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 4161868691138951168

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 ], [ -0.128 ], [ -0.608 ] ],
[ [ 0.7 ], [ 0.496 ], [ 1.764 ] ]
]
Inputs Statistics: {meanExponent=-0.403119694464533, negative=2, min=-0.608, max=1.764, mean=0.38399999999999995, count=6, sum=2.304, positive=4, stdDev=0.747821725636086, zeros=0}
Output: [
[ [ -1.462668401517611 ], [ -0.12991884994603553 ], [ -1.883482520938474 ] ],
[ [ 1.3549773019177707 ], [ -0.024985469998507592 ], [ 2.2477499629728355 ] ]
]
Outputs Statistics: {meanExponent=-0.260808802725839, negative=4, min=-1.883482520938474, max=2.2477499629728355, mean=0.016945337081663014, count=6, sum=0.10167202248997809, positive=2, stdDev=1.4486216988816794, 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 ], [ -0.128 ], [ -0.608 ] ],
[ [ 0.7 ], [ 0.496 ], [ 1.764 ] ]
]
Value Statistics: {meanExponent=-0.403119694464533, negative=2, min=-0.608, max=1.764, mean=0.38399999999999995, count=6, sum=2.304, positive=4, stdDev=0.747821725636086, zeros=0}
Implemented Feedback: [ [ 1.0, 0.0, 0.0, 0.0, 0.0, 0.0 ], [ 0.0, 1.0, 0.0, 0.0, 0.0, 0.0 ], [ 0.0, 0.0, 1.0, 0.0, 0.0, 0.0 ], [ 0.0, 0.0, 0.0, 1.0, 0.0, 0.0 ], [ 0.0, 0.0, 0.0, 0.0, 1.0, 0.0 ], [ 0.0, 0.0, 0.0, 0.0, 0.0, 1.0 ] ]
Implemented Statistics: {meanExponent=0.0, negative=0, min=0.0, max=1.0, mean=0.16666666666666666, count=36, sum=6.0, positive=6, stdDev=0.37267799624996495, zeros=30}
Measured Feedback: [ [ 0.9999999999998899, 0.0, 0.0, 0.0, 0.0, 0.0 ], [ 0.0, 0.9999999999976694, 0.0, 0.0, 0.0, 0.0 ], [ 0.0, 0.0, 0.9999999999998899, 0.0, 0.0, 0.0 ], [ 0.0, 0.0, 0.0, 0.9999999999998899, 0.0, 0.0 ], [ 0.0, 0.0, 0.0, 0.0, 1.0000000000021103, 0.0 ], [ 0.0, 0.0, 0.0, 0.0, 0.0, 1.0000000000021103 ] ]
Measured Statistics: {meanExponent=1.128906020839846E-13, negative=0, min=0.0, max=1.0000000000021103, mean=0.16666666666670998, count=36, sum=6.00000000000156, positive=6, stdDev=0.3726779962500618, zeros=30}
Feedback Error: [ [ -1.1013412404281553E-13, 0.0, 0.0, 0.0, 0.0, 0.0 ], [ 0.0, -2.3305801732931286E-12, 0.0, 0.0, 0.0, 0.0 ], [ 0.0, 0.0, -1.1013412404281553E-13, 0.0, 0.0, 0.0 ], [ 0.0, 0.0, 0.0, -1.1013412404281553E-13, 0.0, 0.0 ], [ 0.0, 0.0, 0.0, 0.0, 2.1103119252074976E-12, 0.0 ], [ 0.0, 0.0, 0.0, 0.0, 0.0, 2.1103119252074976E-12 ] ]
Error Statistics: {meanExponent=-12.309679490064285, negative=4, min=-2.3305801732931286E-12, max=2.1103119252074976E-12, mean=4.3323369583150554E-14, count=36, sum=1.55964130499342E-12, positive=2, stdDev=6.304155694340669E-13, zeros=30}

Returns

    {
      "absoluteTol" : {
        "count" : 36,
        "sum" : 6.88160639583657E-12,
        "min" : 0.0,
        "max" : 2.3305801732931286E-12,
        "sumOfSquare" : 1.4374825363329014E-23,
        "standardDeviation" : 6.022957663763935E-13,
        "average" : 1.9115573321768251E-13
      },
      "relativeTol" : {
        "count" : 6,
        "sum" : 3.4408031979174257E-12,
        "min" : 5.50670620214108E-14,
        "max" : 1.1652900866479222E-12,
        "sumOfSquare" : 3.59370634083072E-24,
        "standardDeviation" : 5.196984007358268E-13,
        "average" : 5.734671996529043E-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" : 36,
        "sum" : 6.88160639583657E-12,
        "min" : 0.0,
        "max" : 2.3305801732931286E-12,
        "sumOfSquare" : 1.4374825363329014E-23,
        "standardDeviation" : 6.022957663763935E-13,
        "average" : 1.9115573321768251E-13
      },
      "relativeTol" : {
        "count" : 6,
        "sum" : 3.4408031979174257E-12,
        "min" : 5.50670620214108E-14,
        "max" : 1.1652900866479222E-12,
        "sumOfSquare" : 3.59370634083072E-24,
        "standardDeviation" : 5.196984007358268E-13,
        "average" : 5.734671996529043E-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: 1.9116e-13 +- 6.0230e-13 [0.0000e+00 - 2.3306e-12] (36#)
relativeTol: 5.7347e-13 +- 5.1970e-13 [5.5067e-14 - 1.1653e-12] (6#)

Frozen and Alive Status

SingleDerivativeTester.java:156 executed in 0.00 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": "0.161",
      "gc_time": "0.107"
    },
    "created_on": 1586738881337,
    "file_name": "derivativeTest",
    "report": {
      "simpleName": "Basic",
      "canonicalName": "com.simiacryptus.mindseye.layers.java.GaussianNoiseLayerTest.Basic",
      "link": "https://github.com/SimiaCryptus/mindseye-java/tree/93db34cedee48c0202777a2b25deddf1dfaf5731/src/test/java/com/simiacryptus/mindseye/layers/java/GaussianNoiseLayerTest.java",
      "javaDoc": ""
    },
    "archive": "s3://code.simiacrypt.us/tests/com/simiacryptus/mindseye/layers/java/GaussianNoiseLayer/Basic/derivativeTest/202004134801",
    "id": "c9c1e550-895c-4c8d-9af6-c2d07c45d39f",
    "report_type": "Components",
    "display_name": "Derivative Validation",
    "target": {
      "simpleName": "GaussianNoiseLayer",
      "canonicalName": "com.simiacryptus.mindseye.layers.java.GaussianNoiseLayer",
      "link": "https://github.com/SimiaCryptus/mindseye-java/tree/93db34cedee48c0202777a2b25deddf1dfaf5731/src/main/java/com/simiacryptus/mindseye/layers/java/GaussianNoiseLayer.java",
      "javaDoc": ""
    }
  }