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 7009024570784574464

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: [
[ [ 0.7 ], [ -0.608 ], [ 1.524 ] ],
[ [ 0.496 ], [ 1.764 ], [ 1.208 ] ]
]
Inputs Statistics: {meanExponent=-0.027327703689719207, negative=1, min=-0.608, max=1.764, mean=0.8473333333333333, count=6, sum=5.084, positive=5, stdDev=0.7843652777175242, zeros=0}
Output: [
[ [ 1.4285714285714286 ], [ -1.6447368421052633 ], [ 0.6561679790026247 ] ],
[ [ 2.0161290322580645 ], [ 0.5668934240362812 ], [ 0.8278145695364238 ] ]
]
Outputs Statistics: {meanExponent=0.02732770368971919, negative=1, min=-1.6447368421052633, max=2.0161290322580645, mean=0.6418065985499265, count=6, sum=3.850839591299559, positive=5, stdDev=1.1384535676221956, 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: [
[ [ 0.7 ], [ -0.608 ], [ 1.524 ] ],
[ [ 0.496 ], [ 1.764 ], [ 1.208 ] ]
]
Value Statistics: {meanExponent=-0.027327703689719207, negative=1, min=-0.608, max=1.764, mean=0.8473333333333333, count=6, sum=5.084, positive=5, stdDev=0.7843652777175242, zeros=0}
Implemented Feedback: [ [ -2.0408163265306123, 0.0, 0.0, 0.0, 0.0, 0.0 ], [ 0.0, -4.064776274713839, 0.0, 0.0, 0.0, 0.0 ], [ 0.0, 0.0, -2.7051592797783934, 0.0, 0.0, 0.0 ], [ 0.0, 0.0, 0.0, -0.3213681542155789, 0.0, 0.0 ], [ 0.0, 0.0, 0.0, 0.0, -0.4305564166683889, 0.0 ], [ 0.0, 0.0, 0.0, 0.0, 0.0, -0.6852769615367748 ] ]
Implemented Statistics: {meanExponent=0.05465540737943838, negative=6, min=-4.064776274713839, max=0.0, mean=-0.2846653725956553, count=36, sum=-10.24795341344359, positive=0, stdDev=0.8473200813085149, zeros=30}
Measured Feedback: [ [ -2.0405248229837802, 0.0, 0.0, 0.0, 0.0, 0.0 ], [ 0.0, -4.063956928557211, 0.0, 0.0, 0.0, 0.0 ], [ 0.0, 0.0, -2.7056042804818325, 0.0, 0.0, 0.0 ], [ 0.0, 0.0, 0.0, -0.3213499370990913, 0.0, 0.0 ], [ 0.0, 0.0, 0.0, 0.0, -0.4305281667893457, 0.0 ], [ 0.0, 0.0, 0.0, 0.0, 0.0, -0.6852202380069894 ] ]
Measured Statistics: {meanExponent=0.05462753778174606, negative=6, min=-4.063956928557211, max=0.0, mean=-0.2846440103866181, count=36, sum=-10.24718437391825, positive=0, stdDev=0.8472361830719296, zeros=30}
Feedback Error: [ [ 2.915035468320504E-4, 0.0, 0.0, 0.0, 0.0, 0.0 ], [ 0.0, 8.193461566285976E-4, 0.0, 0.0, 0.0, 0.0 ], [ 0.0, 0.0, -4.4500070343911347E-4, 0.0, 0.0, 0.0 ], [ 0.0, 0.0, 0.0, 1.8217116487584395E-5, 0.0, 0.0 ], [ 0.0, 0.0, 0.0, 0.0, 2.8249879043207304E-5, 0.0 ], [ 0.0, 0.0, 0.0, 0.0, 0.0, 5.672352978536299E-5 ] ]
Error Statistics: {meanExponent=-3.9180447603326303, negative=1, min=-4.4500070343911347E-4, max=8.193461566285976E-4, mean=2.1362209037158032E-5, count=36, sum=7.690395253376892E-4, positive=5, stdDev=1.6178236092143753E-4, zeros=30}

Returns

    {
      "absoluteTol" : {
        "count" : 36,
        "sum" : 0.001659040932215916,
        "min" : 0.0,
        "max" : 8.193461566285976E-4,
        "sumOfSquare" : 9.586755460894145E-7,
        "standardDeviation" : 1.5654423615644504E-4,
        "average" : 4.6084470339331E-5
      },
      "relativeTol" : {
        "count" : 6,
        "sum" : 3.570035711639884E-4,
        "min" : 2.8343867605035917E-5,
        "max" : 1.0079629090285407E-4,
        "sumOfSquare" : 2.5617960749860618E-8,
        "standardDeviation" : 2.7006282538267707E-5,
        "average" : 5.950059519399807E-5
      }
    }

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" : 0.001659040932215916,
        "min" : 0.0,
        "max" : 8.193461566285976E-4,
        "sumOfSquare" : 9.586755460894145E-7,
        "standardDeviation" : 1.5654423615644504E-4,
        "average" : 4.6084470339331E-5
      },
      "relativeTol" : {
        "count" : 6,
        "sum" : 3.570035711639884E-4,
        "min" : 2.8343867605035917E-5,
        "max" : 1.0079629090285407E-4,
        "sumOfSquare" : 2.5617960749860618E-8,
        "standardDeviation" : 2.7006282538267707E-5,
        "average" : 5.950059519399807E-5
      }
    }

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: 4.6084e-05 +- 1.5654e-04 [0.0000e+00 - 8.1935e-04] (36#)
relativeTol: 5.9501e-05 +- 2.7006e-05 [2.8344e-05 - 1.0080e-04] (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": "0.167",
      "gc_time": "0.111"
    },
    "created_on": 1586737227269,
    "file_name": "derivativeTest",
    "report": {
      "simpleName": "InvPowerTest",
      "canonicalName": "com.simiacryptus.mindseye.layers.java.NthPowerActivationLayerTest.InvPowerTest",
      "link": "https://github.com/SimiaCryptus/mindseye-java/tree/93db34cedee48c0202777a2b25deddf1dfaf5731/src/test/java/com/simiacryptus/mindseye/layers/java/NthPowerActivationLayerTest.java",
      "javaDoc": ""
    },
    "archive": "s3://code.simiacrypt.us/tests/com/simiacryptus/mindseye/layers/java/NthPowerActivationLayer/InvPowerTest/derivativeTest/202004132027",
    "id": "dcf6c749-5a3f-4429-b760-ea486a8460d4",
    "report_type": "Components",
    "display_name": "Derivative Validation",
    "target": {
      "simpleName": "NthPowerActivationLayer",
      "canonicalName": "com.simiacryptus.mindseye.layers.java.NthPowerActivationLayer",
      "link": "https://github.com/SimiaCryptus/mindseye-java/tree/93db34cedee48c0202777a2b25deddf1dfaf5731/src/main/java/com/simiacryptus/mindseye/layers/java/NthPowerActivationLayer.java",
      "javaDoc": ""
    }
  }