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 2750818069592927232

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: [
[ [ 0.039978680311163695 ], [ -0.0639127615952817 ], [ -0.29496888033103175 ] ],
[ [ 0.3363755443363323 ], [ 0.243037713855222 ], [ 0.7074198489653143 ] ]
]
Outputs Statistics: {meanExponent=-0.7267720728174915, negative=2, min=-0.29496888033103175, max=0.7074198489653143, mean=0.1613216909236198, count=6, sum=0.9679301455417189, positive=4, stdDev=0.31864304178207764, 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: [ [ 0.499200852560289, 0.0, 0.0, 0.0, 0.0, 0.0 ], [ 0.0, 0.44342574658621814, 0.0, 0.0, 0.0, 0.0 ], [ 0.0, 0.0, 0.4979575794526323, 0.0, 0.0, 0.0 ], [ 0.0, 0.0, 0.0, 0.4704663348220136, 0.0, 0.0 ], [ 0.0, 0.0, 0.0, 0.0, 0.4564966798181287, 0.0 ], [ 0.0, 0.0, 0.0, 0.0, 0.0, 0.24977857864494593 ] ]
Implemented Statistics: {meanExponent=-0.3713650105578275, negative=0, min=0.0, max=0.499200852560289, mean=0.07270349366345076, count=36, sum=2.6173257718842273, positive=6, stdDev=0.16630110212362303, zeros=30}
Measured Feedback: [ [ 0.4991998542758225, 0.0, 0.0, 0.0, 0.0, 0.0 ], [ 0.0, 0.44341828846050646, 0.0, 0.0, 0.0, 0.0 ], [ 0.0, 0.0, 0.4979591703369035, 0.0, 0.0, 0.0 ], [ 0.0, 0.0, 0.0, 0.47046061744770995, 0.0, 0.0 ], [ 0.0, 0.0, 0.0, 0.0, 0.45650341215175416, 0.0 ], [ 0.0, 0.0, 0.0, 0.0, 0.0, 0.24976974383195127 ] ]
Measured Statistics: {meanExponent=-0.3713685139070689, negative=0, min=0.0, max=0.4991998542758225, mean=0.07270308573624022, count=36, sum=2.617311086504648, positive=6, stdDev=0.1663004726108787, zeros=30}
Feedback Error: [ [ -9.98284466535715E-7, 0.0, 0.0, 0.0, 0.0, 0.0 ], [ 0.0, -7.458125711679031E-6, 0.0, 0.0, 0.0, 0.0 ], [ 0.0, 0.0, 1.590884271207571E-6, 0.0, 0.0, 0.0 ], [ 0.0, 0.0, 0.0, -5.717374303659817E-6, 0.0, 0.0 ], [ 0.0, 0.0, 0.0, 0.0, 6.732333625469877E-6, 0.0 ], [ 0.0, 0.0, 0.0, 0.0, 0.0, -8.834812994656938E-6 ] ]
Error Statistics: {meanExponent=-5.399152963788719, negative=4, min=-8.834812994656938E-6, max=6.732333625469877E-6, mean=-4.079272105515014E-7, count=36, sum=-1.4685379579854052E-5, positive=2, stdDev=2.4107811289839876E-6, zeros=30}

Returns

    {
      "absoluteTol" : {
        "count" : 36,
        "sum" : 3.133181537320895E-5,
        "min" : 0.0,
        "max" : 8.834812994656938E-6,
        "sumOfSquare" : 2.1521772939505105E-10,
        "standardDeviation" : 2.284906798292542E-6,
        "average" : 8.703282048113597E-7
      },
      "relativeTol" : {
        "count" : 6,
        "sum" : 4.214281028027537E-5,
        "min" : 9.99883573499497E-7,
        "max" : 1.7685602367457703E-5,
        "sumOfSquare" : 4.783511647476975E-10,
        "standardDeviation" : 5.512839886641664E-6,
        "average" : 7.023801713379228E-6
      }
    }

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" : 3.133181537320895E-5,
        "min" : 0.0,
        "max" : 8.834812994656938E-6,
        "sumOfSquare" : 2.1521772939505105E-10,
        "standardDeviation" : 2.284906798292542E-6,
        "average" : 8.703282048113597E-7
      },
      "relativeTol" : {
        "count" : 6,
        "sum" : 4.214281028027537E-5,
        "min" : 9.99883573499497E-7,
        "max" : 1.7685602367457703E-5,
        "sumOfSquare" : 4.783511647476975E-10,
        "standardDeviation" : 5.512839886641664E-6,
        "average" : 7.023801713379228E-6
      }
    }

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: 8.7033e-07 +- 2.2849e-06 [0.0000e+00 - 8.8348e-06] (36#)
relativeTol: 7.0238e-06 +- 5.5128e-06 [9.9988e-07 - 1.7686e-05] (6#)

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=8.7033e-07 +- 2.2849e-06 [0.0000e+00 - 8.8348e-06] (36#), relativeTol=7.0238e-06 +- 5.5128e-06 [9.9988e-07 - 1.7686e-05] (6#)}OK
  {
    "result": "OK",
    "performance": {
      "execution_time": "0.157",
      "gc_time": "0.101"
    },
    "created_on": 1586736749408,
    "file_name": "derivativeTest",
    "report": {
      "simpleName": "Basic",
      "canonicalName": "com.simiacryptus.mindseye.layers.java.SigmoidActivationLayerTest.Basic",
      "link": "https://github.com/SimiaCryptus/mindseye-java/tree/93db34cedee48c0202777a2b25deddf1dfaf5731/src/test/java/com/simiacryptus/mindseye/layers/java/SigmoidActivationLayerTest.java",
      "javaDoc": ""
    },
    "archive": "s3://code.simiacrypt.us/tests/com/simiacryptus/mindseye/layers/java/SigmoidActivationLayer/Basic/derivativeTest/202004131229",
    "id": "ee2a63cf-7c7b-48c2-ac10-71cf5fc07fe1",
    "report_type": "Components",
    "display_name": "Derivative Validation",
    "target": {
      "simpleName": "SigmoidActivationLayer",
      "canonicalName": "com.simiacryptus.mindseye.layers.java.SigmoidActivationLayer",
      "link": "https://github.com/SimiaCryptus/mindseye-java/tree/93db34cedee48c0202777a2b25deddf1dfaf5731/src/main/java/com/simiacryptus/mindseye/layers/java/SigmoidActivationLayer.java",
      "javaDoc": ""
    }
  }