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 7318081207172371456

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: [
[ [ 0.7 ], [ -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: [
[ [ 1.1952286093343938 ], [ 0.0 ], [ 0.810041961260418 ] ],
[ [ 1.419904585617662 ], [ 0.7529232524210427 ], [ 0.9098431565585487 ] ]
]
Outputs Statistics: {meanExponent=-0.005213019858894985, negative=0, min=0.0, max=1.419904585617662, mean=0.8479902608653442, count=6, sum=5.087941565192065, positive=5, stdDev=0.4436687086610864, zeros=1}

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: [
[ [ 0.7 ], [ -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: [ [ -0.8537347209531384, 0.0, 0.0, 0.0, 0.0, 0.0 ], [ 0.0, -1.4313554290500625, 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, -0.21341362030074906, 0.0, 0.0 ], [ 0.0, 0.0, 0.0, 0.0, -0.26576179831378544, 0.0 ], [ 0.0, 0.0, 0.0, 0.0, 0.0, -0.37659071049608805 ] ]
Implemented Statistics: {meanExponent=-0.31666905524066624, negative=5, min=-1.4313554290500625, max=0.0, mean=-0.08724600775316176, count=36, sum=-3.140856279113823, positive=0, stdDev=0.2769681809220494, zeros=31}
Measured Feedback: [ [ -0.8536432602657129, 0.0, 0.0, 0.0, 0.0, 0.0 ], [ 0.0, -1.4311390306143146, 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, -0.21340454702145628, 0.0, 0.0 ], [ 0.0, 0.0, 0.0, 0.0, -0.26574872019957674, 0.0 ], [ 0.0, 0.0, 0.0, 0.0, 0.0, -0.37656733106294915 ] ]
Measured Statistics: {meanExponent=-0.31670485346650357, negative=5, min=-1.4311390306143146, max=0.0, mean=-0.08723619136566693, count=36, sum=-3.1405028891640097, positive=0, stdDev=0.27693095145833296, zeros=31}
Feedback Error: [ [ 9.146068742549307E-5, 0.0, 0.0, 0.0, 0.0, 0.0 ], [ 0.0, 2.1639843574794426E-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, 9.073279292781677E-6, 0.0, 0.0 ], [ 0.0, 0.0, 0.0, 0.0, 1.3078114208697755E-5, 0.0 ], [ 0.0, 0.0, 0.0, 0.0, 0.0, 2.337943313890367E-5 ] ]
Error Statistics: {meanExponent=-4.452073607900388, negative=0, min=0.0, max=2.1639843574794426E-4, mean=9.816387494828346E-6, count=36, sum=3.5338994981382044E-4, positive=5, stdDev=3.81969628725125E-5, zeros=31}

Returns

    {
      "absoluteTol" : {
        "count" : 36,
        "sum" : 3.5338994981382044E-4,
        "min" : 0.0,
        "max" : 2.1639843574794426E-4,
        "sumOfSquare" : 5.5993299700777935E-8,
        "standardDeviation" : 3.81969628725125E-5,
        "average" : 9.816387494828346E-6
      },
      "relativeTol" : {
        "count" : 5,
        "sum" : 2.0607115270858146E-4,
        "min" : 2.125795007674135E-5,
        "max" : 7.559785313027877E-5,
        "sumOfSquare" : 1.0605489139779447E-8,
        "standardDeviation" : 2.0554440610489717E-5,
        "average" : 4.1214230541716294E-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" : 3.5338994981382044E-4,
        "min" : 0.0,
        "max" : 2.1639843574794426E-4,
        "sumOfSquare" : 5.5993299700777935E-8,
        "standardDeviation" : 3.81969628725125E-5,
        "average" : 9.816387494828346E-6
      },
      "relativeTol" : {
        "count" : 5,
        "sum" : 2.0607115270858146E-4,
        "min" : 2.125795007674135E-5,
        "max" : 7.559785313027877E-5,
        "sumOfSquare" : 1.0605489139779447E-8,
        "standardDeviation" : 2.0554440610489717E-5,
        "average" : 4.1214230541716294E-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));
Logging
Finite-Difference Derivative Accuracy:
absoluteTol: 9.8164e-06 +- 3.8197e-05 [0.0000e+00 - 2.1640e-04] (36#)
relativeTol: 4.1214e-05 +- 2.0554e-05 [2.1258e-05 - 7.5598e-05] (5#)

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=9.8164e-06 +- 3.8197e-05 [0.0000e+00 - 2.1640e-04] (36#), relativeTol=4.1214e-05 +- 2.0554e-05 [2.1258e-05 - 7.5598e-05] (5#)}OK
  {
    "result": "OK",
    "performance": {
      "execution_time": "0.179",
      "gc_time": "0.116"
    },
    "created_on": 1586737355079,
    "file_name": "derivativeTest",
    "report": {
      "simpleName": "InvSqrtPowerTest",
      "canonicalName": "com.simiacryptus.mindseye.layers.java.NthPowerActivationLayerTest.InvSqrtPowerTest",
      "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/InvSqrtPowerTest/derivativeTest/202004132235",
    "id": "97358db8-8310-409b-a353-4e0e83be33d0",
    "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": ""
    }
  }