2] = 0.002637, [3] = 0.06249, [4] = 0.0752, [5] = 0.01326, [6] = 2.478
18 train: [0] = 100.0%, [1] = 86.0%
18 val: [0] = 0.05157, [1] = 0.001734, [2] = 0.001955, [3] = 0.05594, [4] = 0.07286, [5] = 0.01336, [6] = 2.478
18 val: [0] = 100.0%, [1] = 86.0%
19 train: [0] = 0.05141, [1] = 0.001872, [2] = 0.002046, [3] = 0.05656, [4] = 0.07467, [5] = 0.01326, [6] = 2.478
19 train: [0] = 100.0%, [1] = 86.0%
19 val: [0] = 0.04891, [1] = 0.001833, [2] = 0.001825, [3] = 0.05412, [4] = 0.07314, [5] = 0.01329, [6] = 2.478
19 val: [0] = 100.0%, [1] = 86.0%
[run_0] training getter for step 1:
0 train: [0] = 0.04711, [1] = 0.1118, [2] = 0.002032, [3] = 0.05249, [4] = 0.1114, [5] = 0.3675, [6] = 2.046
0 train: [0] = 97.5%, [1] = 71.0%
0 val: [0] = 0.04592, [1] = 0.000585, [2] = 0.001926, [3] = 0.05104, [4] = 0.1005, [5] = 0.1266, [6] = 2.48
0 val: [0] = 100.0%, [1] = 75.0%
1 train: [0] = 0.04729, [1] = 0.0001724, [2] = 0.002013, [3] = 0.0527, [4] = 0.1006, [5] = 0.09907, [6] = 2.872
1 train: [0] = 100.0%, [1] = 74.6%
1 val: [0] = 0.04619, [1] = 2.191e-05, [2] = 0.001932, [3] = 0.05139, [4] = 0.1087, [5] = 0.07376, [6] = 3.082
1 val: [0] = 100.0%, [1] = 77.0%
2 train: [0] = 0.04754, [1] = 6.245e-05, [2] = 0.002003, [3] = 0.05291, [4] = 0.09562, [5] = 0.06666, [6] = 3.232
2 train: [0] = 100.0%, [1] = 76.6%
2 val: [0] = 0.04644, [1] = 0.001119, [2] = 0.001854, [3] = 0.05163, [4] = 0.09111, [5] = 0.05401, [6] = 3.288
2 val: [0] = 100.0%, [1] = 78.0%
3 train: [0] = 0.04761, [1] = 6.856e-05, [2] = 0.002019, [3] = 0.05299, [4] = 0.09828, [5] = 0.05001, [6] = 3.644
3 train: [0] = 100.0%, [1] = 77.2%
3 val: [0] = 0.04674, [1] = 3.535e-07, [2] = 0.001793, [3] = 0.05171, [4] = 0.08678, [5] = 0.04252, [6] = 3.547
3 val: [0] = 100.0%, [1] = 79.0%
4 train: [0] = 0.0475, [1] = 0.0001466, [2] = 0.001982, [3] = 0.05291, [4] = 0.1015, [5] = 0.04053, [6] = 3.796
4 train: [0] = 100.0%, [1] = 77.2%
4 val: [0] = 0.04631, [1] = 4.185e-07, [2] = 0.001949, [3] = 0.05155, [4] = 0.1002, [5] = 0.03371, [6] = 3.529
4 val: [0] = 100.0%, [1] = 78.0%
5 train: [0] = 0.04764, [1] = 0.001184, [2] = 0.002004, [3] = 0.05306, [4] = 0.09296, [5] = 0.03578, [6] = 3.625
5 train: [0] = 100.0%, [1] = 77.4%
5 val: [0] = 0.04651, [1] = 9.96e-07, [2] = 0.001906, [3] = 0.05156, [4] = 0.08945, [5] = 0.03332, [6] = 3.524
5 val: [0] = 100.0%, [1] = 78.0%
6 train: [0] = 0.0477, [1] = 1.188e-06, [2] = 0.001981, [3] = 0.05305, [4] = 0.08878, [5] = 0.03098, [6] = 3.515
6 train: [0] = 100.0%, [1] = 77.5%
6 val: [0] = 0.04699, [1] = 9.465e-07, [2] = 0.001937, [3] = 0.05204, [4] = 0.08482, [5] = 0.03053, [6] = 3.606
6 val: [0] = 100.0%, [1] = 77.0%
7 train: [0] = 0.04764, [1] = 0.0001532, [2] = 0.00205, [3] = 0.05304, [4] = 0.09409, [5] = 0.02876, [6] = 3.957
7 train: [0] = 100.0%, [1] = 75.6%
7 val: [0] = 0.04625, [1] = 8.637e-09, [2] = 0.001861, [3] = 0.05138, [4] = 0.09065, [5] = 0.02671, [6] = 3.835
7 val: [0] = 100.0%, [1] = 74.0%
8 train: [0] = 0.04768, [1] = 7.9e-07, [2] = 0.00201, [3] = 0.05301, [4] = 0.09098, [5] = 0.02606, [6] = 3.806
8 train: [0] = 100.0%, [1] = 76.5%
8 val: [0] = 0.04644, [1] = 1.898e-10, [2] = 0.001863, [3] = 0.05149, [4] = 0.09139, [5] = 0.02578, [6] = 3.822
8 val: [0] = 100.0%, [1] = 78.0%
9 train: [0] = 0.04779, [1] = 0.002102, [2] = 0.002016, [3] = 0.0531, [4] = 0.09186, [5] = 0.02513, [6] = 3.992
9 train: [0] = 100.0%, [1] = 76.6%
9 val: [0] = 0.04649, [1] = 5.161e-09, [2] = 0.001935, [3] = 0.05167, [4] = 0.09503, [5] = 0.02507, [6] = 4.186
9 val: [0] = 100.0%, [1] = 75.0%
10 train: [0] = 0.04756, [1] = 3.924e-06, [2] = 0.001986, [3] = 0.05295, [4] = 0.09222, [5] = 0.02391, [6] = 4.007
10 train: [0] = 100.0%, [1] = 77.7%
10 val: [0] = 0.04652, [1] = 5.006e-07, [2] = 0.001873, [3] = 0.05166, [4] = 0.09201, [5] = 0.02238, [6] = 3.779
10 val: [0] = 100.0%, [1] = 81.0%
11 train: [0] = 0.04746, [1] = 0.000581, [2] = 0.002007, [3] = 0.05285, [4] = 0.09582, [5] = 0.02215, [6] = 3.879
11 train: [0] = 100.0%, [1] = 77.9%
11 val: [0] = 0.04698, [1] = 2.66e-07, [2] = 0.00192, [3] = 0.05205, [4] = 0.08691, [5] = 0.02178, [6] = 4.488
11 val: [0] = 100.0%, [1] = 75.0%
12 train: [0] = 0.0474, [1] = 5.693e-05, [2] = 0.001989, [3] = 0.05282, [4] = 0.09583, [5] = 0.02183, [6] = 4.409
12 train: [0] = 100.0%, [1] = 75.2%
12 val: [0] = 0.04635, [1] = 5.81e-05, [2] = 0.001929, [3] = 0.05155, [4] = 0.09069, [5] = 0.0209, [6] = 4.375
12 val: [0] = 100.0%, [1] = 78.0%
13 train: [0] = 0.04747, [1] = 5.808e-07, [2] = 0.001973, [3] = 0.05288, [4] = 0.09314, [5] = 0.01995, [6] = 4.164
13 train: [0] = 100.0%, [1] = 77.7%
13 val: [0] = 0.04617, [1] = 1.174e-07, [2] = 0.001882, [3] = 0.05136, [4] = 0.09831, [5] = 0.01803, [6] = 3.952
13 val: [0] = 100.0%, [1] = 80.0%
14 train: [0] = 0.04749, [1] = 8.18e-07, [2] = 0.001972, [3] = 0.05287, [4] = 0.09044, [5] = 0.01794, [6] = 4.162
14 train: [0] = 100.0%, [1] = 78.1%
14 val: [0] = 0.04658, [1] = 1.186e-11, [2] = 0.001961, [3] = 0.05179, [4] = 0.1, [5] = 0.0167, [6] = 4.829
14 val: [0] = 100.0%, [1] = 75.0%
15 train: [0] = 0.0474, [1] = 5.736e-06, [2] = 0.001993, [3] = 0.05279, [4] = 0.09278, [5] = 0.01798, [6] = 4.472
15 train: [0] = 100.0%, [1] = 76.6%
15 val: [0] = 0.0461, [1] = 0.001346, [2] = 0.001926, [3] = 0.05149, [4] = 0.1084, [5] = 0.01615, [6] = 4.252
15 val: [0] = 100.0%, [1] = 78.0%
16 train: [0] = 0.04778, [1] = 2.501e-05, [2] = 0.002018, [3] = 0.05315, [4] = 0.09319, [5] = 0.01668, [6] = 4.142
16 train: [0] = 100.0%, [1] = 78.3%
16 val: [0] = 0.0475, [1] = 0, [2] = 0.001866, [3] = 0.05245, [4] = 0.083, [5] = 0.01593, [6] = 4.648
16 val: [0] = 100.0%, [1] = 77.0%
17 train: [0] = 0.04755, [1] = 0.0002469, [2] = 0.002002, [3] = 0.05291, [4] = 0.0931, [5] = 0.01627, [6] = 4.298
17 train: [0] = 100.0%, [1] = 77.7%
17 val: [0] = 0.04672, [1] = 3.559e-11, [2] = 0.001945, [3] = 0.05188, [4] = 0.08895, [5] = 0.01363, [6] = 4.479
17 val: [0] = 100.0%, [1] = 78.0%
18 train: [0] = 0.04779, [1] = 2.908e-05, [2] = 0.001979, [3] = 0.05312, [4] = 0.08704, [5] = 0.01514, [6] = 4.307
18 train: [0] = 100.0%, [1] = 77.5%
18 val: [0] = 0.04615, [1] = 0, [2] = 0.001959, [3] = 0.05146, [4] = 0.0977, [5] = 0.01338, [6] = 3.974
18 val: [0] = 100.0%, [1] = 80.0%
19 train: [0] = 0.04714, [1] = 9.487e-07, [2] = 0.002021, [3] = 0.05261, [4] = 0.0965, [5] = 0.01385, [6] = 4.172
19 train: [0] = 100.0%, [1] = 79.1%
19 val: [0] = 0.04636, [1] = 4.835e-06, [2] = 0.001958, [3] = 0.05148, [4] = 0.0918, [5] = 0.01233, [6] = 3.985
19 val: [0] = 100.0%, [1] = 80.0%
[run_0] test accuracy = 78.7%
[run_0] training autoencoder for step 2:
0 train: [0] = 0.111822, [1] = 0.20047, [2] = 0.834336
0 val: [0] = 0.0835864, [1] = 0.133121, [2] = 0.791784
1 train: [0] = 0.0800661, [1] = 0.13273, [2] = 0.781581
1 val: [0] = 0.0742484, [1] = 0.123552, [2] = 0.719667
2 train: [0] = 0.0741798, [1] = 0.120528, [2] = 0.682979
2 val: [0] = 0.0702942, [1] = 0.116018, [2] = 0.656307
3 train: [0] = 0.0685917, [1] = 0.114267, [2] = 0.647394
3 val: [0] = 0.0646786, [1] = 0.108765, [2] = 0.613739
4 train: [0] = 0.064152, [1] = 0.10605, [2] = 0.591601
4 val: [0] = 0.0615806, [1] = 0.102144, [2] = 0.566146
5 train: [0] = 0.0623048, [1] = 0.101537, [2] = 0.561004
5 val: [0] = 0.0599308, [1] = 0.0985045, [2] = 0.544756
6 train: [0] = 0.0603631, [1] = 0.0984186, [2] = 0.541877
6 val: [0] = 0.0579444, [1] = 0.0957901, [2] = 0.526295
7 train: [0] = 0.0573631, [1] = 0.0943, [2] = 0.515709
7 val: [0] = 0.0548208, [1] = 0.0910467, [2] = 0.495318
8 train: [0] = 0.0554302, [1] = 0.0911107, [2] = 0.492137
8 val: [0] = 0.0537017, [1] = 0.0891677, [2] = 0.479876
9 train: [0] = 0.0541588, [1] = 0.0893467, [2] = 0.479932
9 val: [0] = 0.0523007, [1] = 0.0875056, [2] = 0.46946
10 train: [0] = 0.0529313, [1] = 0.0878616, [2] = 0.469369
10 val: [0] = 0.0508872, [1] = 0.0857665, [2] = 0.457627
11 train: [0] = 0.0512592, [1] = 0.0856978, [2] = 0.454585
11 val: [0] = 0.0490575, [1] = 0.0831453, [2] = 0.439746
12 train: [0] = 0.049219, [1] = 0.0829291, [2] = 0.43652
12 val: [0] = 0.0473058, [1] = 0.0807915, [2] = 0.423291
13 train: [0] = 0.0476021, [1] = 0.0808268, [2] = 0.422817
13 val: [0] = 0.0458572, [1] = 0.0789927, [2] = 0.410543
14 train: [0] = 0.0461748, [1] = 0.0791278, [2] = 0.411396
14 val: [0] = 0.0445165, [1] = 0.0773663, [2] = 0.40083
15 train: [0] = 0.0446562, [1] = 0.0774132, [2] = 0.400933
15 val: [0] = 0.0430626, [1] = 0.0759051, [2] = 0.391605
16 train: [0] = 0.0431641, [1] = 0.0756999, [2] = 0.390176
16 val: [0] = 0.0415518, [1] = 0.0740604, [2] = 0.379746
17 train: [0] = 0.041661, [1] = 0.0740466, [2] = 0.378181
17 val: [0] = 0.0404107, [1] = 0.0730198, [2] = 0.370043
18 train: [0] = 0.0405316, [1] = 0.072731, [2] = 0.367776
18 val: [0] = 0.03921, [1] = 0.0713805, [2] = 0.35932
19 train: [0] = 0.0397418, [1] = 0.0716852, [2] = 0.359583
19 val: [0] = 0.0388178, [1] = 0.070629, [2] = 0.353723
20 train: [0] = 0.0391127, [1] = 0.0707868, [2] = 0.353157
20 val: [0] = 0.0381973, [1] = 0.0698284, [2] = 0.347102
21 train: [0] = 0.0385221, [1] = 0.0699951, [2] = 0.347276
21 val: [0] = 0.0375974, [1] = 0.0691366, [2] = 0.342193
22 train: [0] = 0.0378176, [1] = 0.0691506, [2] = 0.340813
22 val: [0] = 0.0370813, [1] = 0.0684, [2] = 0.336731
23 train: [0] = 0.0373439, [1] = 0.0684968, [2] = 0.336444
23 val: [0] = 0.0364845, [1] = 0.0676101, [2] = 0.331569
24 train: [0] = 0.0367799, [1] = 0.0677996, [2] = 0.331006
24 val: [0] = 0.0360598, [1] = 0.0670543, [2] = 0.325543
25 train: [0] = 0.0362567, [1] = 0.0671821, [2] = 0.325818
25 val: [0] = 0.0355848, [1] = 0.0665488, [2] = 0.322245
26 train: [0] = 0.0358221, [1] = 0.0666482, [2] = 0.321935
26 val: [0] = 0.0351373, [1] = 0.0658767, [2] = 0.318466
27 train: [0] = 0.0353873, [1] = 0.0661141, [2] = 0.317921
27 val: [0] = 0.0346331, [1] = 0.0653416, [2] = 0.313245
28 train: [0] = 0.034869, [1] = 0.0654911, [2] = 0.313009
28 val: [0] = 0.0342864, [1] = 0.06502, [2] = 0.310379
29 train: [0] = 0.0344238, [1] = 0.0648824, [2] = 0.308957
29 val: [0] = 0.0335754, [1] = 0.0640137, [2] = 0.303567
30 train: [0] = 0.0338815, [1] = 0.064134, [2] = 0.303463
30 val: [0] = 0.0332197, [1] = 0.0634264, [2] = 0.299049
31 train: [0] = 0.0333347, [1] = 0.063382, [2] = 0.298052
31 val: [0] = 0.0326891, [1] = 0.0626634, [2] = 0.293637
32 train: [0] = 0.0328714, [1] = 0.0626813, [2] = 0.293289
32 val: [0] = 0.0324045, [1] = 0.0620948, [2] = 0.290148
33 train: [0] = 0.0324158, [1] = 0.0620446, [2] = 0.28878
33 val: [0] = 0.0318262, [1] = 0.0615301, [2] = 0.285753
34 train: [0] = 0.0320135, [1] = 0.0614674, [2] = 0.284903
34 val: [0] = 0.0315112, [1] = 0.0610864, [2] = 0.282454
35 train: [0] = 0.0315947, [1] = 0.0609085, [2] = 0.281249
35 val: [0] = 0.0310409, [1] = 0.0604709, [2] = 0.278415
36 train: [0] = 0.0311681, [1] = 0.0603515, [2] = 0.277578
36 val: [0] = 0.0307436, [1] = 0.0599946, [2] = 0.275577
37 train: [0] = 0.0308084, [1] = 0.0598876, [2] = 0.274855
37 val: [0] = 0.0303386, [1] = 0.059526, [2] = 0.272433
38 train: [0] = 0.0304079, [1] = 0.0593849, [2] = 0.271659
38 val: [0] = 0.0299654, [1] = 0.0590018, [2] = 0.27062
39 train: [0] = 0.0299951, [1] = 0.058836, [2] = 0.26854
39 val: [0] = 0.0296546, [1] = 0.0584689, [2] = 0.267577
40 train: [0] = 0.0296398, [1] = 0.0583597, [2] = 0.26598
40 val: [0] = 0.0291566, [1] = 0.057891, [2] = 0.263629
41 train: [0] = 0.0292597, [1] = 0.0578631, [2] = 0.263053
41 val: [0] = 0.028855, [1] = 0.0576011, [2] = 0.263178
42 train: [0] = 0.0289661, [1] = 0.0574524, [2] = 0.260975
42 val: [0] = 0.0284569, [1] = 0.0570492, [2] = 0.258946
43 train: [0] = 0.0285869, [1] = 0.0570135, [2] = 0.258329
43 val: [0] = 0.0284233, [1] = 0.0567792, [2] = 0.260076
44 train: [0] = 0.0282658, [1] = 0.0565692, [2] = 0.255727
44 val: [0] = 0.0280156, [1] = 0.0564926, [2] = 0.254974
45 train: [0] = 0.0279238, [1] = 0.0561391, [2] = 0.252998
45 val: [0] = 0.0276073, [1] = 0.0560208, [2] = 0.252789
46 train: [0] = 0.0276066, [1] = 0.0557142, [2] = 0.250385
46 val: [0] = 0.0274688, [1] = 0.0557917, [2] = 0.250507
47 train: [0] = 0.0272577, [1] = 0.0552455, [2] = 0.247277
47 val: [0] = 0.026955, [1] = 0.05523, [2] = 0.24718
48 train: [0] = 0.0269542, [1] = 0.0548474, [2] = 0.244724
48 val: [0] = 0.0266888, [1] = 0.0547224, [2] = 0.244461
49 train: [0] = 0.0266469, [1] = 0.054409, [2] = 0.241791
49 val: [0] = 0.0264516, [1] = 0.0543214, [2] = 0.241617
50 train: [0] = 0.0263513, [1] = 0.0540084, [2] = 0.239039
50 val: [0] = 0.0261592, [1] = 0.0539985, [2] = 0.239357
51 train: [0] = 0.0261094, [1] = 0.0536555, [2] = 0.236641
51 val: [0] = 0.0259776, [1] = 0.053675, [2] = 0.237788
52 train: [0] = 0.0258045, [1] = 0.0532422, [2] = 0.233893
52 val: [0] = 0.0254477, [1] = 0.0529532, [2] = 0.232476
53 train: [0] = 0.0255227, [1] = 0.0528365, [2] = 0.231074
53 val: [0] = 0.0252229, [1] = 0.0527075, [2] = 0.230458
54 train: [0] = 0.0252838, [1] = 0.0524864, [2] = 0.228808
54 val: [0] = 0.0250906, [1] = 0.0523001, [2] = 0.228139
55 train: [0] = 0.0250352, [1] = 0.0521175, [2] = 0.226232
55 val: [0] = 0.0247719, [1] = 0.0520343, [2] = 0.226098
56 train: [0] = 0.0247734, [1] = 0.0517673, [2] = 0.223817
56 val: [0] = 0.024558, [1] = 0.0516791, [2] = 0.223552
57 train: [0] = 0.0245598, [1] = 0.0514582, [2] = 0.221667
57 val: [0] = 0.0243451, [1] = 0.0514398, [2] = 0.222316
58 train: [0] = 0.0243412, [1] = 0.0511503, [2] = 0.219792
58 val: [0] = 0.0241082, [1] = 0.0511676, [2] = 0.220332
59 train: [0] = 0.0241717, [1] = 0.0508992, [2] = 0.218096
59 val: [0] = 0.0240697, [1] = 0.0510649, [2] = 0.219841
60 train: [0] = 0.0239677, [1] = 0.0505969, [2] = 0.215802
60 val: [0] = 0.023743, [1] = 0.0504753, [2] = 0.215759
61 train: [0] = 0.023755, [1] = 0.0502832, [2] = 0.213693
61 val: [0] = 0.0236219, [1] = 0.0502678, [2] = 0.215807
62 train: [0] = 0.0236099, [1] = 0.0500675, [2] = 0.212203
62 val: [0] = 0.0234723, [1] = 0.0501651, [2] = 0.213041
63 train: [0] = 0.0234269, [1] = 0.0497863, [2] = 0.210147
63 val: [0] = 0.0235024, [1] = 0.0500702, [2] = 0.212054
64 train: [0] = 0.0233066, [1] = 0.0495994, [2] = 0.208995
64 val: [0] = 0.0231456, [1] = 0.0495923, [2] = 0.2096
65 train: [0] = 0.0231577, [1] = 0.0493864, [2] = 0.207425
65 val: [0] = 0.023163, [1] = 0.0496454, [2] = 0.208982
66 train: [0] = 0.0230173, [1] = 0.0491592, [2] = 0.205844
66 val: [0] = 0.0228982, [1] = 0.0492459, [2] = 0.207358
67 train: [0] = 0.0229004, [1] = 0.0489839, [2] = 0.204693
67 val: [0] = 0.0228753, [1] = 0.0491561, [2] = 0.206783
68 train: [0] = 0.0227625, [1] = 0.0487731, [2] = 0.203275
68 val: [0] = 0.0228239, [1] = 0.0491106, [2] = 0.205887
69 train: [0] = 0.0226468, [1] = 0.0485966, [2] = 0.202063
69 val: [0] = 0.0225395, [1] = 0.0487305, [2] = 0.204338
70 train: [0] = 0.0224839, [1] = 0.0484355, [2] = 0.20102
70 val: [0] = 0.0225315, [1] = 0.0486772, [2] = 0.203041
71 train: [0] = 0.0223466, [1] = 0.0482271, [2] = 0.199556
71 val: [0] = 0.0222162, [1] = 0.0482123, [2] = 0.201628
72 train: [0] = 0.0222532, [1] = 0.0480905, [2] = 0.198636
72 val: [0] = 0.0221464, [1] = 0.0482152, [2] = 0.200811
73 train: [0] = 0.0221202, [1] = 0.047885, [2] = 0.19729
73 val: [0] = 0.0221959, [1] = 0.0482716, [2] = 0.200061
74 train: [0] = 0.0220225, [1] = 0.0477443, [2] = 0.19639
74 val: [0] = 0.0219999, [1] = 0.048011, [2] = 0.200249
75 train: [0] = 0.0219182, [1] = 0.0475779, [2] = 0.195351
75 val: [0] = 0.0219737, [1] = 0.04779, [2] = 0.198741
76 train: [0] = 0.0218034, [1] = 0.0473951, [2] = 0.194037
76 val: [0] = 0.0218152, [1] = 0.0476701, [2] = 0.196396
77 train: [0] = 0.0217283, [1] = 0.0472936, [2] = 0.19339
77 val: [0] = 0.0216918, [1] = 0.0474304, [2] = 0.194926
78 train: [0] = 0.0216147, [1] = 0.0471098, [2] = 0.192202
78 val: [0] = 0.021754, [1] = 0.0475584, [2] = 0.196149
79 train: [0] = 0.0215197, [1] = 0.0469761, [2] = 0.191283
79 val: [0] = 0.0216133, [1] = 0.047283, [2] = 0.193912
[run_0] training putter for step 2:
0 train: [0] = 0.09195, [1] = 2.288, [2] = 0.0001164, [3] = 0.09183, [4] = 0.08706, [5] = 0.01324, [6] = 5.164
0 train: [0] = 88.6%, [1] = 73.0%
0 val: [0] = 0.08604, [1] = 0.01008, [2] = 0.0001649, [3] = 0.08615, [4] = 0.09154, [5] = 0.01235, [6] = 5.164
0 val: [0] = 100.0%, [1] = 73.0%
1 train: [0] = 0.08188, [1] = 0.03015, [2] = 0.0004265, [3] = 0.08243, [4] = 0.08688, [5] = 0.01324, [6] = 5.164
1 train: [0] = 99.5%, [1] = 73.0%
1 val: [0] = 0.08322, [1] = 0.001252, [2] = 0.000219, [3] = 0.08375, [4] = 0.08923, [5] = 0.01231, [6] = 5.164
1 val: [0] = 100.0%, [1] = 73.0%
2 train: [0] = 0.07598, [1] = 0.0007874, [2] = 0.000632, [3] = 0.07692, [4] = 0.08898, [5] = 0.0132, [6] = 5.164
2 train: [0] = 100.0%, [1] = 73.0%
2 val: [0] = 0.06412, [1] = 4.555e-05, [2] = 0.0009592, [3] = 0.06572, [4] = 0.08787, [5] = 0.01233, [6] = 5.164
2 val: [0] = 100.0%, [1] = 73.0%
3 train: [0] = 0.06741, [1] = 0.06407, [2] = 0.0009621, [3] = 0.06899, [4] = 0.08598, [5] = 0.01321, [6] = 5.164
3 train: [0] = 99.6%, [1] = 73.0%
3 val: [0] = 0.06098, [1] = 3.595e-09, [2] = 0.001027, [3] = 0.0619, [4] = 0.08395, [5] = 0.01251, [6] = 5.164
3 val: [0] = 100.0%, [1] = 73.0%
4 train: [0] = 0.05649, [1] = 0.03406, [2] = 0.0009214, [3] = 0.05872, [4] = 0.08965, [5] = 0.01321, [6] = 5.164
4 train: [0] = 99.8%, [1] = 73.0%
4 val: [0] = 0.07125, [1] = 6.134e-09, [2] = 0.001641, [3] = 0.07296, [4] = 0.08589, [5] = 0.01231, [6] = 5.164
4 val: [0] = 100.0%, [1] = 73.0%
5 train: [0] = 0.05779, [1] = 0.001484, [2] = 0.001512, [3] = 0.06066, [4] = 0.08695, [5] = 0.0132, [6] = 5.164
5 train: [0] = 100.0%, [1] = 73.0%
5 val: [0] = 0.05127, [1] = 2.52e-05, [2] = 0.001486, [3] = 0.05535, [4] = 0.08678, [5] = 0.01242, [6] = 5.164
5 val: [0] = 100.0%, [1] = 73.0%
6 train: [0] = 0.06322, [1] = 0.02794, [2] = 0.001405, [3] = 0.06557, [4] = 0.08722, [5] = 0.0132, [6] = 5.164
6 train: [0] = 99.8%, [1] = 73.0%
6 val: [0] = 0.06058, [1] = 0, [2] = 0.0012, [3] = 0.06251, [4] = 0.09331, [5] = 0.01231, [6] = 5.164
6 val: [0] = 100.0%, [1] = 73.0%
7 train: [0] = 0.05625, [1] = 2.567e-08, [2] = 0.001165, [3] = 0.05922, [4] = 0.08733, [5] = 0.01321, [6] = 5.164
7 train: [0] = 100.0%, [1] = 73.0%
7 val: [0] = 0.05048, [1] = 2.515e-09, [2] = 0.001223, [3] = 0.05471, [4] = 0.09453, [5] = 0.01261, [6] = 5.164
7 val: [0] = 100.0%, [1] = 73.0%
8 train: [0] = 0.05435, [1] = 0.001885, [2] = 0.001443, [3] = 0.05868, [4] = 0.08822, [5] = 0.0132, [6] = 5.164
8 train: [0] = 100.0%, [1] = 73.0%
8 val: [0] = 0.05023, [1] = 0, [2] = 0.001622, [3] = 0.05498, [4] = 0.08921, [5] = 0.01231, [6] = 5.164
8 val: [0] = 100.0%, [1] = 73.0%
9 train: [0] = 0.05481, [1] = 0.004745, [2] = 0.001721, [3] = 0.05876, [4] = 0.08538, [5] = 0.01324, [6] = 5.164
9 train: [0] = 99.9%, [1] = 73.0%
9 val: [0] = 0.05857, [1] = 0, [2] = 0.001892, [3] = 0.0628, [4] = 0.09027, [5] = 0.01232, [6] = 5.164
9 val: [0] = 100.0%, [1] = 73.0%
10 train: [0] = 0.05438, [1] = 0.001455, [2] = 0.001461, [3] = 0.05888, [4] = 0.08765, [5] = 0.0132, [6] = 5.164
10 train: [0] = 100.0%, [1] = 73.0%
10 val: [0] = 0.04834, [1] = 9.491e-11, [2] = 0.001615, [3] = 0.05291, [4] = 0.0899, [5] = 0.01235, [6] = 5.164
10 val: [0] = 100.0%, [1] = 73.0%
11 train: [0] = 0.04699, [1] = 1.33e-05, [2] = 0.001304, [3] = 0.05088, [4] = 0.08708, [5] = 0.01325, [6] = 5.164
11 train: [0] = 100.0%, [1] = 73.0%
11 val: [0] = 0.04491, [1] = 0.001826, [2] = 0.001225, [3] = 0.04932, [4] = 0.08647, [5] = 0.01231, [6] = 5.164
11 val: [0] = 99.9%, [1] = 73.0%
12 train: [0] = 0.04944, [1] = 0.01543, [2] = 0.001346, [3] = 0.0534, [4] = 0.08631, [5] = 0.01323, [6] = 5.164
12 train: [0] = 99.9%, [1] = 73.0%
12 val: [0] = 0.07479, [1] = 2.622e-09, [2] = 0.001285, [3] = 0.07625, [4] = 0.08934, [5] = 0.01246, [6] = 5.164
12 val: [0] = 100.0%, [1] = 73.0%
13 train: [0] = 0.06462, [1] = 0.003721, [2] = 0.001575, [3] = 0.06754, [4] = 0.08709, [5] = 0.0132, [6] = 5.164
13 train: [0] = 100.0%, [1] = 73.0%
13 val: [0] = 0.06634, [1] = 0, [2] = 0.00135, [3] = 0.06847, [4] = 0.08788, [5] = 0.0124, [6] = 5.164
13 val: [0] = 100.0%, [1] = 73.0%
14 train: [0] = 0.05788, [1] = 0.0002032, [2] = 0.00149, [3] = 0.06147, [4] = 0.08747, [5] = 0.0132, [6] = 5.164
14 train: [0] = 100.0%, [1] = 73.0%
14 val: [0] = 0.05023, [1] = 0.001602, [2] = 0.001465, [3] = 0.05357, [4] = 0.08953, [5] = 0.0125, [6] = 5.164
14 val: [0] = 100.0%, [1] = 73.0%
15 train: [0] = 0.05475, [1] = 0.002284, [2] = 0.001312, [3] = 0.05815, [4] = 0.08471, [5] = 0.01321, [6] = 5.164
15 train: [0] = 100.0%, [1] = 73.0%
15 val: [0] = 0.04708, [1] = 0, [2] = 0.001032, [3] = 0.04965, [4] = 0.09077, [5] = 0.01243, [6] = 5.164
15 val: [0] = 100.0%, [1] = 73.0%
16 train: [0] = 0.05601, [1] = 0.002518, [2] = 0.001207, [3] = 0.05928, [4] = 0.08649, [5] = 0.01323, [6] = 5.164
16 train: [0] = 100.0%, [1] = 73.0%
16 val: [0] = 0.05655, [1] = 0, [2] = 0.001195, [3] = 0.06012, [4] = 0.08936, [5] = 0.01271, [6] = 5.164
16 val: [0] = 100.0%, [1] = 73.0%
17 train: [0] = 0.05324, [1] = 0.0003474, [2] = 0.001503, [3] = 0.05738, [4] = 0.08606, [5] = 0.01324, [6] = 5.164
17 train: [0] = 100.0%, [1] = 73.0%
17 val: [0] = 0.04842, [1] = 1.279e-06, [2] = 0.00133, [3] = 0.05117, [4] = 0.08897, [5] = 0.01252, [6] = 5.164
17 val: [0] = 100.0%, [1] = 73.0%
18 train: [0] = 0.05433, [1] = 0.00232, [2] = 0.00138, [3] = 0.05799, [4] = 0.08603, [5] = 0.0132, [6] = 5.164
18 train: [0] = 100.0%, [1] = 73.0%
18 val: [0] = 0.04915, [1] = 0.0007432, [2] = 0.001534, [3] = 0.05394, [4] = 0.08675, [5] = 0.01231, [6] = 5.164
18 val: [0] = 100.0%, [1] = 73.0%
19 train: [0] = 0.06263, [1] = 0.01111, [2] = 0.001557, [3] = 0.06625, [4] = 0.08635, [5] = 0.0132, [6] = 5.164
19 train: [0] = 99.9%, [1] = 73.0%
19 val: [0] = 0.06706, [1] = 0, [2] = 0.001082, [3] = 0.06943, [4] = 0.08921, [5] = 0.01231, [6] = 5.164
19 val: [0] = 100.0%, [1] = 73.0%
[run_0] training getter for step 2:
0 train: [0] = 0.06436, [1] = 0.07675, [2] = 0.001124, [3] = 0.06669, [4] = 0.08631, [5] = 0.3869, [6] = 1.979
0 train: [0] = 99.0%, [1] = 62.7%
0 val: [0] = 0.06329, [1] = 6.027e-07, [2] = 0.001069, [3] = 0.06551, [4] = 0.08529, [5] = 0.1215, [6] = 3.062
0 val: [0] = 100.0%, [1] = 64.0%
1 train: [0] = 0.06465, [1] = 4.329e-07, [2] = 0.001113, [3] = 0.06706, [4] = 0.08866, [5] = 0.0949, [6] = 3.682
1 train: [0] = 100.0%, [1] = 65.5%
1 val: [0] = 0.06334, [1] = 3.322e-10, [2] = 0.001078, [3] = 0.06563, [4] = 0.08876, [5] = 0.071, [6] = 4.602
1 val: [0] = 100.0%, [1] = 64.0%
2 train: [0] = 0.06469, [1] = 2.24e-08, [2] = 0.001119, [3] = 0.06709, [4] = 0.08955, [5] = 0.06318, [6] = 4.938
2 train: [0] = 100.0%, [1] = 64.8%
2 val: [0] = 0.06344, [1] = 5.932e-11, [2] = 0.001082, [3] = 0.0656, [4] = 0.09568, [5] = 0.05249, [6] = 5.557
2 val: [0] = 100.0%, [1] = 62.0%
3 train: [0] = 0.06479, [1] = 3.168e-10, [2] = 0.001113, [3] = 0.06721, [4] = 0.09256, [5] = 0.04891, [6] = 5.858
3 train: [0] = 100.0%, [1] = 63.7%
3 val: [0] = 0.06369, [1] = 0, [2] = 0.001083, [3] = 0.06596, [4] = 0.08739, [5] = 0.04285, [6] = 5.891
3 val: [0] = 100.0%, [1] = 65.0%
4 train: [0] = 0.06472, [1] = 5.771e-09, [2] = 0.001094, [3] = 0.06719, [4] = 0.09608, [5] = 0.04004, [6] = 6.132
4 train: [0] = 100.0%, [1] = 65.2%
4 val: [0] = 0.06399, [1] = 0, [2] = 0.001108, [3] = 0.06632, [4] = 0.0816, [5] = 0.03671, [6] = 5.496
4 val: [0] = 100.0%, [1] = 67.0%
5 train: [0] = 0.06464, [1] = 0, [2] = 0.001114, [3] = 0.06711, [4] = 0.09633, [5] = 0.03399, [6] = 6.269
5 train: [0] = 100.0%, [1] = 65.8%
5 val: [0] = 0.06323, [1] = 0, [2] = 0.001084, [3] = 0.06556, [4] = 0.09694, [5] = 0.02757, [6] = 6.081
5 val: [0] = 100.0%, [1] = 66.0%
6 train: [0] = 0.06485, [1] = 3.597e-10, [2] = 0.001119, [3] = 0.0673, [4] = 0.09274, [5] = 0.02916, [6] = 6.552
6 train: [0] = 100.0%, [1] = 65.4%
6 val: [0] = 0.06369, [1] = 0, [2] = 0.001065, [3] = 0.06602, [4] = 0.1053, [5] = 0.02673, [6] = 6.44
6 val: [0] = 100.0%, [1] = 66.0%
7 train: [0] = 0.06469, [1] = 0, [2] = 0.001122, [3] = 0.06712, [4] = 0.09667, [5] = 0.02523, [6] = 6.798
7 train: [0] = 100.0%, [1] = 65.7%
7 val: [0] = 0.06316, [1] = 0, [2] = 0.00107, [3] = 0.06541, [4] = 0.1023, [5] = 0.0238, [6] = 6.765
7 val: [0] = 100.0%, [1] = 65.0%
8 train: [0] = 0.06478, [1] = 0, [2] = 0.001109, [3] = 0.06717, [4] = 0.09558, [5] = 0.02259, [6] = 6.46
8 train: [0] = 100.0%, [1] = 66.8%
8 val: [0] = 0.06313, [1] = 0, [2] = 0.001102, [3] = 0.06549, [4] = 0.09835, [5] = 0.02058, [6] = 6.648
8 val: [0] = 100.0%, [1] = 66.0%
9 train: [0] = 0.06479, [1] = 2.239e-10, [2] = 0.001107, [3] = 0.06724, [4] = 0.09658, [5] = 0.02125, [6] = 7.024
9 train: [0] = 100.0%, [1] = 65.4%
9 val: [0] = 0.06286, [1] = 0, [2] = 0.001058, [3] = 0.06513, [4] = 0.118, [5] = 0.02089, [6] = 7.108
9 val: [0] = 100.0%, [1] = 66.0%
10 train: [0] = 0.06501, [1] = 3.811e-11, [2] = 0.001112, [3] = 0.06742, [4] = 0.09181, [5] = 0.02056, [6] = 7.161
10 train: [0] = 100.0%, [1] = 64.1%
10 val: [0] = 0.06412, [1] = 0, [2] = 0.001056, [3] = 0.06634, [4] = 0.08012, [5] = 0.01897, [6] = 7.217
10 val: [0] = 100.0%, [1] = 63.0%
11 train: [0] = 0.06506, [1] = 0, [2] = 0.001109, [3] = 0.06753, [4] = 0.09579, [5] = 0.01868, [6] = 6.644
11 train: [0] = 100.0%, [1] = 67.2%
11 val: [0] = 0.06351, [1] = 0, [2] = 0.001073, [3] = 0.06593, [4] = 0.08897, [5] = 0.01793, [6] = 6.355
11 val: [0] = 100.0%, [1] = 71.0%
12 train: [0] = 0.06469, [1] = 0, [2] = 0.001119, [3] = 0.06724, [4] = 0.09825, [5] = 0.0171, [6] = 6.664
12 train: [0] = 100.0%, [1] = 67.9%
12 val: [0] = 0.06308, [1] = 0, [2] = 0.001102, [3] = 0.06537, [4] = 0.09843, [5] = 0.01756, [6] = 6.569
12 val: [0] = 100.0%, [1] = 68.0%
13 train: [0] = 0.06485, [1] = 1.667e-11, [2] = 0.001123, [3] = 0.0673, [4] = 0.09797, [5] = 0.01637, [6] = 6.657
13 train: [0] = 100.0%, [1] = 67.9%
13 val: [0] = 0.06339, [1] = 0, [2] = 0.001092, [3] = 0.06564, [4] = 0.08555, [5] = 0.01394, [6] = 6.028
13 val: [0] = 100.0%, [1] = 70.0%
14 train: [0] = 0.06506, [1] = 0, [2] = 0.001118, [3] = 0.06742, [4] = 0.09305, [5] = 0.01543, [6] = 6.411
14 train: [0] = 100.0%, [1] = 68.4%
14 val: [0] = 0.0635, [1] = 0, [2] = 0.00109, [3] = 0.06574, [4] = 0.09339, [5] = 0.01382, [6] = 5.91
14 val: [0] = 100.0%, [1] = 70.0%
15 train: [0] = 0.06508, [1] = 3.335e-11, [2] = 0.001108, [3] = 0.06742, [4] = 0.0975, [5] = 0.01504, [6] = 6.086
15 train: [0] = 100.0%, [1] = 70.1%
15 val: [0] = 0.06429, [1] = 0, [2] = 0.001079, [3] = 0.06646, [4] = 0.08541, [5] = 0.01625, [6] = 6.482
15 val: [0] = 100.0%, [1] = 70.0%
16 train: [0] = 0.06517, [1] = 0, [2] = 0.0011, [3] = 0.06753, [4] = 0.08874, [5] = 0.0146, [6] = 6.372
16 train: [0] = 100.0%, [1] = 69.2%
16 val: [0] = 0.06405, [1] = 0, [2] = 0.001061, [3] = 0.06621, [4] = 0.0894, [5] = 0.01321, [6] = 6.084
16 val: [0] = 100.0%, [1] = 71.0%
17 train: [0] = 0.06489, [1] = 7.445e-08, [2] = 0.001125, [3] = 0.06722, [4] = 0.09786, [5] = 0.01354, [6] = 6.555
17 train: [0] = 100.0%, [1] = 68.5%
17 val: [0] = 0.06314, [1] = 0, [2] = 0.001074, [3] = 0.06541, [4] = 0.1044, [5] = 0.01203, [6] = 6.11
17 val: [0] = 100.0%, [1] = 67.0%
18 train: [0] = 0.06474, [1] = 2.382e-12, [2] = 0.00111, [3] = 0.06715, [4] = 0.09587, [5] = 0.01274, [6] = 6.229
18 train: [0] = 100.0%, [1] = 70.6%
18 val: [0] = 0.06347, [1] = 0, [2] = 0.001061, [3] = 0.06582, [4] = 0.09225, [5] = 0.01236, [6] = 6.515
18 val: [0] = 100.0%, [1] = 70.0%
19 train: [0] = 0.06504, [1] = 0, [2] = 0.001114, [3] = 0.06749, [4] = 0.08882, [5] = 0.01278, [6] = 6.649
19 train: [0] = 100.0%, [1] = 68.8%
19 val: [0] = 0.06341, [1] = 0, [2] = 0.001054, [3] = 0.06581, [4] = 0.09596, [5] = 0.01162, [6] = 6.933
19 val: [0] = 100.0%, [1] = 68.0%
[run_0] test accuracy = 68.7%
[run_0] training autoencoder for step 3:
0 train: [0] = 0.115788, [1] = 0.211706, [2] = 0.84156
0 val: [0] = 0.0827638, [1] = 0.133291, [2] = 0.792691
1 train: [0] = 0.0825244, [1] = 0.133985, [2] = 0.796282
1 val: [0] = 0.0799323, [1] = 0.133525, [2] = 0.78784
2 train: [0] = 0.0764192, [1] = 0.127811, [2] = 0.731993
2 val: [0] = 0.0747408, [1] = 0.119704, [2] = 0.679671
3 train: [0] = 0.0700713, [1] = 0.11677, [2] = 0.652906
3 val: [0] = 0.064121, [1] = 0.107378, [2] = 0.596193
4 train: [0] = 0.0637641, [1] = 0.105113, [2] = 0.578853
4 val: [0] = 0.0617091, [1] = 0.102461, [2] = 0.560174
5 train: [0] = 0.0618793, [1] = 0.101556, [2] = 0.555482
5 val: [0] = 0.0597644, [1] = 0.0989023, [2] = 0.541999
6 train: [0] = 0.0601077, [1] = 0.0986746, [2] = 0.538376
6 val: [0] = 0.0579973, [1] = 0.0964328, [2] = 0.525377
7 train: [0] = 0.0570875, [1] = 0.0950774, [2] = 0.516153
7 val: [0] = 0.0545883, [1] = 0.0921021, [2] = 0.498122
8 train: [0] = 0.0547533, [1] = 0.0916452, [2] = 0.493081
8 val: [0] = 0.0528018, [1] = 0.0892994, [2] = 0.480849
9 train: [0] = 0.0533118, [1] = 0.0896135, [2] = 0.479258
9 val: [0] = 0.0512087, [1] = 0.0875226, [2] = 0.468215
10 train: [0] = 0.0513681, [1] = 0.087103, [2] = 0.461834
10 val: [0] = 0.0490634, [1] = 0.0841389, [2] = 0.445444
11 train: [0] = 0.0496218, [1] = 0.0845991, [2] = 0.444016
11 val: [0] = 0.0474923, [1] = 0.0819522, [2] = 0.429765
12 train: [0] = 0.047735, [1] = 0.0822894, [2] = 0.428471
12 val: [0] = 0.0454937, [1] = 0.0796496, [2] = 0.415574
13 train: [0] = 0.0453818, [1] = 0.0795117, [2] = 0.411787
13 val: [0] = 0.0432808, [1] = 0.0767125, [2] = 0.395513
14 train: [0] = 0.0431734, [1] = 0.0765159, [2] = 0.39223
14 val: [0] = 0.0416498, [1] = 0.0746852, [2] = 0.37937
15 train: [0] = 0.04156, [1] = 0.0744071, [2] = 0.37677
15 val: [0] = 0.0402337, [1] = 0.0728846, [2] = 0.366265
16 train: [0] = 0.0404676, [1] = 0.0729705, [2] = 0.366665
16 val: [0] = 0.0394936, [1] = 0.0718444, [2] = 0.358939
17 train: [0] = 0.0395478, [1] = 0.0717712, [2] = 0.357968
17 val: [0] = 0.0383973, [1] = 0.0705981, [2] = 0.350403
18 train: [0] = 0.0386917, [1] = 0.0707028, [2] = 0.350387
18 val: [0] = 0.0376315, [1] = 0.0696576, [2] = 0.343279
19 train: [0] = 0.0379221, [1] = 0.0696883, [2] = 0.343405
19 val: [0] = 0.0370626, [1] = 0.0687811, [2] = 0.338175
20 train: [0] = 0.0372503, [1] = 0.0687757, [2] = 0.337446
20 val: [0] = 0.0362932, [1] = 0.0678003, [2] = 0.331113
21 train: [0] = 0.0365131, [1] = 0.0678388, [2] = 0.330856
21 val: [0] = 0.0355381, [1] = 0.0667367, [2] = 0.325004
22 train: [0] = 0.0358081, [1] = 0.0669415, [2] = 0.324784
22 val: [0] = 0.0348723, [1] = 0.0658702, [2] = 0.31921
23 train: [0] = 0.0351476, [1] = 0.0660793, [2] = 0.318953
23 val: [0] = 0.03425, [1] = 0.0650135, [2] = 0.314462
24 train: [0] = 0.0343294, [1] = 0.0650701, [2] = 0.312154
24 val: [0] = 0.0335333, [1] = 0.0642468, [2] = 0.306529
25 train: [0] = 0.0334772, [1] = 0.063919, [2] = 0.304477
25 val: [0] = 0.0326172, [1] = 0.0629654, [2] = 0.298886
26 train: [0] = 0.0327437, [1] = 0.062831, [2] = 0.297383
26 val: [0] = 0.0321894, [1] = 0.0621529, [2] = 0.29448
27 train: [0] = 0.032165, [1] = 0.0618943, [2] = 0.291548
27 val: [0] = 0.0314344, [1] = 0.061098, [2] = 0.287012
28 train: [0] = 0.0316088, [1] = 0.0610095, [2] = 0.286038
28 val: [0] = 0.0310447, [1] = 0.0605703, [2] = 0.282438
29 train: [0] = 0.0310883, [1] = 0.0602357, [2] = 0.280962
29 val: [0] = 0.0305147, [1] = 0.0597501, [2] = 0.277403
30 train: [0] = 0.0306064, [1] = 0.0595053, [2] = 0.276237
30 val: [0] = 0.0300379, [1] = 0.0588874, [2] = 0.273567
31 train: [0] = 0.0301777, [1] = 0.0588481, [2] = 0.272115
31 val: [0] = 0.0298604, [1] = 0.0585715, [2] = 0.270409
32 train: [0] = 0.0298158, [1] = 0.0583109, [2] = 0.268669
32 val: [0] = 0.0293243, [1] = 0.0578681, [2] = 0.266928
33 train: [0] = 0.029466, [1] = 0.0578114, [2] = 0.265177
33 val: [0] = 0.0290956, [1] = 0.057467, [2] = 0.26406
34 train: [0] = 0.029148, [1] = 0.0573553, [2] = 0.261973
34 val: [0] = 0.0288965, [1] = 0.0572227, [2] = 0.261332
35 train: [0] = 0.0288972, [1] = 0.0569737, [2] = 0.259419
35 val: [0] = 0.0285739, [1] = 0.0567675, [2] = 0.258195
36 train: [0] = 0.0286624, [1] = 0.0566261, [2] = 0.256885
36 val: [0] = 0.0284593, [1] = 0.0566445, [2] = 0.257406
37 train: [0] = 0.0284363, [1] = 0.0562884, [2] = 0.254602
37 val: [0] = 0.0282521, [1] = 0.0561554, [2] = 0.256267
38 train: [0] = 0.0282398, [1] = 0.0560002, [2] = 0.252702
38 val: [0] = 0.0279641, [1] = 0.0558851, [2] = 0.251916
39 train: [0] = 0.0279822, [1] = 0.055645, [2] = 0.250024
39 val: [0] = 0.027753, [1] = 0.0555792, [2] = 0.249941
40 train: [0] = 0.0277341, [1] = 0.0553196, [2] = 0.24774
40 val: [0] = 0.0276677, [1] = 0.0553761, [2] = 0.248556
41 train: [0] = 0.0275336, [1] = 0.0550226, [2] = 0.246002
41 val: [0] = 0.0273125, [1] = 0.0550359, [2] = 0.245733
42 train: [0] = 0.0273226, [1] = 0.0546956, [2] = 0.243569
42 val: [0] = 0.0271397, [1] = 0.0547055, [2] = 0.244406
43 train: [0] = 0.0271027, [1] = 0.0543735, [2] = 0.24145
43 val: [0] = 0.0270506, [1] = 0.0545766, [2] = 0.243207
44 train: [0] = 0.0269207, [1] = 0.054075, [2] = 0.239384
44 val: [0] = 0.0267041, [1] = 0.0539324, [2] = 0.240536
45 train: [0] = 0.0267437, [1] = 0.0537963, [2] = 0.237493
45 val: [0] = 0.0265473, [1] = 0.0537434, [2] = 0.238157
46 train: [0] = 0.0265788, [1] = 0.0535472, [2] = 0.235796
46 val: [0] = 0.0265634, [1] = 0.0537325, [2] = 0.236864
47 train: [0] = 0.0264022, [1] = 0.0532692, [2] = 0.233944
47 val: [0] = 0.0263406, [1] = 0.0533812, [2] = 0.23527
48 train: [0] = 0.0262433, [1] = 0.053047, [2] = 0.232354
48 val: [0] = 0.0262407, [1] = 0.0532841, [2] = 0.234697
49 train: [0] = 0.0260963, [1] = 0.0528149, [2] = 0.230767
49 val: [0] = 0.025875, [1] = 0.0527334, [2] = 0.232485
50 train: [0] = 0.0259331, [1] = 0.0525875, [2] = 0.229142
50 val: [0] = 0.0260158, [1] = 0.0528929, [2] = 0.232173
51 train: [0] = 0.0257498, [1] = 0.0523447, [2] = 0.227357
51 val: [0] = 0.0257188, [1] = 0.0525831, [2] = 0.228891
52 train: [0] = 0.0256414, [1] = 0.0521953, [2] = 0.226379
52 val: [0] = 0.0255333, [1] = 0.0522881, [2] = 0.227519
53 train: [0] = 0.025444, [1] = 0.0519487, [2] = 0.224531
53 val: [0] = 0.0253561, [1] = 0.0521923, [2] = 0.226878
54 train: [0] = 0.0252209, [1] = 0.051703, [2] = 0.222986
54 val: [0] = 0.0251624, [1] = 0.0518023, [2] = 0.225708
55 train: [0] = 0.025081, [1] = 0.051512, [2] = 0.221759
55 val: [0] = 0.0249202, [1] = 0.0515549, [2] = 0.223328
56 train: [0] = 0.024953, [1] = 0.0513309, [2] = 0.220402
56 val: [0] = 0.0249062, [1] = 0.0515682, [2] = 0.222796
57 train: [0] = 0.0248095, [1] = 0.051135, [2] = 0.219049
57 val: [0] = 0.0247027, [1] = 0.0511696, [2] = 0.221351
58 train: [0] = 0.0246829, [1] = 0.0509636, [2] = 0.217785
58 val: [0] = 0.0246617, [1] = 0.0511502, [2] = 0.219951
59 train: [0] = 0.0245487, [1] = 0.0507833, [2] = 0.216595
59 val: [0] = 0.0245376, [1] = 0.0510482, [2] = 0.218819
60 train: [0] = 0.0244225, [1] = 0.0506236, [2] = 0.21539
60 val: [0] = 0.0243674, [1] = 0.0506941, [2] = 0.216953
61 train: [0] = 0.0242919, [1] = 0.050445, [2] = 0.214046
61 val: [0] = 0.0242469, [1] = 0.0505856, [2] = 0.216388
62 train: [0] = 0.0241532, [1] = 0.0502383, [2] = 0.212674
62 val: [0] = 0.0241584, [1] = 0.0505052, [2] = 0.215868
63 train: [0] = 0.0240348, [1] = 0.0500906, [2] = 0.211545
63 val: [0] = 0.024043, [1] = 0.050203, [2] = 0.214479
64 train: [0] = 0.0239009, [1] = 0.049896, [2] = 0.210374
64 val: [0] = 0.0238189, [1] = 0.0501214, [2] = 0.212356
65 train: [0] = 0.0237548, [1] = 0.0496887, [2] = 0.208762
65 val: [0] = 0.0237379, [1] = 0.0498603, [2] = 0.211552
66 train: [0] = 0.0236109, [1] = 0.0495018, [2] = 0.207584
66 val: [0] = 0.0235095, [1] = 0.0495853, [2] = 0.209778
67 train: [0] = 0.0235128, [1] = 0.0493707, [2] = 0.206681
67 val: [0] = 0.0235777, [1] = 0.0497792, [2] = 0.209659
68 train: [0] = 0.0233826, [1] = 0.0491801, [2] = 0.205365
68 val: [0] = 0.0233929, [1] = 0.0493779, [2] = 0.207274
69 train: [0] = 0.0232704, [1] = 0.0490182, [2] = 0.204203
69 val: [0] = 0.0232867, [1] = 0.0493087, [2] = 0.207351
70 train: [0] = 0.0231396, [1] = 0.0488411, [2] = 0.202899
70 val: [0] = 0.0231084, [1] = 0.0490014, [2] = 0.205359
71 train: [0] = 0.0230459, [1] = 0.0486954, [2] = 0.201982
71 val: [0] = 0.0229964, [1] = 0.0489219, [2] = 0.20461
72 train: [0] = 0.0229483, [1] = 0.0485595, [2] = 0.201005
72 val: [0] = 0.0229971, [1] = 0.0488457, [2] = 0.204138
73 train: [0] = 0.0228259, [1] = 0.0484122, [2] = 0.199971
73 val: [0] = 0.0228516, [1] = 0.0485801, [2] = 0.202127
74 train: [0] = 0.022725, [1] = 0.0482582, [2] = 0.198964
74 val: [0] = 0.02267, [1] = 0.0483615, [2] = 0.201396
75 train: [0] = 0.0226378, [1] = 0.0481393, [2] = 0.198172
75 val: [0] = 0.0228068, [1] = 0.0485784, [2] = 0.205253
76 train: [0] = 0.022523, [1] = 0.0479768, [2] = 0.19702
76 val: [0] = 0.0225064, [1] = 0.048213, [2] = 0.200054
77 train: [0] = 0.0224068, [1] = 0.0478252, [2] = 0.195936
77 val: [0] = 0.0224643, [1] = 0.0481325, [2] = 0.199067
78 train: [0] = 0.0223091, [1] = 0.0476861, [2] = 0.194957
78 val: [0] = 0.02242, [1] = 0.0479097, [2] = 0.197991
79 train: [0] = 0.0222156, [1] = 0.0475493, [2] = 0.194053
79 val: [0] = 0.0221622, [1] = 0.0476786, [2] = 0.196466
[run_0] training putter for step 3:
0 train: [0] = 0.1013, [1] = 0.4056, [2] = 0.0001451, [3] = 0.1012, [4] = 0.09975, [5] = 0.01228, [6] = 6.534
0 train: [0] = 91.7%, [1] = 70.0%
0 val: [0] = 0.09667, [1] = 0.002751, [2] = 0.0003116, [3] = 0.09625, [4] = 0.09779, [5] = 0.01161, [6] = 6.534
0 val: [0] = 100.0%, [1] = 70.0%
1 train: [0] = 0.09174, [1] = 0.03907, [2] = 0.000492, [3] = 0.09169, [4] = 0.09664, [5] = 0.01231, [6] = 6.534
1 train: [0] = 99.3%, [1] = 70.0%
1 val: [0] = 0.08969, [1] = 0.002244, [2] = 0.0007313, [3] = 0.08965, [4] = 0.1044, [5] = 0.01161, [6] = 6.534
1 val: [0] = 100.0%, [1] = 70.0%
2 train: [0] = 0.08548, [1] = 0.008467, [2] = 0.0006972, [3] = 0.08615, [4] = 0.09663, [5] = 0.01228, [6] = 6.534
2 train: [0] = 99.9%, [1] = 70.0%
2 val: [0] = 0.09739, [1] = 0.001098, [2] = 0.0004329, [3] = 0.09761, [4] = 0.09872, [5] = 0.01161, [6] = 6.534
2 val: [0] = 100.0%, [1] = 70.0%
3 train: [0] = 0.08857, [1] = 0.002692, [2] = 0.001027, [3] = 0.08854, [4] = 0.09849, [5] = 0.01228, [6] = 6.534
3 train: [0] = 100.0%, [1] = 70.0%
3 val: [0] = 0.07935, [1] = 0.00141, [2] = 0.001132, [3] = 0.08022, [4] = 0.09627, [5] = 0.01182, [6] = 6.534
3 val: [0] = 100.0%, [1] = 70.0%
4 train: [0] = 0.07456, [1] = 0.004419, [2] = 0.001154, [3] = 0.07618, [4] = 0.09819, [5] = 0.01228, [6] = 6.534
4 train: [0] = 99.9%, [1] = 70.0%
4 val: [0] = 0.07227, [1] = 0.001058, [2] = 0.001032, [3] = 0.0733, [4] = 0.09972, [5] = 0.01161, [6] = 6.534
4 val: [0] = 100.0%, [1] = 70.0%
5 train: [0] = 0.06744, [1] = 0.0009855, [2] = 0.0006669, [3] = 0.06934, [4] = 0.09553, [5] = 0.01233, [6] = 6.534
5 train: [0] = 100.0%, [1] = 70.0%
5 val: [0] = 0.06267, [1] = 0.0008377, [2] = 0.0006271, [3] = 0.06507, [4] = 0.09909, [5] = 0.01163, [6] = 6.534
5 val: [0] = 100.0%, [1] = 70.0%
6 train: [0] = 0.07442, [1] = 0.0416, [2] = 0.0008253, [3] = 0.07561, [4] = 0.09782, [5] = 0.0123, [6] = 6.534
6 train: [0] = 99.7%, [1] = 70.0%
6 val: [0] = 0.06489, [1] = 0.0008186, [2] = 0.0006546, [3] = 0.06616, [4] = 0.1002, [5] = 0.01186, [6] = 6.534
6 val: [0] = 100.0%, [1] = 70.0%
7 train: [0] = 0.07134, [1] = 0.005096, [2] = 0.001072, [3] = 0.07274, [4] = 0.09743, [5] = 0.01232, [6] = 6.534
7 train: [0] = 100.0%, [1] = 70.0%
7 val: [0] = 0.06619, [1] = 0.000324, [2] = 0.0009378, [3] = 0.06802, [4] = 0.1015, [5] = 0.01161, [6] = 6.534
7 val: [0] = 100.0%, [1] = 70.0%
8 train: [0] = 0.06396, [1] = 0.0003012, [2] = 0.0009201, [3] = 0.06685, [4] = 0.09535, [5] = 0.01228, [6] = 6.534
8 train: [0] = 100.0%, [1] = 70.0%
8 val: [0] = 0.05722, [1] = 1.49e-05, [2] = 0.001007, [3] = 0.06122, [4] = 0.09652, [5] = 0.01161, [6] = 6.534
8 val: [0] = 100.0%, [1] = 70.0%
9 train: [0] = 0.06626, [1] = 0.05141, [2] = 0.001272, [3] = 0.06847, [4] = 0.09777, [5] = 0.01231, [6] = 6.534
9 train: [0] = 99.7%, [1] = 70.0%
9 val: [0] = 0.057, [1] = 0, [2] = 0.00107, [3] = 0.0589, [4] = 0.1035, [5] = 0.01161, [6] = 6.534
9 val: [0] = 100.0%, [1] = 70.0%
10 train: [0] = 0.0562, [1] = 0, [2] = 0.001042, [3] = 0.05886, [4] = 0.09782, [5] = 0.01229, [6] = 6.534
10 train: [0] = 100.0%, [1] = 70.0%
10 val: [0] = 0.05338, [1] = 0, [2] = 0.001155, [3] = 0.05682, [4] = 0.09781, [5] = 0.01161, [6] = 6.534
10 val: [0] = 100.0%, [1] = 70.0%
11 train: [0] = 0.05534, [1] = 0.003149, [2] = 0.001254, [3] = 0.05937, [4] = 0.09836, [5] = 0.01228, [6] = 6.534
11 train: [0] = 100.0%, [1] = 70.0%
11 val: [0] = 0.07191, [1] = 0, [2] = 0.001654, [3] = 0.07415, [4] = 0.0982, [5] = 0.01162, [6] = 6.534
11 val: [0] = 100.0%, [1] = 70.0%
12 train: [0] = 0.05775, [1] = 0.005304, [2] = 0.001436, [3] = 0.0617, [4] = 0.0979, [5] = 0.01228, [6] = 6.534
12 train: [0] = 100.0%, [1] = 70.0%
12 val: [0] = 0.06363, [1] = 2.46e-06, [2] = 0.001396, [3] = 0.06615, [4] = 0.09721, [5] = 0.01162, [6] = 6.534
12 val: [0] = 100.0%, [1] = 70.0%
13 train: [0] = 0.05589, [1] = 2.605e-05, [2] = 0.00143, [3] = 0.0603, [4] = 0.09709, [5] = 0.01229, [6] = 6.534
13 train: [0] = 100.0%, [1] = 70.0%
13 val: [0] = 0.05031, [1] = 0, [2] = 0.001698, [3] = 0.05602, [4] = 0.09659, [5] = 0.01161, [6] = 6.534
13 val: [0] = 100.0%, [1] = 70.0%
14 train: [0] = 0.05534, [1] = 0.001902, [2] = 0.001877, [3] = 0.06029, [4] = 0.1007, [5] = 0.01232, [6] = 6.534
14 train: [0] = 100.0%, [1] = 70.0%
14 val: [0] = 0.05427, [1] = 3.559e-11, [2] = 0.001823, [3] = 0.05932, [4] = 0.1006, [5] = 0.01162, [6] = 6.534
14 val: [0] = 100.0%, [1] = 70.0%
15 train: [0] = 0.07616, [1] = 0.09963, [2] = 0.001862, [3] = 0.07825, [4] = 0.09648, [5] = 0.01228, [6] = 6.534
15 train: [0] = 99.5%, [1] = 70.0%
15 val: [0] = 0.07274, [1] = 6.005e-08, [2] = 0.001441, [3] = 0.07455, [4] = 0.1013, [5] = 0.01166, [6] = 6.534
15 val: [0] = 100.0%, [1] = 70.0%
16 train: [0] = 0.06998, [1] = 0.001474, [2] = 0.001407, [3] = 0.07234, [4] = 0.0966, [5] = 0.01228, [6] = 6.534
16 train: [0] = 100.0%, [1] = 70.0%
16 val: [0] = 0.0754, [1] = 0, [2] = 0.001573, [3] = 0.07674, [4] = 0.1004, [5] = 0.01161, [6] = 6.534
16 val: [0] = 100.0%, [1] = 70.0%
17 train: [0] = 0.06706, [1] = 0, [2] = 0.001204, [3] = 0.06954, [4] = 0.1009, [5] = 0.01228, [6] = 6.534
17 train: [0] = 100.0%, [1] = 70.0%
17 val: [0] = 0.05692, [1] = 0, [2] = 0.0013, [3] = 0.06038, [4] = 0.1018, [5] = 0.01161, [6] = 6.534
17 val: [0] = 100.0%, [1] = 70.0%
18 train: [0] = 0.05796, [1] = 0.001698, [2] = 0.001433, [3] = 0.06168, [4] = 0.09995, [5] = 0.01232, [6] = 6.534
18 train: [0] = 100.0%, [1] = 70.0%
18 val: [0] = 0.05703, [1] = 0, [2] = 0.001411, [3] = 0.05976, [4] = 0.1026, [5] = 0.01161, [6] = 6.534
18 val: [0] = 100.0%, [1] = 70.0%
19 train: [0] = 0.05416, [1] = 0, [2] = 0.001196, [3] = 0.05793, [4] = 0.09784, [5] = 0.01232, [6] = 6.534
19 train: [0] = 100.0%, [1] = 70.0%
19 val: [0] = 0.0512, [1] = 0, [2] = 0.001112, [3] = 0.05547, [4] = 0.098, [5] = 0.01165, [6] = 6.534
19 val: [0] = 100.0%, [1] = 70.0%
[run_0] training getter for step 3:
0 train: [0] = 0.05307, [1] = 0.1043, [2] = 0.001166, [3] = 0.05753, [4] = 0.06535, [5] = 0.376, [6] = 3.074
0 train: [0] = 98.5%, [1] = 54.2%
0 val: [0] = 0.05085, [1] = 3.129e-06, [2] = 0.00115, [3] = 0.05503, [4] = 0.07301, [5] = 0.1188, [6] = 4.397
0 val: [0] = 100.0%, [1] = 58.0%
1 train: [0] = 0.05195, [1] = 9.351e-06, [2] = 0.001173, [3] = 0.05641, [4] = 0.07506, [5] = 0.09069, [6] = 4.946
1 train: [0] = 100.0%, [1] = 57.8%
1 val: [0] = 0.05098, [1] = 2.863e-08, [2] = 0.001123, [3] = 0.05515, [4] = 0.07414, [5] = 0.07121, [6] = 5.76
1 val: [0] = 100.0%, [1] = 58.0%
2 train: [0] = 0.05201, [1] = 5.416e-05, [2] = 0.001172, [3] = 0.05651, [4] = 0.07885, [5] = 0.06121, [6] = 6.012
2 train: [0] = 100.0%, [1] = 59.2%
2 val: [0] = 0.05103, [1] = 8.708e-09, [2] = 0.00114, [3] = 0.05521, [4] = 0.0801, [5] = 0.04982, [6] = 6.288
2 val: [0] = 100.0%, [1] = 60.0%
3 train: [0] = 0.05194, [1] = 3.687e-08, [2] = 0.001172, [3] = 0.05645, [4] = 0.07977, [5] = 0.04664, [6] = 6.635
3 train: [0] = 100.0%, [1] = 58.1%
3 val: [0] = 0.05113, [1] = 2.588e-08, [2] = 0.001123, [3] = 0.05524, [4] = 0.08257, [5] = 0.04306, [6] = 5.93
3 val: [0] = 100.0%, [1] = 62.0%
4 train: [0] = 0.05194, [1] = 8.209e-08, [2] = 0.001169, [3] = 0.05646, [4] = 0.07854, [5] = 0.03877, [6] = 7.102
4 train: [0] = 100.0%, [1] = 58.7%
4 val: [0] = 0.05105, [1] = 1.186e-10, [2] = 0.001124, [3] = 0.05515, [4] = 0.0791, [5] = 0.03433, [6] = 7.901
4 val: [0] = 100.0%, [1] = 56.0%
5 train: [0] = 0.05195, [1] = 9.98e-10, [2] = 0.001174, [3] = 0.05645, [4] = 0.07952, [5] = 0.03344, [6] = 7.446
5 train: [0] = 100.0%, [1] = 58.8%
5 val: [0] = 0.05104, [1] = 1.186e-11, [2] = 0.001133, [3] = 0.05523, [4] = 0.08373, [5] = 0.0301, [6] = 6.778
5 val: [0] = 100.0%, [1] = 61.0%
6 train: [0] = 0.0518, [1] = 4.423e-09, [2] = 0.001171, [3] = 0.05635, [4] = 0.08207, [5] = 0.02875, [6] = 7.563
6 train: [0] = 100.0%, [1] = 59.5%
6 val: [0] = 0.05096, [1] = 0, [2] = 0.001143, [3] = 0.05519, [4] = 0.07773, [5] = 0.02789, [6] = 6.951
6 val: [0] = 100.0%, [1] = 63.0%
7 train: [0] = 0.05181, [1] = 1.667e-11, [2] = 0.001164, [3] = 0.05638, [4] = 0.08407, [5] = 0.02681, [6] = 7.81
7 train: [0] = 100.0%, [1] = 59.0%
7 val: [0] = 0.05087, [1] = 0, [2] = 0.001153, [3] = 0.05509, [4] = 0.07473, [5] = 0.02424, [6] = 8.348
7 val: [0] = 100.0%, [1] = 59.0%
8 train: [0] = 0.05179, [1] = 1.18e-07, [2] = 0.001171, [3] = 0.05639, [4] = 0.08129, [5] = 0.02386, [6] = 8.385
8 train: [0] = 100.0%, [1] = 58.2%
8 val: [0] = 0.05094, [1] = 1.186e-11, [2] = 0.001141, [3] = 0.05519, [4] = 0.08754, [5] = 0.02271, [6] = 8.632
8 val: [0] = 100.0%, [1] = 56.0%
9 train: [0] = 0.05183, [1] = 2.404e-07, [2] = 0.001171, [3] = 0.05638, [4] = 0.07855, [5] = 0.02077, [6] = 8.457
9 train: [0] = 100.0%, [1] = 58.4%
9 val: [0] = 0.05093, [1] = 0, [2] = 0.001132, [3] = 0.05514, [4] = 0.07243, [5] = 0.02198, [6] = 8.554
9 val: [0] = 100.0%, [1] = 59.0%
10 train: [0] = 0.05182, [1] = 0, [2] = 0.001178, [3] = 0.05643, [4] = 0.08494, [5] = 0.01974, [6] = 8.26
10 train: [0] = 100.0%, [1] = 59.5%
10 val: [0] = 0.05127, [1] = 0, [2] = 0.001137, [3] = 0.05556, [4] = 0.08234, [5] = 0.01942, [6] = 8.377
10 val: [0] = 100.0%, [1] = 59.0%
11 train: [0] = 0.0518, [1] = 0, [2] = 0.001184, [3] = 0.05641, [4] = 0.0822, [5] = 0.01773, [6] = 8.307
11 train: [0] = 100.0%, [1] = 59.8%
11 val: [0] = 0.05102, [1] = 0, [2] = 0.001138, [3] = 0.05529, [4] = 0.07966, [5] = 0.01735, [6] = 8.384
11 val: [0] = 100.0%, [1] = 60.0%
12 train: [0] = 0.05181, [1] = 0, [2] = 0.001161, [3] = 0.05638, [4] = 0.08153, [5] = 0.01722, [6] = 8.959
12 train: [0] = 100.0%, [1] = 57.3%
12 val: [0] = 0.0509, [1] = 0, [2] = 0.001117, [3] = 0.0551, [4] = 0.07562, [5] = 0.01662, [6] = 8.951
12 val: [0] = 100.0%, [1] = 57.0%
13 train: [0] = 0.0518, [1] = 0, [2] = 0.001175, [3] = 0.05636, [4] = 0.08071, [5] = 0.01647, [6] = 8.885
13 train: [0] = 100.0%, [1] = 57.7%
13 val: [0] = 0.05131, [1] = 0, [2] = 0.001134, [3] = 0.05557, [4] = 0.08701, [5] = 0.01607, [6] = 9.766
13 val: [0] = 100.0%, [1] = 53.0%
14 train: [0] = 0.05186, [1] = 0, [2] = 0.001172, [3] = 0.05644, [4] = 0.08049, [5] = 0.0153, [6] = 9.226
14 train: [0] = 100.0%, [1] = 56.9%
14 val: [0] = 0.05102, [1] = 0, [2] = 0.00113, [3] = 0.05529, [4] = 0.08548, [5] = 0.01455, [6] = 9.336
14 val: [0] = 100.0%, [1] = 56.0%
15 train: [0] = 0.05183, [1] = 0, [2] = 0.00117, [3] = 0.05642, [4] = 0.08315, [5] = 0.01466, [6] = 9.511
15 train: [0] = 100.0%, [1] = 55.7%
15 val: [0] = 0.05101, [1] = 0, [2] = 0.001122, [3] = 0.0553, [4] = 0.08626, [5] = 0.01573, [6] = 9.463
15 val: [0] = 100.0%, [1] = 56.0%
16 train: [0] = 0.05182, [1] = 0, [2] = 0.001167, [3] = 0.05638, [4] = 0.08414, [5] = 0.01474, [6] = 9.119
16 train: [0] = 100.0%, [1] = 57.2%
16 val: [0] = 0.05086, [1] = 0, [2] = 0.001141, [3] = 0.05503, [4] = 0.08632, [5] = 0.01272, [6] = 8.437
16 val: [0] = 100.0%, [1] = 59.0%
17 train: [0] = 0.05174, [1] = 0, [2] = 0.001171, [3] = 0.0563, [4] = 0.07823, [5] = 0.0132, [6] = 8.831
17 train: [0] = 100.0%, [1] = 58.7%
17 val: [0] = 0.05096, [1] = 0, [2] = 0.001159, [3] = 0.05517, [4] = 0.07909, [5] = 0.01195, [6] = 8.332
17 val: [0] = 100.0%, [1] = 60.0%
18 train: [0] = 0.05185, [1] = 0, [2] = 0.001174, [3] = 0.05644, [4] = 0.08064, [5] = 0.01274, [6] = 8.705
18 train: [0] = 100.0%, [1] = 59.1%
18 val: [0] = 0.05108, [1] = 0, [2] = 0.001136, [3] = 0.05542, [4] = 0.08482, [5] = 0.01259, [6] = 8.802
18 val: [0] = 100.0%, [1] = 61.0%
19 train: [0] = 0.05183, [1] = 0, [2] = 0.001166, [3] = 0.05644, [4] = 0.08286, [5] = 0.01234, [6] = 8.688
19 train: [0] = 100.0%, [1] = 59.7%
19 val: [0] = 0.05093, [1] = 0, [2] = 0.001121, [3] = 0.05519, [4] = 0.08792, [5] = 0.012, [6] = 8.717
19 val: [0] = 100.0%, [1] = 61.0%
[run_0] test accuracy = 64.5%
[run_0] training autoencoder for step 4:
0 train: [0] = 0.115724, [1] = 0.212531, [2] = 0.841593
0 val: [0] = 0.0835574, [1] = 0.133632, [2] = 0.792395
1 train: [0] = 0.0833426, [1] = 0.134246, [2] = 0.796559
1 val: [0] = 0.0808772, [1] = 0.133402, [2] = 0.792109
2 train: [0] = 0.0780336, [1] = 0.131189, [2] = 0.764495
2 val: [0] = 0.0735004, [1] = 0.120895, [2] = 0.694696
3 train: [0] = 0.0723549, [1] = 0.118417, [2] = 0.665532
3 val: [0] = 0.0661134, [1] = 0.114677, [2] = 0.643187
4 train: [0] = 0.0641113, [1] = 0.107199, [2] = 0.597512
4 val: [0] = 0.0610522, [1] = 0.101457, [2] = 0.560735
5 train: [0] = 0.0611544, [1] = 0.100496, [2] = 0.552971
5 val: [0] = 0.0593268, [1] = 0.0977207, [2] = 0.537086
6 train: [0] = 0.0595879, [1] = 0.0974981, [2] = 0.533275
6 val: [0] = 0.0579264, [1] = 0.0954828, [2] = 0.522886
7 train: [0] = 0.0582484, [1] = 0.0953765, [2] = 0.52096
7 val: [0] = 0.0564068, [1] = 0.0931873, [2] = 0.510855
8 train: [0] = 0.0563129, [1] = 0.0926636, [2] = 0.50606
8 val: [0] = 0.0545818, [1] = 0.0904487, [2] = 0.492595
9 train: [0] = 0.0545006, [1] = 0.0898359, [2] = 0.487072
9 val: [0] = 0.0529008, [1] = 0.0879507, [2] = 0.476066
10 train: [0] = 0.0533841, [1] = 0.0881074, [2] = 0.474535
10 val: [0] = 0.0516185, [1] = 0.0864431, [2] = 0.464508
11 train: [0] = 0.0522254, [1] = 0.0866876, [2] = 0.464282
11 val: [0] = 0.0502988, [1] = 0.0847053, [2] = 0.453467
12 train: [0] = 0.0509473, [1] = 0.085122, [2] = 0.453695
12 val: [0] = 0.0490423, [1] = 0.0829884, [2] = 0.441444
13 train: [0] = 0.0494597, [1] = 0.0833095, [2] = 0.440823
13 val: [0] = 0.0475813, [1] = 0.0811014, [2] = 0.428024
14 train: [0] = 0.0479896, [1] = 0.0814142, [2] = 0.426422
14 val: [0] = 0.0462063, [1] = 0.0792472, [2] = 0.414533
15 train: [0] = 0.0467168, [1] = 0.0796447, [2] = 0.414038
15 val: [0] = 0.0452788, [1] = 0.0777996, [2] = 0.403728
16 train: [0] = 0.0456442, [1] = 0.0781391, [2] = 0.403545
16 val: [0] = 0.0443194, [1] = 0.0766598, [2] = 0.395374
17 train: [0] = 0.0448157, [1] = 0.0769799, [2] = 0.395707
17 val: [0] = 0.0436227, [1] = 0.0755638, [2] = 0.388492
18 train: [0] = 0.0441035, [1] = 0.076043, [2] = 0.389416
18 val: [0] = 0.0430344, [1] = 0.0750177, [2] = 0.38296
19 train: [0] = 0.0434613, [1] = 0.0752524, [2] = 0.383876
19 val: [0] = 0.0423274, [1] = 0.0740831, [2] = 0.377561
20 train: [0] = 0.0427521, [1] = 0.0744194, [2] = 0.377877
20 val: [0] = 0.041869, [1] = 0.0734333, [2] = 0.373645
21 train: [0] = 0.0421577, [1] = 0.0737079, [2] = 0.372509
21 val: [0] = 0.0409766, [1] = 0.0724859, [2] = 0.366403
22 train: [0] = 0.0413968, [1] = 0.0728463, [2] = 0.36659
22 val: [0] = 0.0402772, [1] = 0.0716598, [2] = 0.361271
23 train: [0] = 0.0405902, [1] = 0.0719203, [2] = 0.360325
23 val: [0] = 0.0396216, [1] = 0.0708287, [2] = 0.354948
24 train: [0] = 0.0397545, [1] = 0.0709797, [2] = 0.353983
24 val: [0] = 0.0388294, [1] = 0.0700554, [2] = 0.348323
25 train: [0] = 0.0388298, [1] = 0.0699341, [2] = 0.347033
25 val: [0] = 0.0378598, [1] = 0.0689839, [2] = 0.341776
26 train: [0] = 0.0380061, [1] = 0.0690216, [2] = 0.340939
26 val: [0] = 0.0372128, [1] = 0.0682049, [2] = 0.335598
27 train: [0] = 0.0373146, [1] = 0.0682218, [2] = 0.335132
27 val: [0] = 0.0365748, [1] = 0.0673995, [2] = 0.329985
28 train: [0] = 0.0366793, [1] = 0.0674299, [2] = 0.329466
28 val: [0] = 0.035981, [1] = 0.0666551, [2] = 0.32526
29 train: [0] = 0.036121, [1] = 0.0666809, [2] = 0.324154
29 val: [0] = 0.0354112, [1] = 0.0660185, [2] = 0.319793
30 train: [0] = 0.0355542, [1] = 0.0659254, [2] = 0.318554
30 val: [0] = 0.0351365, [1] = 0.0656508, [2] = 0.315356
31 train: [0] = 0.0351455, [1] = 0.0653338, [2] = 0.31463
31 val: [0] = 0.0346222, [1] = 0.0648, [2] = 0.312559
32 train: [0] = 0.0346621, [1] = 0.0646799, [2] = 0.309993
32 val: [0] = 0.0341949, [1] = 0.0641144, [2] = 0.306847
33 train: [0] = 0.0342169, [1] = 0.0640838, [2] = 0.305913
33 val: [0] = 0.0337873, [1] = 0.0636094, [2] = 0.303086
34 train: [0] = 0.0338145, [1] = 0.0635251, [2] = 0.302178
34 val: [0] = 0.0334977, [1] = 0.0631118, [2] = 0.300948
35 train: [0] = 0.0334183, [1] = 0.0629805, [2] = 0.298467
35 val: [0] = 0.0329174, [1] = 0.0624633, [2] = 0.295812
36 train: [0] = 0.0330148, [1] = 0.0624876, [2] = 0.295
36 val: [0] = 0.032723, [1] = 0.0622207, [2] = 0.293368
37 train: [0] = 0.0326343, [1] = 0.0620019, [2] = 0.291483
37 val: [0] = 0.0324967, [1] = 0.0620194, [2] = 0.290991
38 train: [0] = 0.0323918, [1] = 0.0616341, [2] = 0.289002
38 val: [0] = 0.0322051, [1] = 0.0614454, [2] = 0.287557
39 train: [0] = 0.032063, [1] = 0.0612207, [2] = 0.285904
39 val: [0] = 0.0318095, [1] = 0.0609684, [2] = 0.285809
40 train: [0] = 0.0317953, [1] = 0.0608783, [2] = 0.283536
40 val: [0] = 0.0314456, [1] = 0.0606275, [2] = 0.28278
41 train: [0] = 0.0314754, [1] = 0.060503, [2] = 0.280677
41 val: [0] = 0.0316099, [1] = 0.0610344, [2] = 0.283698
42 train: [0] = 0.0312119, [1] = 0.0601879, [2] = 0.278459
42 val: [0] = 0.0309479, [1] = 0.0599972, [2] = 0.278066
43 train: [0] = 0.0309506, [1] = 0.0598593, [2] = 0.276277
43 val: [0] = 0.0309624, [1] = 0.0600926, [2] = 0.277166
44 train: [0] = 0.0307117, [1] = 0.059546, [2] = 0.274175
44 val: [0] = 0.0304828, [1] = 0.0594083, [2] = 0.274106
45 train: [0] = 0.0304891, [1] = 0.0592469, [2] = 0.272238
45 val: [0] = 0.0303952, [1] = 0.0592106, [2] = 0.273092
46 train: [0] = 0.0302645, [1] = 0.0589712, [2] = 0.270392
46 val: [0] = 0.0303352, [1] = 0.0592669, [2] = 0.272079
47 train: [0] = 0.0300488, [1] = 0.0586855, [2] = 0.268525
47 val: [0] = 0.030012, [1] = 0.0586705, [2] = 0.269013
48 train: [0] = 0.0298205, [1] = 0.0584104, [2] = 0.266755
48 val: [0] = 0.0297375, [1] = 0.0582871, [2] = 0.267638
49 train: [0] = 0.0295837, [1] = 0.0580921, [2] = 0.264906
49 val: [0] = 0.0295113, [1] = 0.0581921, [2] = 0.266198
50 train: [0] = 0.0293621, [1] = 0.057809, [2] = 0.263182
50 val: [0] = 0.0292779, [1] = 0.0578484, [2] = 0.264057
51 train: [0] = 0.0291253, [1] = 0.0574936, [2] = 0.261496
51 val: [0] = 0.0290606, [1] = 0.0575394, [2] = 0.263
52 train: [0] = 0.028883, [1] = 0.0571542, [2] = 0.259745
52 val: [0] = 0.0288159, [1] = 0.0572301, [2] = 0.261914
53 train: [0] = 0.0286116, [1] = 0.0567848, [2] = 0.257542
53 val: [0] = 0.028531, [1] = 0.056749, [2] = 0.258918
54 train: [0] = 0.0283632, [1] = 0.0564422, [2] = 0.25564
54 val: [0] = 0.0282276, [1] = 0.0564179, [2] = 0.256667
55 train: [0] = 0.0281033, [1] = 0.0560844, [2] = 0.253396
55 val: [0] = 0.0281051, [1] = 0.0561353, [2] = 0.255183
56 train: [0] = 0.0279307, [1] = 0.0558059, [2] = 0.251941
56 val: [0] = 0.0278624, [1] = 0.0559185, [2] = 0.253634
57 train: [0] = 0.0276975, [1] = 0.0554805, [2] = 0.249887
57 val: [0] = 0.0276694, [1] = 0.0555357, [2] = 0.250718
58 train: [0] = 0.0275095, [1] = 0.0552018, [2] = 0.248262
58 val: [0] = 0.0276975, [1] = 0.0555546, [2] = 0.251249
59 train: [0] = 0.0273283, [1] = 0.0549202, [2] = 0.246666
59 val: [0] = 0.0274349, [1] = 0.0551955, [2] = 0.249632
60 train: [0] = 0.0271539, [1] = 0.0546709, [2] = 0.245043
60 val: [0] = 0.0271836, [1] = 0.0549631, [2] = 0.246981
61 train: [0] = 0.0269624, [1] = 0.0544008, [2] = 0.243316
61 val: [0] = 0.0268759, [1] = 0.0544819, [2] = 0.245712
62 train: [0] = 0.0267664, [1] = 0.054122, [2] = 0.241581
62 val: [0] = 0.0268646, [1] = 0.0543987, [2] = 0.244047
63 train: [0] = 0.0266352, [1] = 0.0539404, [2] = 0.240392
63 val: [0] = 0.0267785, [1] = 0.0543648, [2] = 0.245829
64 train: [0] = 0.0264445, [1] = 0.0536863, [2] = 0.23893
64 val: [0] = 0.0267491, [1] = 0.0543463, [2] = 0.243306
65 train: [0] = 0.0262304, [1] = 0.0533933, [2] = 0.236744
65 val: [0] = 0.0262715, [1] = 0.0535625, [2] = 0.239501
66 train: [0] = 0.0260759, [1] = 0.0531826, [2] = 0.235483
66 val: [0] = 0.0262702, [1] = 0.0537384, [2] = 0.238989
67 train: [0] = 0.0259238, [1] = 0.0529737, [2] = 0.234108
67 val: [0] = 0.0260798, [1] = 0.0533175, [2] = 0.236729
68 train: [0] = 0.0257206, [1] = 0.0526903, [2] = 0.232195
68 val: [0] = 0.0258647, [1] = 0.0530496, [2] = 0.235755
69 train: [0] = 0.0255423, [1] = 0.0524537, [2] = 0.230708
69 val: [0] = 0.025555, [1] = 0.0526747, [2] = 0.233614
70 train: [0] = 0.025383, [1] = 0.0522441, [2] = 0.22935
70 val: [0] = 0.025581, [1] = 0.0527214, [2] = 0.23313
71 train: [0] = 0.025247, [1] = 0.0520555, [2] = 0.22824
71 val: [0] = 0.0253939, [1] = 0.0524248, [2] = 0.231512
72 train: [0] = 0.0250792, [1] = 0.051823, [2] = 0.2266
72 val: [0] = 0.0252726, [1] = 0.0521121, [2] = 0.231575
73 train: [0] = 0.0249653, [1] = 0.0516582, [2] = 0.225669
73 val: [0] = 0.0251817, [1] = 0.0523329, [2] = 0.230355
74 train: [0] = 0.0247882, [1] = 0.0514329, [2] = 0.224108
74 val: [0] = 0.0249211, [1] = 0.0517423, [2] = 0.227789
75 train: [0] = 0.0246416, [1] = 0.0512164, [2] = 0.222718
75 val: [0] = 0.0247561, [1] = 0.0514851, [2] = 0.225404
76 train: [0] = 0.0244947, [1] = 0.0510224, [2] = 0.221474
76 val: [0] = 0.0246762, [1] = 0.0514769, [2] = 0.225339
77 train: [0] = 0.0243946, [1] = 0.0508703, [2] = 0.220474
77 val: [0] = 0.0245768, [1] = 0.0511989, [2] = 0.223711
78 train: [0] = 0.0242559, [1] = 0.0506776, [2] = 0.219203
78 val: [0] = 0.0244655, [1] = 0.0511363, [2] = 0.222392
79 train: [0] = 0.0241036, [1] = 0.0504727, [2] = 0.217826
79 val: [0] = 0.0242864, [1] = 0.0508538, [2] = 0.221313
[run_0] training putter for step 4:
0 train: [0] = 0.09849, [1] = 0.6238, [2] = 0.0004924, [3] = 0.09781, [4] = 0.08075, [5] = 0.01223, [6] = 7.878
0 train: [0] = 92.3%, [1] = 63.0%
0 val: [0] = 0.07894, [1] = 0.001798, [2] = 0.000743, [3] = 0.07913, [4] = 0.08111, [5] = 0.01215, [6] = 7.878
0 val: [0] = 100.0%, [1] = 63.0%
1 train: [0] = 0.07391, [1] = 0.001271, [2] = 0.0006124, [3] = 0.0746, [4] = 0.08116, [5] = 0.01223, [6] = 7.878
1 train: [0] = 100.0%, [1] = 63.0%
1 val: [0] = 0.06272, [1] = 3.279e-05, [2] = 0.0006444, [3] = 0.06401, [4] = 0.08666, [5] = 0.012, [6] = 7.878
1 val: [0] = 100.0%, [1] = 63.0%
2 train: [0] = 0.06957, [1] = 0.06473, [2] = 0.0008002, [3] = 0.07056, [4] = 0.08119, [5] = 0.01226, [6] = 7.878
2 train: [0] = 99.3%, [1] = 63.0%
2 val: [0] = 0.06081, [1] = 2.056e-05, [2] = 0.0006202, [3] = 0.06184, [4] = 0.07911, [5] = 0.012, [6] = 7.878
2 val: [0] = 100.0%, [1] = 63.0%
3 train: [0] = 0.06497, [1] = 0.01972, [2] = 0.0006217, [3] = 0.06596, [4] = 0.07837, [5] = 0.01225, [6] = 7.878
3 train: [0] = 99.9%, [1] = 63.0%
3 val: [0] = 0.05668, [1] = 3.559e-11, [2] = 0.0004627, [3] = 0.05752, [4] = 0.08331, [5] = 0.012, [6] = 7.878
3 val: [0] = 100.0%, [1] = 63.0%
4 train: [0] = 0.05626, [1] = 2.382e-12, [2] = 0.0005079, [3] = 0.05773, [4] = 0.08039, [5] = 0.01223, [6] = 7.878
4 train: [0] = 100.0%, [1] = 63.0%
4 val: [0] = 0.05431, [1] = 0, [2] = 0.0005723, [3] = 0.05632, [4] = 0.08467, [5] = 0.01221, [6] = 7.878
4 val: [0] = 100.0%, [1] = 63.0%
5 train: [0] = 0.05376, [1] = 0, [2] = 0.0007862, [3] = 0.05712, [4] = 0.08253, [5] = 0.01227, [6] = 7.878
5 train: [0] = 100.0%, [1] = 63.0%
5 val: [0] = 0.05135, [1] = 0, [2] = 0.001062, [3] = 0.05595, [4] = 0.08463, [5] = 0.012, [6] = 7.878
5 val: [0] = 100.0%, [1] = 63.0%
6 train: [0] = 0.05591, [1] = 0.01543, [2] = 0.001165, [3] = 0.0595, [4] = 0.07927, [5] = 0.01223, [6] = 7.878
6 train: [0] = 99.9%, [1] = 63.0%
6 val: [0] = 0.05358, [1] = 0, [2] = 0.0009287, [3] = 0.05613, [4] = 0.08519, [5] = 0.01217, [6] = 7.878
6 val: [0] = 100.0%, [1] = 63.0%
7 train: [0] = 0.0528, [1] = 2.943e-08, [2] = 0.001129, [3] = 0.0567, [4] = 0.08024, [5] = 0.01223, [6] = 7.878
7 train: [0] = 100.0%, [1] = 63.0%
7 val: [0] = 0.05103, [1] = 0, [2] = 0.001251, [3] = 0.0557, [4] = 0.08251, [5] = 0.01212, [6] = 7.878
7 val: [0] = 100.0%, [1] = 63.0%
8 train: [0] = 0.05687, [1] = 0.0267, [2] = 0.001586, [3] = 0.06157, [4] = 0.08079, [5] = 0.01223, [6] = 7.878
8 train: [0] = 99.8%, [1] = 63.0%
8 val: [0] = 0.06846, [1] = 0, [2] = 0.001155, [3] = 0.06949, [4] = 0.08371, [5] = 0.01205, [6] = 7.878
8 val: [0] = 100.0%, [1] = 63.0%
9 train: [0] = 0.06272, [1] = 0.001274, [2] = 0.001094, [3] = 0.06471, [4] = 0.07996, [5] = 0.01223, [6] = 7.878
9 train: [0] = 100.0%, [1] = 63.0%
9 val: [0] = 0.05998, [1] = 0, [2] = 0.001024, [3] = 0.06229, [4] = 0.08296, [5] = 0.012, [6] = 7.878
9 val: [0] = 100.0%, [1] = 63.0%
10 train: [0] = 0.05538, [1] = 1.775e-09, [2] = 0.0009986, [3] = 0.05838, [4] = 0.08018, [5] = 0.01223, [6] = 7.878
10 train: [0] = 100.0%, [1] = 63.0%
10 val: [0] = 0.05212, [1] = 0, [2] = 0.001098, [3] = 0.05609, [4] = 0.08159, [5] = 0.012, [6] = 7.878
10 val: [0] = 100.0%, [1] = 63.0%
11 train: [0] = 0.06129, [1] = 0.004154, [2] = 0.001783, [3] = 0.06432, [4] = 0.08014, [5] = 0.01225, [6] = 7.878
11 train: [0] = 100.0%, [1] = 63.0%
11 val: [0] = 0.05626, [1] = 3.915e-10, [2] = 0.001212, [3] = 0.0589, [4] = 0.08454, [5] = 0.01221, [6] = 7.878
11 val: [0] = 100.0%, [1] = 63.0%
12 train: [0] = 0.05364, [1] = 8.918e-08, [2] = 0.001304, [3] = 0.05759, [4] = 0.08295, [5] = 0.01223, [6] = 7.878
12 train: [0] = 100.0%, [1] = 63.0%
12 val: [0] = 0.05121, [1] = 0.001612, [2] = 0.001458, [3] = 0.05589, [4] = 0.08866, [5] = 0.012, [6] = 7.878
12 val: [0] = 100.0%, [1] = 63.0%
13 train: [0] = 0.05885, [1] = 0.001091, [2] = 0.00145, [3] = 0.06237, [4] = 0.08163, [5] = 0.01223, [6] = 7.878
13 train: [0] = 100.0%, [1] = 63.0%
13 val: [0] = 0.05288, [1] = 2.468e-09, [2] = 0.001167, [3] = 0.05679, [4] = 0.08249, [5] = 0.012, [6] = 7.878
13 val: [0] = 100.0%, [1] = 63.0%
14 train: [0] = 0.05178, [1] = 0, [2] = 0.001368, [3] = 0.05687, [4] = 0.07894, [5] = 0.01223, [6] = 7.878
14 train: [0] = 100.0%, [1] = 63.0%
14 val: [0] = 0.04982, [1] = 1.002e-06, [2] = 0.001505, [3] = 0.0555, [4] = 0.08844, [5] = 0.012, [6] = 7.878
14 val: [0] = 100.0%, [1] = 63.0%
15 train: [0] = 0.05964, [1] = 0.04187, [2] = 0.001703, [3] = 0.06373, [4] = 0.0817, [5] = 0.01226, [6] = 7.878
15 train: [0] = 99.7%, [1] = 63.0%
15 val: [0] = 0.05958, [1] = 0, [2] = 0.001085, [3] = 0.06176, [4] = 0.08399, [5] = 0.01218, [6] = 7.878
15 val: [0] = 100.0%, [1] = 63.0%
16 train: [0] = 0.05506, [1] = 0, [2] = 0.000996, [3] = 0.05792, [4] = 0.08227, [5] = 0.01223, [6] = 7.878
16 train: [0] = 100.0%, [1] = 63.0%
16 val: [0] = 0.05218, [1] = 0, [2] = 0.001124, [3] = 0.05585, [4] = 0.08189, [5] = 0.012, [6] = 7.878
16 val: [0] = 100.0%, [1] = 63.0%
17 train: [0] = 0.05127, [1] = 1.667e-11, [2] = 0.001431, [3] = 0.05649, [4] = 0.08137, [5] = 0.01227, [6] = 7.878
17 train: [0] = 100.0%, [1] = 63.0%
17 val: [0] = 0.04906, [1] = 2.065e-06, [2] = 0.001768, [3] = 0.05499, [4] = 0.08743, [5] = 0.012, [6] = 7.878
17 val: [0] = 100.0%, [1] = 63.0%
18 train: [0] = 0.06499, [1] = 0.01153, [2] = 0.001461, [3] = 0.06742, [4] = 0.08116, [5] = 0.01223, [6] = 7.878
18 train: [0] = 99.9%, [1] = 63.0%
18 val: [0] = 0.05671, [1] = 1.068e-10, [2] = 0.001369, [3] = 0.06011, [4] = 0.08237, [5] = 0.012, [6] = 7.878
18 val: [0] = 100.0%, [1] = 63.0%
19 train: [0] = 0.05394, [1] = 3.096e-11, [2] = 0.001448, [3] = 0.05831, [4] = 0.08071, [5] = 0.01224, [6] = 7.878
19 train: [0] = 100.0%, [1] = 63.0%
19 val: [0] = 0.05078, [1] = 7.474e-10, [2] = 0.001578, [3] = 0.05602, [4] = 0.08564, [5] = 0.012, [6] = 7.878
19 val: [0] = 100.0%, [1] = 63.0%
[run_0] training getter for step 4:
0 train: [0] = 0.05221, [1] = 0.1169, [2] = 0.001591, [3] = 0.05731, [4] = 0.07121, [5] = 0.3599, [6] = 4.378
0 train: [0] = 97.7%, [1] = 51.9%
0 val: [0] = 0.05054, [1] = 1.617e-05, [2] = 0.00156, [3] = 0.05556, [4] = 0.07319, [5] = 0.1162, [6] = 5.942
0 val: [0] = 100.0%, [1] = 54.0%
1 train: [0] = 0.05126, [1] = 3.976e-05, [2] = 0.001608, [3] = 0.05659, [4] = 0.07642, [5] = 0.08953, [6] = 6.584
1 train: [0] = 100.0%, [1] = 54.8%
1 val: [0] = 0.05051, [1] = 7.227e-06, [2] = 0.001579, [3] = 0.05567, [4] = 0.07903, [5] = 0.07202, [6] = 7.317
1 val: [0] = 100.0%, [1] = 56.0%
2 train: [0] = 0.05131, [1] = 7.174e-05, [2] = 0.001606, [3] = 0.05671, [4] = 0.07718, [5] = 0.0607, [6] = 7.275
2 train: [0] = 100.0%, [1] = 56.1%
2 val: [0] = 0.05069, [1] = 6.585e-06, [2] = 0.001566, [3] = 0.05572, [4] = 0.08808, [5] = 0.05208, [6] = 7.394
2 val: [0] = 100.0%, [1] = 56.0%
3 train: [0] = 0.05132, [1] = 2.288e-05, [2] = 0.001621, [3] = 0.05672, [4] = 0.08329, [5] = 0.04752, [6] = 7.684
3 train: [0] = 100.0%, [1] = 56.1%
3 val: [0] = 0.05056, [1] = 3.053e-07, [2] = 0.001533, [3] = 0.05578, [4] = 0.08472, [5] = 0.04674, [6] = 7.608
3 val: [0] = 100.0%, [1] = 57.0%
4 train: [0] = 0.05129, [1] = 0.000276, [2] = 0.001605, [3] = 0.05679, [4] = 0.08308, [5] = 0.03868, [6] = 7.728
4 train: [0] = 100.0%, [1] = 57.3%
4 val: [0] = 0.05058, [1] = 1.569e-05, [2] = 0.001587, [3] = 0.0558, [4] = 0.08089, [5] = 0.03553, [6] = 7.601
4 val: [0] = 100.0%, [1] = 61.0%
5 train: [0] = 0.05131, [1] = 0.0002, [2] = 0.001607, [3] = 0.05681, [4] = 0.08026, [5] = 0.0345, [6] = 8.099
5 train: [0] = 100.0%, [1] = 56.1%
5 val: [0] = 0.05061, [1] = 3.82e-09, [2] = 0.001546, [3] = 0.05573, [4] = 0.07173, [5] = 0.03272, [6] = 8.197
5 val: [0] = 100.0%, [1] = 56.0%
6 train: [0] = 0.05131, [1] = 1.624e-06, [2] = 0.001597, [3] = 0.05675, [4] = 0.0774, [5] = 0.03064, [6] = 8.607
6 train: [0] = 100.0%, [1] = 53.9%
6 val: [0] = 0.0507, [1] = 1.244e-06, [2] = 0.001534, [3] = 0.05584, [4] = 0.07833, [5] = 0.02995, [6] = 8.622
6 val: [0] = 100.0%, [1] = 54.0%
7 train: [0] = 0.05131, [1] = 0.000386, [2] = 0.001607, [3] = 0.05673, [4] = 0.07762, [5] = 0.02741, [6] = 8.774
7 train: [0] = 100.0%, [1] = 54.2%
7 val: [0] = 0.05061, [1] = 0, [2] = 0.001541, [3] = 0.05574, [4] = 0.08146, [5] = 0.02829, [6] = 8.598
7 val: [0] = 100.0%, [1] = 54.0%
8 train: [0] = 0.05131, [1] = 4.769e-05, [2] = 0.001612, [3] = 0.05672, [4] = 0.07857, [5] = 0.02657, [6] = 9.039
8 train: [0] = 100.0%, [1] = 54.5%
8 val: [0] = 0.0508, [1] = 0.0007586, [2] = 0.001584, [3] = 0.05602, [4] = 0.09027, [5] = 0.02507, [6] = 8.521
8 val: [0] = 100.0%, [1] = 59.0%
9 train: [0] = 0.05123, [1] = 8.317e-07, [2] = 0.001614, [3] = 0.05674, [4] = 0.07793, [5] = 0.02378, [6] = 8.633
9 train: [0] = 100.0%, [1] = 55.8%
9 val: [0] = 0.0506, [1] = 5.398e-09, [2] = 0.001562, [3] = 0.05579, [4] = 0.08031, [5] = 0.02389, [6] = 8.844
9 val: [0] = 100.0%, [1] = 54.0%
10 train: [0] = 0.05124, [1] = 4.721e-06, [2] = 0.001625, [3] = 0.05676, [4] = 0.07691, [5] = 0.02119, [6] = 9.173
10 train: [0] = 100.0%, [1] = 54.0%
10 val: [0] = 0.05052, [1] = 0.000759, [2] = 0.001595, [3] = 0.05573, [4] = 0.08247, [5] = 0.02042, [6] = 8.679
10 val: [0] = 100.0%, [1] = 56.0%
11 train: [0] = 0.05135, [1] = 2.039e-06, [2] = 0.001611, [3] = 0.05682, [4] = 0.07895, [5] = 0.01966, [6] = 9.169
11 train: [0] = 100.0%, [1] = 54.6%
11 val: [0] = 0.05109, [1] = 3.048e-07, [2] = 0.001571, [3] = 0.05596, [4] = 0.08858, [5] = 0.01929, [6] = 8.61
11 val: [0] = 100.0%, [1] = 58.0%
12 train: [0] = 0.05136, [1] = 3.706e-05, [2] = 0.001601, [3] = 0.0568, [4] = 0.08249, [5] = 0.01935, [6] = 9.09
12 train: [0] = 100.0%, [1] = 55.5%
12 val: [0] = 0.0506, [1] = 7.309e-08, [2] = 0.001571, [3] = 0.05583, [4] = 0.07898, [5] = 0.01945, [6] = 8.506
12 val: [0] = 100.0%, [1] = 58.0%
13 train: [0] = 0.05129, [1] = 1.247e-05, [2] = 0.001593, [3] = 0.05675, [4] = 0.08104, [5] = 0.01766, [6] = 8.986
13 train: [0] = 100.0%, [1] = 56.0%
13 val: [0] = 0.05055, [1] = 0, [2] = 0.001544, [3] = 0.05575, [4] = 0.07636, [5] = 0.01535, [6] = 9.607
13 val: [0] = 100.0%, [1] = 52.0%
14 train: [0] = 0.05142, [1] = 0.000136, [2] = 0.001613, [3] = 0.0568, [4] = 0.08489, [5] = 0.01729, [6] = 9.745
14 train: [0] = 100.0%, [1] = 52.9%
14 val: [0] = 0.05063, [1] = 4.117e-09, [2] = 0.001559, [3] = 0.05577, [4] = 0.08872, [5] = 0.01792, [6] = 8.728
14 val: [0] = 100.0%, [1] = 58.0%
15 train: [0] = 0.05129, [1] = 4.724e-07, [2] = 0.001608, [3] = 0.05678, [4] = 0.08012, [5] = 0.01668, [6] = 9.291
15 train: [0] = 100.0%, [1] = 54.5%
15 val: [0] = 0.05065, [1] = 0.0001071, [2] = 0.001546, [3] = 0.0558, [4] = 0.07546, [5] = 0.015, [6] = 9.677
15 val: [0] = 100.0%, [1] = 53.0%
16 train: [0] = 0.05136, [1] = 0.0004628, [2] = 0.001609, [3] = 0.05682, [4] = 0.08599, [5] = 0.0157, [6] = 9.532
16 train: [0] = 100.0%, [1] = 54.4%
16 val: [0] = 0.05055, [1] = 9.729e-10, [2] = 0.001563, [3] = 0.05587, [4] = 0.08851, [5] = 0.01487, [6] = 9.708
16 val: [0] = 100.0%, [1] = 54.0%
17 train: [0] = 0.05117, [1] = 0, [2] = 0.00161, [3] = 0.05669, [4] = 0.08004, [5] = 0.01505, [6] = 9.594
17 train: [0] = 100.0%, [1] = 54.7%
17 val: [0] = 0.0505, [1] = 0, [2] = 0.001572, [3] = 0.05568, [4] = 0.07926, [5] = 0.01651, [6] = 9.208
17 val: [0] = 100.0%, [1] = 54.0%
18 train: [0] = 0.05125, [1] = 5.24e-11, [2] = 0.001615, [3] = 0.05674, [4] = 0.08192, [5] = 0.01451, [6] = 9.781
18 train: [0] = 100.0%, [1] = 54.3%
18 val: [0] = 0.05069, [1] = 9.373e-10, [2] = 0.00158, [3] = 0.05589, [4] = 0.09051, [5] = 0.01473, [6] = 10.18
18 val: [0] = 100.0%, [1] = 54.0%
19 train: [0] = 0.05127, [1] = 2.737e-05, [2] = 0.00161, [3] = 0.05679, [4] = 0.08019, [5] = 0.01413, [6] = 9.807
19 train: [0] = 100.0%, [1] = 53.9%
19 val: [0] = 0.05048, [1] = 0, [2] = 0.001557, [3] = 0.05572, [4] = 0.08573, [5] = 0.01474, [6] = 9.597
19 val: [0] = 100.0%, [1] = 55.0%
[run_0] test accuracy = 61.4%
