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 7246901800012200960

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: [
[ [ 0.08 ], [ -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: [
[ [ 3.5805979730981933E-4 ], [ 0.0 ], [ 0.0 ] ],
[ [ 0.32610776537785674 ], [ 0.11049114445659096 ], [ 5.948372135444591 ] ]
]
Outputs Statistics: {meanExponent=-1.0287394238766356, negative=0, min=0.0, max=5.948372135444591, mean=1.0642215175127248, count=6, sum=6.385329105076348, positive=4, stdDev=2.187322607190543, zeros=2}

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()));
Logging
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: [ [ 0.014060975359679734, 0.0, 0.0, 0.0, 0.0, 0.0 ], [ 0.0, 1.4635682285566551, 0.0, 0.0, 0.0, 0.0 ], [ 0.0, 0.0, 0.0, 0.0, 0.0, 0.0 ], [ 0.0, 0.0, 0.0, 0.6998350155474895, 0.0, 0.0 ], [ 0.0, 0.0, 0.0, 0.0, 0.0, 0.0 ], [ 0.0, 0.0, 0.0, 0.0, 0.0, 10.59374274463206 ] ]
Implemented Statistics: {meanExponent=-0.20413162225555154, negative=0, min=0.0, max=10.59374274463206, mean=0.3547557490026635, count=36, sum=12.771206964095885, positive=4, stdDev=1.750624689828077, zeros=32}
Measured Feedback: [ [ 0.014079804863285028, 0.0, 0.0, 0.0, 0.0, 0.0 ], [ 0.0, 1.4637921240817642, 0.0, 0.0, 0.0, 0.0 ], [ 0.0, 0.0, 0.0, 0.0, 0.0, 0.0 ], [ 0.0, 0.0, 0.0, 0.6999861119702522, 0.0, 0.0 ], [ 0.0, 0.0, 0.0, 0.0, 0.0, 0.0 ], [ 0.0, 0.0, 0.0, 0.0, 0.0, 10.594385827706887 ] ]
Measured Statistics: {meanExponent=-0.2039396873712781, negative=0, min=0.0, max=10.594385827706887, mean=0.3547845519061719, count=36, sum=12.772243868622189, positive=4, stdDev=1.7507338336706688, zeros=32}
Feedback Error: [ [ 1.882950360529434E-5, 0.0, 0.0, 0.0, 0.0, 0.0 ], [ 0.0, 2.2389552510904132E-4, 0.0, 0.0, 0.0, 0.0 ], [ 0.0, 0.0, 0.0, 0.0, 0.0, 0.0 ], [ 0.0, 0.0, 0.0, 1.5109642276267543E-4, 0.0, 0.0 ], [ 0.0, 0.0, 0.0, 0.0, 0.0, 0.0 ], [ 0.0, 0.0, 0.0, 0.0, 0.0, 6.430830748271887E-4 ] ]
Error Statistics: {meanExponent=-3.846898613313185, negative=0, min=0.0, max=6.430830748271887E-4, mean=2.8802903508449995E-5, count=36, sum=0.0010369045263041998, positive=4, stdDev=1.1267010170455467E-4, zeros=32}

Returns

    {
      "absoluteTol" : {
        "count" : 36,
        "sum" : 0.0010369045263041998,
        "min" : 0.0,
        "max" : 6.430830748271887E-4,
        "sumOfSquare" : 4.868697264707439E-7,
        "standardDeviation" : 1.1267010170455467E-4,
        "average" : 2.8802903508449995E-5
      },
      "relativeTol" : {
        "count" : 4,
        "sum" : 8.838927062705052E-4,
        "min" : 3.035110310151375E-5,
        "max" : 6.69118036390768E-4,
        "sumOfSquare" : 4.661409035469769E-7,
        "standardDeviation" : 2.602039221924928E-4,
        "average" : 2.209731765676263E-4
      }
    }

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" : 0.0010369045263041998,
        "min" : 0.0,
        "max" : 6.430830748271887E-4,
        "sumOfSquare" : 4.868697264707439E-7,
        "standardDeviation" : 1.1267010170455467E-4,
        "average" : 2.8802903508449995E-5
      },
      "relativeTol" : {
        "count" : 4,
        "sum" : 8.838927062705052E-4,
        "min" : 3.035110310151375E-5,
        "max" : 6.69118036390768E-4,
        "sumOfSquare" : 4.661409035469769E-7,
        "standardDeviation" : 2.602039221924928E-4,
        "average" : 2.209731765676263E-4
      }
    }

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: 2.8803e-05 +- 1.1267e-04 [0.0000e+00 - 6.4308e-04] (36#)
relativeTol: 2.2097e-04 +- 2.6020e-04 [3.0351e-05 - 6.6912e-04] (4#)

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

classdetailsresult
com.simiacryptus.mindseye.test.unit.SingleDerivativeTesterToleranceStatistics{absoluteTol=2.8803e-05 +- 1.1267e-04 [0.0000e+00 - 6.4308e-04] (36#), relativeTol=2.2097e-04 +- 2.6020e-04 [3.0351e-05 - 6.6912e-04] (4#)}OK
  {
    "result": "OK",
    "performance": {
      "execution_time": "0.162",
      "gc_time": "0.107"
    },
    "created_on": 1586737483835,
    "file_name": "derivativeTest",
    "report": {
      "simpleName": "NthPowerTest",
      "canonicalName": "com.simiacryptus.mindseye.layers.java.NthPowerActivationLayerTest.NthPowerTest",
      "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/NthPowerTest/derivativeTest/202004132443",
    "id": "8f481fc3-b7a9-4080-92d1-8804e41e266e",
    "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": ""
    }
  }