log.txt

   1 winlen  winstep model   loss    accuracy
   2
   3 (array([0, 1]), array([476266, 325691]))
   4 0.025   0.01    bottle3 0.3389156284828169      0.8597792879855353
   5
   6 model_0.025_0.01_bottle3
   7 (array([0, 1]), array([476266, 325691]))
   8 0.025   0.01    bottle5 0.30842042285169746     0.874156742939086
   9
  10 model_0.025_0.01_bottle5
  11 (array([0, 1]), array([476266, 325691]))
  12 0.025   0.01    bottle8 0.2965808613601041      0.8830849803611148
  13
  14 model_0.025_0.01_bottle8
  15 (array([0, 1]), array([476266, 325691]))
  16 0.025   0.01    bottle13        0.28287798926393115     0.889955732901797
  17
  18 model_0.025_0.01_bottle13
  19 (array([0, 1, 2, 3]), array([476266, 125684, 151977,  48030]))
  20 0.025   0.01    multi3  0.48447136703078025     0.8319970072947191
  21
  22 model_0.025_0.01_multi3
  23 (array([0, 1, 2, 3]), array([476266, 125684, 151977,  48030]))
  24 0.025   0.01    multi5  0.43238697158434836     0.8497038468739897
  25
  26 model_0.025_0.01_multi5
  27 (array([0, 1, 2, 3]), array([476266, 125684, 151977,  48030]))
  28 0.025   0.01    multi8  0.4072758802538003      0.8624353139223143
  29
  30 model_0.025_0.01_multi8
  31 (array([0, 1, 2, 3]), array([476266, 125684, 151977,  48030]))
  32 0.025   0.01    multi13 0.3707011521600716      0.868520481326766
  33
  34 model_0.025_0.01_multi13
  35 (array([0, 1]), array([119037,  81431]))
  36 0.1     0.04    bottle3 0.32152209714074387     0.8698992317728829
  37
  38 model_0.1_0.04_bottle3
  39 (array([0, 1]), array([119037,  81431]))
  40 0.1     0.04    bottle5 0.29785478306865665     0.879028235071257
  41
  42 model_0.1_0.04_bottle5
  43 (array([0, 1]), array([119037,  81431]))
  44 0.1     0.04    bottle8 0.31144997012195363     0.8783797266109135
  45
  46 model_0.1_0.04_bottle8
  47 (array([0, 1]), array([119037,  81431]))
  48 0.1     0.04    bottle13        0.28727721201615886     0.8858126309547645
  49
  50 model_0.1_0.04_bottle13
  51 (array([0, 1, 2, 3]), array([119037,  31435,  37996,  12000]))
  52 0.1     0.04    multi3  0.47640502210221436     0.824603412157997
  53
  54 model_0.1_0.04_multi3
  55 (array([0, 1, 2, 3]), array([119037,  31435,  37996,  12000]))
  56 0.1     0.04    multi5  0.44105214603370885     0.8400678439588946
  57
  58 model_0.1_0.04_multi5
  59 (array([0, 1, 2, 3]), array([119037,  31435,  37996,  12000]))
  60 0.1     0.04    multi8  0.3903473072779056      0.8636635737803053
  61
  62 model_0.1_0.04_multi8
  63 (array([0, 1, 2, 3]), array([119037,  31435,  37996,  12000]))
  64 0.1     0.04    multi13 0.3752127004474934      0.8698493464852026
  65
  66 model_0.1_0.04_multi13
  67 (array([0, 1]), array([59493, 40723]))
  68 0.2     0.08    bottle3 0.35470913447088        0.847719788444267
  69
  70 model_0.2_0.08_bottle3
  71 (array([0, 1]), array([59493, 40723]))
  72 0.2     0.08    bottle5 0.32276059763122966     0.870272427901407
  73
  74 model_0.2_0.08_bottle5
  75 (array([0, 1]), array([59493, 40723]))
  76 0.2     0.08    bottle8 0.2949916362783269      0.8849416225925556
  77
  78 model_0.2_0.08_bottle8
  79 (array([0, 1]), array([59493, 40723]))
  80 0.2     0.08    bottle13        0.3005677448865742      0.8803512623490669
  81
  82 model_0.2_0.08_bottle13
  83 (array([0, 1, 2, 3]), array([59493, 15726, 18993,  6004]))
  84 0.2     0.08    multi3  0.4804011737202328      0.8227721784312548
  85
  86 model_0.2_0.08_multi3
  87 (array([0, 1, 2, 3]), array([59493, 15726, 18993,  6004]))
  88 0.2     0.08    multi5  0.4430828566863061      0.8434287995210059
  89
  90 model_0.2_0.08_multi5
  91 (array([0, 1, 2, 3]), array([59493, 15726, 18993,  6004]))
  92 0.2     0.08    multi8  0.39796411239295504     0.8585969464244296
  93
  94 model_0.2_0.08_multi8
  95 (array([0, 1, 2, 3]), array([59493, 15726, 18993,  6004]))
  96 0.2     0.08    multi13 0.391048318939632       0.8592954794930646
  97
  98 model_0.2_0.08_multi13
  99