Training a Neural Network - Enhanced Console Output From Brain.js

gistfile1.txt

Start Training!...Here's the data, we'll do 2 iterations:
[ { input: [ 0, 1 ], output: [ 1, 0 ] },
  { input: [ 1, 1 ], output: [ 1, 1 ] } ]
======== TRAINING ITERATION 1 =========
--------- Run input set 0: 0,1 ----------
-> Layer 2 has 3 nodes
   START NODE: 0
   -> bias for node 0: 0.13861538469791412
   -> weights for node 0:
     -> input value: 0, weight: -0.03485306352376938
     -> input value: 1, weight: 0.16674068570137024
   -> node value for layer: 2, node: 0 = 0.30535607039928436 ... value = bias + the sum of (dimension * weight) for every dimension in the layer}
   -> sigmoid the node value:  0.5757513046264648
   -> layer:2, node:0 is done

   START NODE: 1
   -> bias for node 1: -0.12367551028728485
   -> weights for node 1:
     -> input value: 0, weight: 0.12118933349847794
     -> input value: 1, weight: -0.19215473532676697
   -> node value for layer: 2, node: 1 = -0.3158302456140518 ... value = bias + the sum of (dimension * weight) for every dimension in the layer}
   -> sigmoid the node value:  0.42169228196144104
   -> layer:2, node:1 is done

   START NODE: 2
   -> bias for node 2: 0.1551799178123474
   -> weights for node 2:
     -> input value: 0, weight: 0.01784847490489483
     -> input value: 1, weight: 0.02730054408311844
   -> node value for layer: 2, node: 2 = 0.18248046189546585 ... value = bias + the sum of (dimension * weight) for every dimension in the layer}
   -> sigmoid the node value:  0.5454939603805542
   -> layer:2, node:2 is done

-> Layer 3 has 2 nodes
   START NODE: 0
   -> bias for node 0: -0.13386934995651245
   -> weights for node 0:
     -> input value: 0.5757513046264648, weight: -0.16104543209075928
     -> input value: 0.42169228196144104, weight: 0.061312124133110046
     -> input value: 0.5454939603805542, weight: 0.05124082416296005
   -> node value for layer: 3, node: 0 = -0.17278505794348886 ... value = bias + the sum of (dimension * weight) for every dimension in the layer}
   -> sigmoid the node value:  0.4569108784198761
   -> layer:3, node:0 is done

   START NODE: 1
   -> bias for node 1: 0.15492039918899536
   -> weights for node 1:
     -> input value: 0.5757513046264648, weight: 0.10478495061397552
     -> input value: 0.42169228196144104, weight: -0.012492481619119644
     -> input value: 0.5454939603805542, weight: -0.014647948555648327
   -> node value for layer: 3, node: 1 = 0.2019921206598123 ... value = bias + the sum of (dimension * weight) for every dimension in the layer}
   -> sigmoid the node value:  0.5503270030021667
   -> layer:3, node:1 is done

Calculate error delta for layer 3:
   -> node: 0
   -> error is 0.5430891215801239
   -> delta is 0.1347639418130422
   -> node: 1
   -> error is -0.5503270030021667
   -> delta is -0.13618787853782413
Calculate error delta for layer 2:
   -> node: 0
   -> error is -0.03597355754482523
   -> delta is -0.008786963754399161
   -> node: 1
   -> error is 0.009963988092636655
   -> delta is 0.0024298968646855853
   -> node: 2
   -> error is 0.008900288491116154
   -> delta is 0.002206651191851995
Calculate error delta for layer 1:
   -> node: 0
   -> error is 0.0006401155434291668
   -> delta is 0
   -> node: 1
   -> error is -0.001871817713965664
   -> delta is 0
   -> adjusted change = (learningRate * delta * value) + (momentum * change)
      learningRate: 0.3, momentum: 0.1
      -> layer:1, node: 0, dimension: 0, value: 0, change:0
         adjusted change: 0
      -> layer:1, node: 0, dimension: 1, value: 1, change:0
         adjusted change: -0.002636088989675045
   -> adjusting bias for layer: 1, node: 0 bias + (learningRate * delta)
   -> 0.1359792947769165
      -> layer:1, node: 1, dimension: 0, value: 0, change:0
         adjusted change: 0
      -> layer:1, node: 1, dimension: 1, value: 1, change:0
         adjusted change: 0.0007289690896868706
   -> adjusting bias for layer: 1, node: 1 bias + (learningRate * delta)
   -> -0.12294653803110123
      -> layer:1, node: 2, dimension: 0, value: 0, change:0
         adjusted change: 0
      -> layer:1, node: 2, dimension: 1, value: 1, change:0
         adjusted change: 0.0006619953783228993
   -> adjusting bias for layer: 1, node: 2 bias + (learningRate * delta)
   -> 0.1558419167995453
      -> layer:2, node: 0, dimension: 0, value: 0.5757513046264648, change:0
         adjusted change: 0.023277154483359652
      -> layer:2, node: 0, dimension: 1, value: 0.42169228196144104, change:0
         adjusted change: 0.017048674163274713
      -> layer:2, node: 0, dimension: 2, value: 0.5454939603805542, change:0
         adjusted change: 0.022053874795395778
   -> adjusting bias for layer: 2, node: 0 bias + (learningRate * delta)
   -> -0.09344016760587692
      -> layer:2, node: 1, dimension: 0, value: 0.5757513046264648, change:0
         adjusted change: -0.023523105087971882
      -> layer:2, node: 1, dimension: 1, value: 0.42169228196144104, change:0
         adjusted change: -0.017228813523577188
      -> layer:2, node: 1, dimension: 2, value: 0.5454939603805542, change:0
         adjusted change: -0.022286900006610818
   -> adjusting bias for layer: 2, node: 1 bias + (learningRate * delta)
   -> 0.1140640377998352
--------- Run input set 1: 1,1 ----------
-> Layer 2 has 3 nodes
   START NODE: 0
   -> bias for node 0: 0.1359792947769165
   -> weights for node 0:
     -> input value: 1, weight: -0.03485306352376938
     -> input value: 1, weight: 0.16410459578037262
   -> node value for layer: 2, node: 0 = 0.26523082703351974 ... value = bias + the sum of (dimension * weight) for every dimension in the layer}
   -> sigmoid the node value:  0.5659217238426208
   -> layer:2, node:0 is done

   START NODE: 1
   -> bias for node 1: -0.12294653803110123
   -> weights for node 1:
     -> input value: 1, weight: 0.12118933349847794
     -> input value: 1, weight: -0.19142577052116394
   -> node value for layer: 2, node: 1 = -0.19318297505378723 ... value = bias + the sum of (dimension * weight) for every dimension in the layer}
   -> sigmoid the node value:  0.4518539011478424
   -> layer:2, node:1 is done

   START NODE: 2
   -> bias for node 2: 0.1558419167995453
   -> weights for node 2:
     -> input value: 1, weight: 0.01784847490489483
     -> input value: 1, weight: 0.027962539345026016
   -> node value for layer: 2, node: 2 = 0.20165293104946613 ... value = bias + the sum of (dimension * weight) for every dimension in the layer}
   -> sigmoid the node value:  0.550243079662323
   -> layer:2, node:2 is done

-> Layer 3 has 2 nodes
   START NODE: 0
   -> bias for node 0: -0.09344016760587692
   -> weights for node 0:
     -> input value: 0.5659217238426208, weight: -0.13776828348636627
     -> input value: 0.4518539011478424, weight: 0.07836079597473145
     -> input value: 0.550243079662323, weight: 0.07329469919204712
   -> node value for layer: 3, node: 0 = -0.09566869972273206 ... value = bias + the sum of (dimension * weight) for every dimension in the layer}
   -> sigmoid the node value:  0.4761010408401489
   -> layer:3, node:0 is done

   START NODE: 1
   -> bias for node 1: 0.1140640377998352
   -> weights for node 1:
     -> input value: 0.5659217238426208, weight: 0.08126184344291687
     -> input value: 0.4518539011478424, weight: -0.029721295461058617
     -> input value: 0.550243079662323, weight: -0.03693484887480736
   -> node value for layer: 3, node: 1 = 0.1262990520306964 ... value = bias + the sum of (dimension * weight) for every dimension in the layer}
   -> sigmoid the node value:  0.531532883644104
   -> layer:3, node:1 is done

Calculate error delta for layer 3:
   -> node: 0
   -> error is 0.5238989591598511
   -> delta is 0.13067550953003787
   -> node: 1
   -> error is 0.468467116355896
   -> delta is 0.11665097157712691
Calculate error delta for layer 2:
   -> node: 0
   -> error is -0.0085236678841627
   -> delta is -0.0020938758919072874
   -> node: 1
   -> error is 0.006772819035239908
   -> delta is 0.0016775050470834668
   -> node: 2
   -> error is 0.005269336262488822
   -> delta is 0.0013040323267649707
Calculate error delta for layer 1:
   -> node: 0
   -> error is 0.0002995486968244655
   -> delta is 0
   -> node: 1
   -> error is -0.0006282682890281048
   -> delta is 0
   -> adjusted change = (learningRate * delta * value) + (momentum * change)
      learningRate: 0.3, momentum: 0.1
      -> layer:1, node: 0, dimension: 0, value: 1, change:0
         adjusted change: -0.0006281627342104912
      -> layer:1, node: 0, dimension: 1, value: 1, change:-0.002636088989675045
         adjusted change: -0.0008917716331779957
   -> adjusting bias for layer: 1, node: 0 bias + (learningRate * delta)
   -> 0.13535113632678986
      -> layer:1, node: 1, dimension: 0, value: 1, change:0
         adjusted change: 0.0005032515269704163
      -> layer:1, node: 1, dimension: 1, value: 1, change:0.0007289691129699349
         adjusted change: 0.0005761484382674098
   -> adjusting bias for layer: 1, node: 1 bias + (learningRate * delta)
   -> -0.122443288564682
      -> layer:1, node: 2, dimension: 0, value: 1, change:0
         adjusted change: 0.0003912096843123436
      -> layer:1, node: 2, dimension: 1, value: 1, change:0.0006619953783228993
         adjusted change: 0.00045740922214463356
   -> adjusting bias for layer: 1, node: 2 bias + (learningRate * delta)
   -> 0.15623313188552856
      -> layer:2, node: 0, dimension: 0, value: 0.5659217238426208, change:0.023277154192328453
         adjusted change: 0.02451334831153753
      -> layer:2, node: 0, dimension: 1, value: 0.4518539011478424, change:0.017048673704266548
         adjusted change: 0.019418739005807727
      -> layer:2, node: 0, dimension: 2, value: 0.550243079662323, change:0.022053875029087067
         adjusted change: 0.023776375949915707
   -> adjusting bias for layer: 2, node: 0 bias + (learningRate * delta)
   -> -0.05423751473426819
      -> layer:2, node: 1, dimension: 0, value: 0.5659217238426208, change:-0.023523105308413506
         adjusted change: 0.01745228467283244
      -> layer:2, node: 1, dimension: 1, value: 0.4518539011478424, change:-0.017228813841938972
         adjusted change: 0.014089877211944477
      -> layer:2, node: 1, dimension: 2, value: 0.550243079662323, change:-0.022286899387836456
         adjusted change: 0.017027226555006256
   -> adjusting bias for layer: 2, node: 1 bias + (learningRate * delta)
   -> 0.14905932545661926
======== TRAINING ITERATION 2 =========
--------- Run input set 0: 0,1 ----------
-> Layer 2 has 3 nodes
   START NODE: 0
   -> bias for node 0: 0.13535113632678986
   -> weights for node 0:
     -> input value: 0, weight: -0.035481225699186325
     -> input value: 1, weight: 0.1632128208875656
   -> node value for layer: 2, node: 0 = 0.29856395721435547 ... value = bias + the sum of (dimension * weight) for every dimension in the layer}
   -> sigmoid the node value:  0.5740914344787598
   -> layer:2, node:0 is done

   START NODE: 1
   -> bias for node 1: -0.122443288564682
   -> weights for node 1:
     -> input value: 0, weight: 0.12169258296489716
     -> input value: 1, weight: -0.19084961712360382
   -> node value for layer: 2, node: 1 = -0.3132929056882858 ... value = bias + the sum of (dimension * weight) for every dimension in the layer}
   -> sigmoid the node value:  0.4223111867904663
   -> layer:2, node:1 is done

   START NODE: 2
   -> bias for node 2: 0.15623313188552856
   -> weights for node 2:
     -> input value: 0, weight: 0.018239684402942657
     -> input value: 1, weight: 0.028419949114322662
   -> node value for layer: 2, node: 2 = 0.18465308099985123 ... value = bias + the sum of (dimension * weight) for every dimension in the layer}
   -> sigmoid the node value:  0.5460325479507446
   -> layer:2, node:2 is done

-> Layer 3 has 2 nodes
   START NODE: 0
   -> bias for node 0: -0.05423751473426819
   -> weights for node 0:
     -> input value: 0.5740914344787598, weight: -0.11325493454933167
     -> input value: 0.4223111867904663, weight: 0.0977795347571373
     -> input value: 0.5460325479507446, weight: 0.097071073949337
   -> node value for layer: 3, node: 0 = -0.024958845363514115 ... value = bias + the sum of (dimension * weight) for every dimension in the layer}
   -> sigmoid the node value:  0.4937606155872345
   -> layer:3, node:0 is done

   START NODE: 1
   -> bias for node 1: 0.14905932545661926
   -> weights for node 1:
     -> input value: 0.5740914344787598, weight: 0.09871412813663483
     -> input value: 0.4223111867904663, weight: -0.01563141867518425
     -> input value: 0.5460325479507446, weight: -0.019907621666789055
   -> node value for layer: 3, node: 1 = 0.18825872852760805 ... value = bias + the sum of (dimension * weight) for every dimension in the layer}
   -> sigmoid the node value:  0.5469261407852173
   -> layer:3, node:1 is done

Calculate error delta for layer 3:
   -> node: 0
   -> error is 0.5062393844127655
   -> delta is 0.1265401382455436
   -> node: 1
   -> error is -0.5469261407852173
   -> delta is -0.1355271695480457
Calculate error delta for layer 2:
   -> node: 0
   -> error is -0.02770974100408541
   -> delta is -0.006775321501014893
   -> node: 1
   -> error is 0.014491517788912
   -> delta is 0.0035354151424319
   -> node: 2
   -> error is 0.014981410722637645
   -> delta is 0.003713607139191384
Calculate error delta for layer 1:
   -> node: 0
   -> error is 0.0007383655192597805
   -> delta is 0
   -> node: 1
   -> error is -0.0016750114078337135
   -> delta is 0
   -> adjusted change = (learningRate * delta * value) + (momentum * change)
      learningRate: 0.3, momentum: 0.1
      -> layer:1, node: 0, dimension: 0, value: 0, change:-0.0006281627574935555
         adjusted change: -0.00006281627574935556
      -> layer:1, node: 0, dimension: 1, value: 1, change:-0.0008917716331779957
         adjusted change: -0.0021217735949903725
   -> adjusting bias for layer: 1, node: 0 bias + (learningRate * delta)
   -> 0.13331854343414307
      -> layer:1, node: 1, dimension: 0, value: 0, change:0.0005032515036873519
         adjusted change: 0.000050325150368735194
      -> layer:1, node: 1, dimension: 1, value: 1, change:0.000576148449908942
         adjusted change: 0.0011182393587660044
   -> adjusting bias for layer: 1, node: 1 bias + (learningRate * delta)
   -> -0.12138266116380692
      -> layer:1, node: 2, dimension: 0, value: 0, change:0.0003912096726708114
         adjusted change: 0.00003912096726708115
      -> layer:1, node: 2, dimension: 1, value: 1, change:0.00045740921632386744
         adjusted change: 0.0011598230485105887
   -> adjusting bias for layer: 1, node: 2 bias + (learningRate * delta)
   -> 0.15734721720218658
      -> layer:2, node: 0, dimension: 0, value: 0.5740914344787598, change:0.024513348937034607
         adjusted change: 0.02424501792368332
      -> layer:2, node: 0, dimension: 1, value: 0.4223111867904663, change:0.019418738782405853
         adjusted change: 0.01797366880178348
      -> layer:2, node: 0, dimension: 2, value: 0.5460325479507446, change:0.023776376619935036
         adjusted change: 0.02310614806886857
   -> adjusting bias for layer: 2, node: 0 bias + (learningRate * delta)
   -> -0.016275472939014435
      -> layer:2, node: 1, dimension: 0, value: 0.5740914344787598, change:0.017452284693717957
         adjusted change: -0.02159626668401273
      -> layer:2, node: 1, dimension: 1, value: 0.4223111867904663, change:0.014089876785874367
         adjusted change: -0.015761403530331463
      -> layer:2, node: 1, dimension: 2, value: 0.5460325479507446, change:0.017027227208018303
         adjusted change: -0.020497950039895938
   -> adjusting bias for layer: 2, node: 1 bias + (learningRate * delta)
   -> 0.10840117931365967
--------- Run input set 1: 1,1 ----------
-> Layer 2 has 3 nodes
   START NODE: 0
   -> bias for node 0: 0.13331854343414307
   -> weights for node 0:
     -> input value: 1, weight: -0.03554404154419899
     -> input value: 1, weight: 0.16109104454517365
   -> node value for layer: 2, node: 0 = 0.2588655464351177 ... value = bias + the sum of (dimension * weight) for every dimension in the layer}
   -> sigmoid the node value:  0.5643573999404907
   -> layer:2, node:0 is done

   START NODE: 1
   -> bias for node 1: -0.12138266116380692
   -> weights for node 1:
     -> input value: 1, weight: 0.12174291163682938
     -> input value: 1, weight: -0.18973137438297272
   -> node value for layer: 2, node: 1 = -0.18937112390995026 ... value = bias + the sum of (dimension * weight) for every dimension in the layer}
   -> sigmoid the node value:  0.45279818773269653
   -> layer:2, node:1 is done

   START NODE: 2
   -> bias for node 2: 0.15734721720218658
   -> weights for node 2:
     -> input value: 1, weight: 0.018278805539011955
     -> input value: 1, weight: 0.02957977168262005
   -> node value for layer: 2, node: 2 = 0.2052057944238186 ... value = bias + the sum of (dimension * weight) for every dimension in the layer}
   -> sigmoid the node value:  0.5511221885681152
   -> layer:2, node:2 is done

-> Layer 3 has 2 nodes
   START NODE: 0
   -> bias for node 0: -0.016275472939014435
   -> weights for node 0:
     -> input value: 0.5643573999404907, weight: -0.08900991827249527
     -> input value: 0.45279818773269653, weight: 0.11575320363044739
     -> input value: 0.5511221885681152, weight: 0.12017722427845001
   -> node value for layer: 3, node: 0 = 0.05213629670430553 ... value = bias + the sum of (dimension * weight) for every dimension in the layer}
   -> sigmoid the node value:  0.5130311250686646
   -> layer:3, node:0 is done

   START NODE: 1
   -> bias for node 1: 0.10840117931365967
   -> weights for node 1:
     -> input value: 0.5643573999404907, weight: 0.07711786031723022
     -> input value: 0.45279818773269653, weight: -0.03139282390475273
     -> input value: 0.5511221885681152, weight: -0.04040557146072388
   -> node value for layer: 3, node: 1 = 0.11544019370560243 ... value = bias + the sum of (dimension * weight) for every dimension in the layer}
   -> sigmoid the node value:  0.5288280248641968
   -> layer:3, node:1 is done

Calculate error delta for layer 3:
   -> node: 0
   -> error is 0.48696887493133545
   -> delta is 0.12165952644077827
   -> node: 1
   -> error is 0.4711719751358032
   -> delta is 0.11740142394987547
Calculate error delta for layer 2:
   -> node: 0
   -> error is -0.0017751579629419756
   -> delta is -0.00043643700847714213
   -> node: 1
   -> error is 0.010396917602851108
   -> delta is 0.002576064953082077
   -> node: 2
   -> error is 0.009877032481504622
   -> delta is 0.0024434447116606843
Calculate error delta for layer 1:
   -> node: 0
   -> error is 0.00037379364063796626
   -> delta is 0
   -> node: 1
   -> error is -0.000486789911916587
   -> delta is 0
   -> adjusted change = (learningRate * delta * value) + (momentum * change)
      learningRate: 0.3, momentum: 0.1
      -> layer:1, node: 0, dimension: 0, value: 1, change:-0.00006281627429416403
         adjusted change: -0.00013721272625843994
      -> layer:1, node: 0, dimension: 1, value: 1, change:-0.002121773548424244
         adjusted change: -0.0003431084536714479
   -> adjusting bias for layer: 1, node: 0 bias + (learningRate * delta)
   -> 0.13318760693073273
      -> layer:1, node: 1, dimension: 0, value: 1, change:0.000050325150368735194
         adjusted change: 0.0007778520201100036
      -> layer:1, node: 1, dimension: 1, value: 1, change:0.0011182393645867705
         adjusted change: 0.0008846434415318072
   -> adjusting bias for layer: 1, node: 1 bias + (learningRate * delta)
   -> -0.1206098422408104
      -> layer:1, node: 2, dimension: 0, value: 1, change:0.000039120968722272664
         adjusted change: 0.0007369455015577842
      -> layer:1, node: 2, dimension: 1, value: 1, change:0.0011598230339586735
         adjusted change: 0.0008490157080814243
   -> adjusting bias for layer: 1, node: 2 bias + (learningRate * delta)
   -> 0.15808025002479553
      -> layer:2, node: 0, dimension: 0, value: 0.5643573999404907, change:0.024245018139481544
         adjusted change: 0.023022337731821718
      -> layer:2, node: 0, dimension: 1, value: 0.45279818773269653, change:0.01797366887331009
         adjusted change: 0.0183235305839744
      -> layer:2, node: 0, dimension: 2, value: 0.5511221885681152, change:0.023106148466467857
         adjusted change: 0.022425393906906167
   -> adjusting bias for layer: 2, node: 0 bias + (learningRate * delta)
   -> 0.020222384482622147
      -> layer:2, node: 1, dimension: 0, value: 0.5643573999404907, change:-0.021596265956759453
         adjusted change: 0.017717281627655977
      -> layer:2, node: 1, dimension: 1, value: 0.45279818773269653, change:-0.015761403366923332
         adjusted change: 0.01437160487264286
      -> layer:2, node: 1, dimension: 2, value: 0.5511221885681152, change:-0.020497949793934822
         adjusted change: 0.017360963456954433
   -> adjusting bias for layer: 2, node: 1 bias + (learningRate * delta)
   -> 0.14362160861492157

HERE'S YOUR TRAINED NETWORK!
{
  "sizes": [
    2,
    3,
    2
  ],
  "layers": [
    {
      "0": {
        
      },
      "1": {
        
      }
    },
    {
      "0": {
        "bias": 0.13318760693073,
        "weights": {
          "0": -0.035681255161762,
          "1": 0.16074793040752
        }
      },
      "1": {
        "bias": -0.12060984224081,
        "weights": {
          "0": 0.12252076715231,
          "1": -0.18884673714638
        }
      },
      "2": {
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