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 5390004519365971968

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: [ -1.616, 1.356, 1.512, 1.556, 1.032 ]
Inputs Statistics: {meanExponent=0.14518842541593246, negative=1, min=-1.616, max=1.556, mean=0.768, count=5, sum=3.84, positive=4, stdDev=1.206099166735472, zeros=0}
Output: [ -2.42, 2.4640000000000004, 1.54, 0.8440000000000001, 2.08 ]
Outputs Statistics: {meanExponent=0.2414765143795398, negative=1, min=-2.42, max=2.4640000000000004, mean=0.9016000000000002, count=5, sum=4.508000000000001, positive=4, stdDev=1.7477768278587515, 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()));
Logging
Feedback for input 0
Inputs Values: [ -1.616, 1.356, 1.512, 1.556, 1.032 ]
Value Statistics: {meanExponent=0.14518842541593246, negative=1, min=-1.616, max=1.556, mean=0.768, count=5, sum=3.84, positive=4, stdDev=1.206099166735472, zeros=0}
Implemented Feedback: [ [ 1.0, 0.0, 0.0, 0.0, 0.0 ], [ 0.0, 1.0, 0.0, 0.0, 0.0 ], [ 0.0, 0.0, 1.0, 0.0, 0.0 ], [ 0.0, 0.0, 0.0, 1.0, 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=0.2, count=25, sum=5.0, positive=5, stdDev=0.4, zeros=20}
Measured Feedback: [ [ 0.9999999999976694, 0.0, 0.0, 0.0, 0.0 ], [ 0.0, 0.9999999999976694, 0.0, 0.0, 0.0 ], [ 0.0, 0.0, 0.9999999999998899, 0.0, 0.0 ], [ 0.0, 0.0, 0.0, 0.9999999999998899, 0.0 ], [ 0.0, 0.0, 0.0, 0.0, 0.9999999999976694 ] ]
Measured Statistics: {meanExponent=-6.26427122273724E-13, negative=0, min=0.0, max=0.9999999999998899, mean=0.19999999999971152, count=25, sum=4.999999999992788, positive=5, stdDev=0.39999999999942304, zeros=20}
Feedback Error: [ [ -2.3305801732931286E-12, 0.0, 0.0, 0.0, 0.0 ], [ 0.0, -2.3305801732931286E-12, 0.0, 0.0, 0.0 ], [ 0.0, 0.0, -1.1013412404281553E-13, 0.0, 0.0 ], [ 0.0, 0.0, 0.0, -1.1013412404281553E-13, 0.0 ], [ 0.0, 0.0, 0.0, 0.0, -2.3305801732931286E-12 ] ]
Error Statistics: {meanExponent=-12.162752810711991, negative=5, min=-2.3305801732931286E-12, max=0.0, mean=-2.884803507186007E-13, count=25, sum=-7.212008767965017E-12, positive=0, stdDev=7.546800133627568E-13, zeros=20}

Returns

    {
      "absoluteTol" : {
        "count" : 25,
        "sum" : 7.212008767965017E-12,
        "min" : 0.0,
        "max" : 2.3305801732931286E-12,
        "sumOfSquare" : 1.6319070882998445E-23,
        "standardDeviation" : 7.546800133627568E-13,
        "average" : 2.884803507186007E-13
      },
      "relativeTol" : {
        "count" : 5,
        "sum" : 3.6060043839865882E-12,
        "min" : 5.50670620214108E-14,
        "max" : 1.1652900866479222E-12,
        "sumOfSquare" : 4.079767720759106E-24,
        "standardDeviation" : 5.438959822048711E-13,
        "average" : 7.212008767973177E-13
      }
    }

Learning Validation

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

SingleDerivativeTester.java:133 executed in 0.01 seconds (0.000 gc):

        return testLearning(
            statistics,
            component.addRef(),
            RefUtil.addRef(inputPrototype),
            outputPrototype.addRef());
      },
      outputPrototype.addRef(),
      RefUtil.addRef(inputPrototype),
      component.addRef()));
Logging
Learning Gradient for weight setByCoord 0
Weights: [ -0.804, 1.108, 0.028, -0.712, 1.048 ]
Implemented Gradient: [ [ 1.0, 0.0, 0.0, 0.0, 0.0 ], [ 0.0, 1.0, 0.0, 0.0, 0.0 ], [ 0.0, 0.0, 1.0, 0.0, 0.0 ], [ 0.0, 0.0, 0.0, 1.0, 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=0.2, count=25, sum=5.0, positive=5, stdDev=0.4, zeros=20}
Measured Gradient: [ [ 0.9999999999976694, 0.0, 0.0, 0.0, 0.0 ], [ 0.0, 0.9999999999976694, 0.0, 0.0, 0.0 ], [ 0.0, 0.0, 0.9999999999998899, 0.0, 0.0 ], [ 0.0, 0.0, 0.0, 0.9999999999998899, 0.0 ], [ 0.0, 0.0, 0.0, 0.0, 0.9999999999976694 ] ]
Measured Statistics: {meanExponent=-6.26427122273724E-13, negative=0, min=0.0, max=0.9999999999998899, mean=0.19999999999971152, count=25, sum=4.999999999992788, positive=5, stdDev=0.39999999999942304, zeros=20}
Gradient Error: [ [ -2.3305801732931286E-12, 0.0, 0.0, 0.0, 0.0 ], [ 0.0, -2.3305801732931286E-12, 0.0, 0.0, 0.0 ], [ 0.0, 0.0, -1.1013412404281553E-13, 0.0, 0.0 ], [ 0.0, 0.0, 0.0, -1.1013412404281553E-13, 0.0 ], [ 0.0, 0.0, 0.0, 0.0, -2.3305801732931286E-12 ] ]
Error Statistics: {meanExponent=-12.162752810711991, negative=5, min=-2.3305801732931286E-12, max=0.0, mean=-2.884803507186007E-13, count=25, sum=-7.212008767965017E-12, positive=0, stdDev=7.546800133627568E-13, zeros=20}

Returns

    {
      "absoluteTol" : {
        "count" : 50,
        "sum" : 1.4424017535930034E-11,
        "min" : 0.0,
        "max" : 2.3305801732931286E-12,
        "sumOfSquare" : 3.263814176599689E-23,
        "standardDeviation" : 7.546800133627568E-13,
        "average" : 2.884803507186007E-13
      },
      "relativeTol" : {
        "count" : 10,
        "sum" : 7.2120087679731764E-12,
        "min" : 5.50670620214108E-14,
        "max" : 1.1652900866479222E-12,
        "sumOfSquare" : 8.159535441518212E-24,
        "standardDeviation" : 5.438959822048711E-13,
        "average" : 7.212008767973177E-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));
Logging
Finite-Difference Derivative Accuracy:
absoluteTol: 2.8848e-13 +- 7.5468e-13 [0.0000e+00 - 2.3306e-12] (50#)
relativeTol: 7.2120e-13 +- 5.4390e-13 [5.5067e-14 - 1.1653e-12] (10#)

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=2.8848e-13 +- 7.5468e-13 [0.0000e+00 - 2.3306e-12] (50#), relativeTol=7.2120e-13 +- 5.4390e-13 [5.5067e-14 - 1.1653e-12] (10#)}OK
  {
    "result": "OK",
    "performance": {
      "execution_time": "0.169",
      "gc_time": "0.097"
    },
    "created_on": 1586735328627,
    "file_name": "derivativeTest",
    "report": {
      "simpleName": "Basic",
      "canonicalName": "com.simiacryptus.mindseye.layers.java.BiasLayerTest.Basic",
      "link": "https://github.com/SimiaCryptus/mindseye-java/tree/93db34cedee48c0202777a2b25deddf1dfaf5731/src/test/java/com/simiacryptus/mindseye/layers/java/BiasLayerTest.java",
      "javaDoc": ""
    },
    "archive": "s3://code.simiacrypt.us/tests/com/simiacryptus/mindseye/layers/java/BiasLayer/Basic/derivativeTest/202004124848",
    "id": "6348c50f-a01b-408d-9e17-01a85886c89d",
    "report_type": "Components",
    "display_name": "Derivative Validation",
    "target": {
      "simpleName": "BiasLayer",
      "canonicalName": "com.simiacryptus.mindseye.layers.java.BiasLayer",
      "link": "https://github.com/SimiaCryptus/mindseye-java/tree/93db34cedee48c0202777a2b25deddf1dfaf5731/src/main/java/com/simiacryptus/mindseye/layers/java/BiasLayer.java",
      "javaDoc": ""
    }
  }