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 957266035359328256

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)));

Inputs: [ 0.08, 0.7, -0.128 ],
[ 0.496 ]
Inputs Statistics: {meanExponent=-0.7148673344486438, negative=1, min=-0.128, max=0.7, mean=0.2173333333333333, count=3, sum=0.6519999999999999, positive=2, stdDev=0.3517018939701949, zeros=0},
{meanExponent=-0.30451832350980257, negative=0, min=0.496, max=0.496, mean=0.496, count=1, sum=0.496, positive=1, stdDev=0.0, zeros=0}
Output: [ 0.576, 1.196, 0.368 ]
Outputs Statistics: {meanExponent=-0.19866617275029277, negative=0, min=0.368, max=1.196, mean=0.7133333333333333, count=3, sum=2.1399999999999997, positive=3, stdDev=0.35170189397019497, zeros=0}

Feedback Validation

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

SingleDerivativeTester.java:117 executed in 0.02 seconds (0.000 gc):

        return testFeedback(
            statistics,
            component.addRef(),
            RefUtil.addRef(inputPrototype),
            outputPrototype.addRef());
      },
      outputPrototype.addRef(),
      RefUtil.addRef(inputPrototype),
      component.addRef()));

Feedback for input 0
Inputs Values: [ 0.08, 0.7, -0.128 ]
Value Statistics: {meanExponent=-0.7148673344486438, negative=1, min=-0.128, max=0.7, mean=0.2173333333333333, count=3, sum=0.6519999999999999, positive=2, stdDev=0.3517018939701949, zeros=0}
Implemented Feedback: [ [ 1.0, 0.0, 0.0 ], [ 0.0, 1.0, 0.0 ], [ 0.0, 0.0, 1.0 ] ]
Implemented Statistics: {meanExponent=0.0, negative=0, min=0.0, max=1.0, mean=0.3333333333333333, count=9, sum=3.0, positive=3, stdDev=0.4714045207910317, zeros=6}
Measured Feedback: [ [ 1.000000000001, 0.0, 0.0 ], [ 0.0, 0.9999999999998899, 0.0 ], [ 0.0, 0.0, 0.9999999999998899 ] ]
Measured Statistics: {meanExponent=1.1289060208443065E-13, negative=0, min=0.0, max=1.000000000001, mean=0.33333333333341997, count=9, sum=3.00000000000078, positive=3, stdDev=0.47140452079115425, zeros=6}
Feedback Error: [ [ 1.000088900582341E-12, 0.0, 0.0 ], [ 0.0, -1.1013412404281553E-13, 0.0 ], [ 0.0, 0.0, -1.1013412404281553E-13 ] ]
Error Statistics: {meanExponent=-12.63870586291913, negative=2, min=-1.1013412404281553E-13, max=1.000088900582341E-12, mean=8.664673916630111E-14, count=9, sum=7.7982065249671E-13, positive=1, stdDev=3.260654233861622E-13, zeros=6}
Feedback for input 1
Inputs Values: [ 0.496 ]
Value Statistics: {meanExponent=-0.30451832350980257, negative=0, min=0.496, max=0.496, mean=0.496, count=1, sum=0.496, positive=1, stdDev=0.0, zeros=0}
Implemented Feedback: [ [ 1.0, 1.0, 1.0 ] ]
Implemented Statistics: {meanExponent=0.0, negative=0, min=1.0, max=1.0, mean=1.0, count=3, sum=3.0, positive=3, stdDev=0.0, zeros=0}
Measured Feedback: [ [ 0.9999999999998899, 0.9999999999998899, 0.9999999999998899 ] ]
Measured Statistics: {meanExponent=-4.7830642341045674E-14, negative=0, min=0.9999999999998899, max=0.9999999999998899, mean=0.9999999999998899, count=3, sum=2.9999999999996696, positive=3, stdDev=0.0, zeros=0}
Feedback Error: [ [ -1.1013412404281553E-13, -1.1013412404281553E-13, -1.1013412404281553E-13 ] ]
Error Statistics: {meanExponent=-12.958078098036827, negative=3, min=-1.1013412404281553E-13, max=-1.1013412404281553E-13, mean=-1.1013412404281553E-13, count=3, sum=-3.304023721284466E-13, positive=0, stdDev=0.0, zeros=0}

Returns

    {
      "absoluteTol" : {
        "count" : 12,
        "sum" : 1.5507595207964187E-12,
        "min" : 0.0,
        "max" : 1.000088900582341E-12,
        "sumOfSquare" : 1.060825435461387E-24,
        "standardDeviation" : 2.6777180031330244E-13,
        "average" : 1.2922996006636822E-13
      },
      "relativeTol" : {
        "count" : 6,
        "sum" : 7.753797603979746E-13,
        "min" : 5.50670620214108E-14,
        "max" : 5.000444502909205E-13,
        "sumOfSquare" : 2.6520635886509837E-25,
        "standardDeviation" : 1.6583328143682353E-13,
        "average" : 1.292299600663291E-13
      }
    }

Learning Validation

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

SingleDerivativeTester.java:133 executed in 0.00 seconds (0.000 gc):

        return testLearning(
            statistics,
            component.addRef(),
            RefUtil.addRef(inputPrototype),
            outputPrototype.addRef());
      },
      outputPrototype.addRef(),
      RefUtil.addRef(inputPrototype),
      component.addRef()));

Returns

    {
      "absoluteTol" : {
        "count" : 12,
        "sum" : 1.5507595207964187E-12,
        "min" : 0.0,
        "max" : 1.000088900582341E-12,
        "sumOfSquare" : 1.060825435461387E-24,
        "standardDeviation" : 2.6777180031330244E-13,
        "average" : 1.2922996006636822E-13
      },
      "relativeTol" : {
        "count" : 6,
        "sum" : 7.753797603979746E-13,
        "min" : 5.50670620214108E-14,
        "max" : 5.000444502909205E-13,
        "sumOfSquare" : 2.6520635886509837E-25,
        "standardDeviation" : 1.6583328143682353E-13,
        "average" : 1.292299600663291E-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));

Finite-Difference Derivative Accuracy:
absoluteTol: 1.2923e-13 +- 2.6777e-13 [0.0000e+00 - 1.0001e-12] (12#)
relativeTol: 1.2923e-13 +- 1.6583e-13 [5.5067e-14 - 5.0004e-13] (6#)

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

  {
    "result": "OK",
    "performance": {
      "execution_time": "0.165",
      "gc_time": "0.101"
    },
    "created_on": 1586736332040,
    "file_name": "derivativeTest",
    "report": {
      "simpleName": "N1Test",
      "canonicalName": "com.simiacryptus.mindseye.layers.java.SumInputsLayerTest.N1Test",
      "link": "https://github.com/SimiaCryptus/mindseye-java/tree/93db34cedee48c0202777a2b25deddf1dfaf5731/src/test/java/com/simiacryptus/mindseye/layers/java/SumInputsLayerTest.java",
      "javaDoc": ""
    },
    "archive": "s3://code.simiacrypt.us/tests/com/simiacryptus/mindseye/layers/java/SumInputsLayer/N1Test/derivativeTest/202004130532",
    "id": "d6f6b64a-c026-40ed-94dc-cec09e6e1f40",
    "report_type": "Components",
    "display_name": "Derivative Validation",
    "target": {
      "simpleName": "SumInputsLayer",
      "canonicalName": "com.simiacryptus.mindseye.layers.java.SumInputsLayer",
      "link": "https://github.com/SimiaCryptus/mindseye-java/tree/93db34cedee48c0202777a2b25deddf1dfaf5731/src/main/java/com/simiacryptus/mindseye/layers/java/SumInputsLayer.java",
      "javaDoc": ""
    }
  }