1. Test Modules
  2. Differential Validation
    1. Feedback Validation
    2. Learning Validation
    3. Total Accuracy
    4. Frozen and Alive Status
  3. Results

Subreport: Logs for com.simiacryptus.ref.lang.ReferenceCountingBase

Test Modules

Using Seed 7003733885302288384

Differential Validation

SingleDerivativeTester.java:101 executed in 0.00 seconds (0.000 gc):

        log.info(RefString.format("Inputs: %s", prettyPrint(inputPrototype)));
        log.info(RefString.format("Inputs Statistics: %s", printStats(inputPrototype)));
        log.info(RefString.format("Output: %s", outputPrototype.prettyPrint()));
        assert outputPrototype != null;
        log.info(RefString.format("Outputs Statistics: %s", outputPrototype.getScalarStatistics()));
      },
      outputPrototype.addRef(),
      RefUtil.addRef(inputPrototype)));
Logging
Inputs: [
[ [ -0.852, 1.108, -1.616 ], [ -1.688, -0.712, 1.512 ] ],
[ [ 1.912, 0.028, 1.356 ], [ -0.804, 1.048, 1.556 ] ]
]
Inputs Statistics: {meanExponent=-0.04822021070395558, negative=5, min=-1.688, max=1.912, mean=0.23733333333333337, count=12, sum=2.8480000000000003, positive=7, stdDev=1.2646689509731612, zeros=0}
Output: [
[ [ 1.4654399999999999, -1.139024, 0.6205440000000001 ], [ 2.9033599999999997, 0.731936, -0.580608 ] ],
[ [ -3.28864, -0.028784, -0.5207040000000001 ], [ 1.38288, -1.077344, -0.597504 ] ]
]
Outputs Statistics: {meanExponent=-0.10426928205917668, negative=7, min=-3.28864, max=2.9033599999999997, mean=-0.010704000000000028, count=12, sum=-0.12844800000000034, positive=5, stdDev=1.5178954098988509, zeros=0}

Feedback Validation

We validate the agreement between the implemented derivative of the inputs apply finite difference estimations:

SingleDerivativeTester.java:117 executed in 0.03 seconds (0.000 gc):

        return testFeedback(
            statistics,
            component.addRef(),
            RefUtil.addRef(inputPrototype),
            outputPrototype.addRef());
      },
      outputPrototype.addRef(),
      RefUtil.addRef(inputPrototype),
      component.addRef()));
Logging
Feedback for input 0
Inputs Values: [
[ [ -0.852, 1.108, -1.616 ], [ -1.688, -0.712, 1.512 ] ],
[ [ 1.912, 0.028, 1.356 ], [ -0.804, 1.048, 1.556 ] ]
]
Value Statistics: {meanExponent=-0.04822021070395558, negative=5, min=-1.688, max=1.912, mean=0.23733333333333337, count=12, sum=2.8480000000000003, positive=7, stdDev=1.2646689509731612, zeros=0}
Implemented Feedback: [ [ -1.72, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ... ], [ 0.0, -1.72, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ... ], [ 0.0, 0.0, -1.72, 0.0, 0.0, 0.0, 0.0, 0.0, ... ], [ 0.0, 0.0, 0.0, -1.72, 0.0, 0.0, 0.0, 0.0, ... ], [ 0.0, 0.0, 0.0, 0.0, -1.028, 0.0, 0.0, 0.0, ... ], [ 0.0, 0.0, 0.0, 0.0, 0.0, -1.028, 0.0, 0.0, ... ], [ 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, -1.028, 0.0, ... ], [ 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, -1.028, ... ], ... ]
Implemented Statistics: {meanExponent=-0.05604907135522113, negative=12, min=-1.72, max=0.0, mean=-0.08700000000000002, count=144, sum=-12.528000000000002, positive=0, stdDev=0.3287246399174982, zeros=132}
Measured Feedback: [ [ -1.7199999999983895, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ... ], [ 0.0, -1.719999999996169, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ... ], [ 0.0, 0.0, -1.72000000000061, 0.0, 0.0, 0.0, 0.0, 0.0, ... ], [ 0.0, 0.0, 0.0, -1.72000000000061, 0.0, 0.0, 0.0, 0.0, ... ], [ 0.0, 0.0, 0.0, 0.0, -1.028000000000695, 0.0, 0.0, 0.0, ... ], [ 0.0, 0.0, 0.0, 0.0, 0.0, -1.0280000000000011, 0.0, 0.0, ... ], [ 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, -1.028000000000695, 0.0, ... ], [ 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, -1.028000000000695, ... ], ... ]
Measured Statistics: {meanExponent=-0.056049071355259134, negative=12, min=-1.72000000000061, max=0.0, mean=-0.0869999999999835, count=144, sum=-12.527999999997624, positive=0, stdDev=0.32872463991739265, zeros=132}
Feedback Error: [ [ 1.610489519521252E-12, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ... ], [ 0.0, 3.830935568771565E-12, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ... ], [ 0.0, 0.0, -6.09956529729061E-13, 0.0, 0.0, 0.0, 0.0, 0.0, ... ], [ 0.0, 0.0, 0.0, -6.09956529729061E-13, 0.0, 0.0, 0.0, 0.0, ... ], [ 0.0, 0.0, 0.0, 0.0, -6.94999613415348E-13, 0.0, 0.0, 0.0, ... ], [ 0.0, 0.0, 0.0, 0.0, 0.0, -1.1102230246251565E-15, 0.0, 0.0, ... ], [ 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, -6.94999613415348E-13, 0.0, ... ], [ 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, -6.94999613415348E-13, ... ], ... ]
Error Statistics: {meanExponent=-12.662778798413193, negative=6, min=-6.94999613415348E-13, max=3.830935568771565E-12, mean=1.649760575064521E-14, count=144, sum=2.37565522809291E-12, positive=6, stdDev=3.6740597857632976E-13, zeros=132}

Returns

    {
      "absoluteTol" : {
        "count" : 144,
        "sum" : 8.987699473550492E-12,
        "min" : 0.0,
        "max" : 3.830935568771565E-12,
        "sumOfSquare" : 1.9477342668835322E-23,
        "standardDeviation" : 3.624413667516767E-13,
        "average" : 6.241457967743397E-14
      },
      "relativeTol" : {
        "count" : 12,
        "sum" : 3.263908460929286E-12,
        "min" : 5.399917434947257E-16,
        "max" : 1.1136440606906488E-12,
        "sumOfSquare" : 1.889530190099291E-24,
        "standardDeviation" : 2.8893078563551563E-13,
        "average" : 2.719923717441072E-13
      }
    }

Learning Validation

We validate the agreement between the implemented derivative of the internal weights apply finite difference estimations:

SingleDerivativeTester.java:133 executed in 0.02 seconds (0.000 gc):

        return testLearning(
            statistics,
            component.addRef(),
            RefUtil.addRef(inputPrototype),
            outputPrototype.addRef());
      },
      outputPrototype.addRef(),
      RefUtil.addRef(inputPrototype),
      component.addRef()));
Logging
Learning Gradient for weight setByCoord 0
Weights: [ -1.72, -1.028, -0.384 ]
Implemented Gradient: [ [ -0.852, 1.912, -1.688, -0.804, 0.0, 0.0, 0.0, 0.0, ... ], [ 0.0, 0.0, 0.0, 0.0, 1.108, 0.028, -0.712, 1.048, ... ], [ 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ... ] ]
Implemented Statistics: {meanExponent=-0.04822021070395558, negative=5, min=-1.688, max=1.912, mean=0.07911111111111112, count=36, sum=2.8480000000000003, positive=7, stdDev=0.7386787671157568, zeros=24}
Measured Gradient: [ [ -0.8520000000000749, 1.9120000000016901, -1.6879999999996897, -0.8040000000009151, 0.0, 0.0, 0.0, 0.0, ... ], [ 0.0, 0.0, 0.0, 0.0, 1.1079999999985546, 0.02799999999997249, -0.71200000000049, 1.0479999999990497, ... ], [ 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ... ] ]
Measured Statistics: {meanExponent=-0.048220210703964456, negative=5, min=-1.6879999999996897, max=1.9120000000016901, mean=0.07911111111108075, count=36, sum=2.847999999998907, positive=7, stdDev=0.7386787671158341, zeros=24}
Gradient Error: [ [ -7.494005416219807E-14, 1.6902035326893383E-12, 3.1019631308026874E-13, -9.15045816896054E-13, 0.0, 0.0, 0.0, 0.0, ... ], [ 0.0, 0.0, 0.0, 0.0, -1.4455103780619538E-12, -2.7509244882040207E-14, -4.900524430695441E-13, -9.50350909079134E-13, ... ], [ 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ... ] ]
Error Statistics: {meanExponent=-12.505188994436304, negative=6, min=-1.4455103780619538E-12, max=1.6902035326893383E-12, mean=-3.036700906165911E-14, count=36, sum=-1.093212326219728E-12, positive=6, stdDev=4.4673655319247273E-13, zeros=24}

Returns

    {
      "absoluteTol" : {
        "count" : 180,
        "sum" : 1.5701304839632613E-11,
        "min" : 0.0,
        "max" : 3.830935568771565E-12,
        "sumOfSquare" : 2.669518798395043E-23,
        "standardDeviation" : 3.750968131590816E-13,
        "average" : 8.72294713312923E-14
      },
      "relativeTol" : {
        "count" : 24,
        "sum" : 6.6182941297216096E-12,
        "min" : 5.399917434947257E-16,
        "max" : 1.1136440606906488E-12,
        "sumOfSquare" : 3.429971804256688E-24,
        "standardDeviation" : 2.5859363939961714E-13,
        "average" : 2.757622554050671E-13
      }
    }

Total Accuracy

The overall agreement accuracy between the implemented derivative and the finite difference estimations:

SingleDerivativeTester.java:148 executed in 0.00 seconds (0.000 gc):

    //log.info(String.format("Component: %s\nInputs: %s\noutput=%s", component, Arrays.toStream(inputPrototype), outputPrototype));
    log.info(RefString.format("Finite-Difference Derivative Accuracy:"));
    log.info(RefString.format("absoluteTol: %s", statistics.absoluteTol));
    log.info(RefString.format("relativeTol: %s", statistics.relativeTol));
Logging
Finite-Difference Derivative Accuracy:
absoluteTol: 8.7229e-14 +- 3.7510e-13 [0.0000e+00 - 3.8309e-12] (180#)
relativeTol: 2.7576e-13 +- 2.5859e-13 [5.3999e-16 - 1.1136e-12] (24#)

Frozen and Alive Status

SingleDerivativeTester.java:156 executed in 0.01 seconds (0.000 gc):

    testFrozen(component.addRef(), RefUtil.addRef(inputPrototype));
    testUnFrozen(component.addRef(), RefUtil.addRef(inputPrototype));

LayerTests.java:425 executed in 0.00 seconds (0.000 gc):

    throwException(exceptions.addRef());

Results

classdetailsresult
com.simiacryptus.mindseye.test.unit.SingleDerivativeTesterToleranceStatistics{absoluteTol=8.7229e-14 +- 3.7510e-13 [0.0000e+00 - 3.8309e-12] (180#), relativeTol=2.7576e-13 +- 2.5859e-13 [5.3999e-16 - 1.1136e-12] (24#)}OK
  {
    "result": "OK",
    "performance": {
      "execution_time": "0.180",
      "gc_time": "0.095"
    },
    "created_on": 1586739350778,
    "file_name": "derivativeTest",
    "report": {
      "simpleName": "Basic",
      "canonicalName": "com.simiacryptus.mindseye.layers.java.ImgBandScaleLayerTest.Basic",
      "link": "https://github.com/SimiaCryptus/mindseye-java/tree/93db34cedee48c0202777a2b25deddf1dfaf5731/src/test/java/com/simiacryptus/mindseye/layers/java/ImgBandScaleLayerTest.java",
      "javaDoc": ""
    },
    "archive": "s3://code.simiacrypt.us/tests/com/simiacryptus/mindseye/layers/java/ImgBandScaleLayer/Basic/derivativeTest/202004135550",
    "id": "bbb7cd15-7158-4573-b210-6d8153d9175c",
    "report_type": "Components",
    "display_name": "Derivative Validation",
    "target": {
      "simpleName": "ImgBandScaleLayer",
      "canonicalName": "com.simiacryptus.mindseye.layers.java.ImgBandScaleLayer",
      "link": "https://github.com/SimiaCryptus/mindseye-java/tree/93db34cedee48c0202777a2b25deddf1dfaf5731/src/main/java/com/simiacryptus/mindseye/layers/java/ImgBandScaleLayer.java",
      "javaDoc": ""
    }
  }