config-hptune.yaml

trainingInput:
  scaleTier: BASIC_GPU
  hyperparameters:
    goal: MINIMIZE
    hyperparameterMetricTag: loss
    maxTrials: 53
    maxParallelTrials: 7
    params:
      - parameterName: hp-conv1
        type: INTEGER
        minValue: 6
        maxValue: 18
        scaleType: UNIT_LINEAR_SCALE
      - parameterName: hp-conv2
        type: INTEGER
        minValue: 12
        maxValue: 40
        scaleType: UNIT_LINEAR_SCALE
      - parameterName: hp-conv3
        type: INTEGER
        minValue: 30
        maxValue: 64
        scaleType: UNIT_LINEAR_SCALE
      - parameterName: hp-lr2
        type: INTEGER
        minValue: 500
        maxValue: 5000
        scaleType: UNIT_LINEAR_SCALE

# best (job153):
#"trialId": "44",
#  "hyperparameters": {
#    "hp-conv3": "64",
#    "hp-conv2": "38",
#    "hp-lr2": "601",
#    "hp-conv1": "15"
#  },
#  "finalMetric": {
#    "trainingStep": "10000",
#    "objectiveValue": 1.48850083351
#  }
#},
# => final accuracy 0.9954
# => at 2000 iterations acc=0.9950 loss=1.663
# => at 3000 iterations acc=0.9953 loss=1.526
#{
#  "trialId": "43",
#  "hyperparameters": {
#    "hp-conv2": "39",
#    "hp-lr2": "541",
#    "hp-conv1": "17",
#    "hp-conv3": "63"
#  },
#  "finalMetric": {
#    "trainingStep": "10000",
#    "objectiveValue": 1.5163513422
#  }
# => final accuracy 0.9944
# => at 2000 iterations acc=0.9942 loss=1.567
# => at 3000 iterations acc=0.9942 loss=1.496
#
# Other params:
# learning rate lr1=0.02-0.0001-???
# conv layers: with patch sizes 6x6 5x5 4x4 with batch norm
# dense layer: 200 batch norm
# dropout 0.3
# bnexp 0.993
# activation: relu
#
# results: These two trials actually hit 0.9950 accuracy in 2000 iterations only
# finally making true on the promise of batch normalisation to speed up training.