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 2789266921523419136

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: [
[ [ 0.0064 ], [ 0.016384 ], [ 0.369664 ] ],
[ [ 0.48999999999999994 ], [ 0.24601599999999998 ], [ 3.1116960000000002 ] ]
]
Outputs Statistics: {meanExponent=-0.806239388929066, negative=0, min=0.0064, max=3.1116960000000002, mean=0.7066933333333334, count=6, sum=4.24016, positive=6, stdDev=1.0896286777887023, 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: [
[ [ 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: [ [ 0.16, 0.0, 0.0, 0.0, 0.0, 0.0 ], [ 0.0, 1.4, 0.0, 0.0, 0.0, 0.0 ], [ 0.0, 0.0, -0.256, 0.0, 0.0, 0.0 ], [ 0.0, 0.0, 0.0, 0.992, 0.0, 0.0 ], [ 0.0, 0.0, 0.0, 0.0, -1.216, 0.0 ], [ 0.0, 0.0, 0.0, 0.0, 0.0, 3.528 ] ]
Implemented Statistics: {meanExponent=-0.10208969880055178, negative=2, min=-1.216, max=3.528, mean=0.128, count=36, sum=4.608, positive=4, stdDev=0.6743477507109288, zeros=30}
Measured Feedback: [ [ 0.1601000000000033, 0.0, 0.0, 0.0, 0.0, 0.0 ], [ 0.0, 1.4000999999996822, 0.0, 0.0, 0.0, 0.0 ], [ 0.0, 0.0, -0.2558999999999548, 0.0, 0.0, 0.0 ], [ 0.0, 0.0, 0.0, 0.9920999999998847, 0.0, 0.0 ], [ 0.0, 0.0, 0.0, 0.0, -1.2159000000000475, 0.0 ], [ 0.0, 0.0, 0.0, 0.0, 0.0, 3.5280999999987017 ] ]
Measured Statistics: {meanExponent=-0.1020641887124527, negative=2, min=-1.2159000000000475, max=3.5280999999987017, mean=0.1280166666666186, count=36, sum=4.60859999999827, positive=4, stdDev=0.6743635693087523, zeros=30}
Feedback Error: [ [ 1.000000000032919E-4, 0.0, 0.0, 0.0, 0.0, 0.0 ], [ 0.0, 9.999999968224316E-5, 0.0, 0.0, 0.0, 0.0 ], [ 0.0, 0.0, 1.0000000004523057E-4, 0.0, 0.0, 0.0 ], [ 0.0, 0.0, 0.0, 9.999999988474784E-5, 0.0, 0.0 ], [ 0.0, 0.0, 0.0, 0.0, 9.999999995247144E-5, 0.0 ], [ 0.0, 0.0, 0.0, 0.0, 0.0, 9.999999870169418E-5 ] ]
Error Statistics: {meanExponent=-4.000000001252448, negative=0, min=0.0, max=1.0000000004523057E-4, mean=1.6666666618602196E-5, count=36, sum=5.999999982696791E-4, positive=6, stdDev=3.726779951752108E-5, zeros=30}

Returns

    {
      "absoluteTol" : {
        "count" : 36,
        "sum" : 5.999999982696791E-4,
        "min" : 0.0,
        "max" : 1.0000000004523057E-4,
        "sumOfSquare" : 5.999999965393582E-8,
        "standardDeviation" : 3.726779951752107E-5,
        "average" : 1.6666666618602196E-5
      },
      "relativeTol" : {
        "count" : 6,
        "sum" : 6.491589707006094E-4,
        "min" : 1.4172134564662147E-5,
        "max" : 3.1240237426832513E-4,
        "sumOfSquare" : 1.4146448284285943E-7,
        "standardDeviation" : 1.0895711794300877E-4,
        "average" : 1.0819316178343489E-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" : 5.999999982696791E-4,
        "min" : 0.0,
        "max" : 1.0000000004523057E-4,
        "sumOfSquare" : 5.999999965393582E-8,
        "standardDeviation" : 3.726779951752107E-5,
        "average" : 1.6666666618602196E-5
      },
      "relativeTol" : {
        "count" : 6,
        "sum" : 6.491589707006094E-4,
        "min" : 1.4172134564662147E-5,
        "max" : 3.1240237426832513E-4,
        "sumOfSquare" : 1.4146448284285943E-7,
        "standardDeviation" : 1.0895711794300877E-4,
        "average" : 1.0819316178343489E-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: 1.6667e-05 +- 3.7268e-05 [0.0000e+00 - 1.0000e-04] (36#)
relativeTol: 1.0819e-04 +- 1.0896e-04 [1.4172e-05 - 3.1240e-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

classdetailsresult
com.simiacryptus.mindseye.test.unit.SingleDerivativeTesterToleranceStatistics{absoluteTol=1.6667e-05 +- 3.7268e-05 [0.0000e+00 - 1.0000e-04] (36#), relativeTol=1.0819e-04 +- 1.0896e-04 [1.4172e-05 - 3.1240e-04] (6#)}OK
 
  {
    "result": "OK",
    "performance": {
      "execution_time": "0.165",
      "gc_time": "0.107"
    },
    "created_on": 1586736836957,
    "file_name": "derivativeTest",
    "report": {
      "simpleName": "Basic",
      "canonicalName": "com.simiacryptus.mindseye.layers.java.SqActivationLayerTest.Basic",
      "link": "https://github.com/SimiaCryptus/mindseye-java/tree/93db34cedee48c0202777a2b25deddf1dfaf5731/src/test/java/com/simiacryptus/mindseye/layers/java/SqActivationLayerTest.java",
      "javaDoc": ""
    },
    "archive": "s3://code.simiacrypt.us/tests/com/simiacryptus/mindseye/layers/java/SqActivationLayer/Basic/derivativeTest/202004131356",
    "id": "378b0806-67d9-4327-912d-22eaadd2f30d",
    "report_type": "Components",
    "display_name": "Derivative Validation",
    "target": {
      "simpleName": "SqActivationLayer",
      "canonicalName": "com.simiacryptus.mindseye.layers.java.SqActivationLayer",
      "link": "https://github.com/SimiaCryptus/mindseye-java/tree/93db34cedee48c0202777a2b25deddf1dfaf5731/src/main/java/com/simiacryptus/mindseye/layers/java/SqActivationLayer.java",
      "javaDoc": ""
    }
  }