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 389194617575093248

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, 0.7, -0.128, 0.496, -0.608, 1.764, 0.048, 1.524 ],
[ 1.208, -1.72, -1.028, -0.384, -0.852, 1.912 ]
Inputs Statistics: {meanExponent=-0.44431149530100367, negative=2, min=-0.608, max=1.764, mean=0.4845, count=8, sum=3.876, positive=6, stdDev=0.7654343538148781, zeros=0},
{meanExponent=0.0209745338210385, negative=4, min=-1.72, max=1.912, mean=-0.144, count=6, sum=-0.8639999999999999, positive=2, stdDev=1.2831689938066093, zeros=0}
Output: [ 0.08, 0.7, -0.128, 0.496, -0.608, 1.764, 0.048, 1.524, ... ]
Outputs Statistics: {meanExponent=-0.2449031971058427, negative=6, min=-1.72, max=1.912, mean=0.21514285714285714, count=14, sum=3.012, positive=8, stdDev=1.0663884883865098, zeros=0}

Feedback Validation

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

SingleDerivativeTester.java:117 executed in 0.05 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, 0.7, -0.128, 0.496, -0.608, 1.764, 0.048, 1.524 ]
Value Statistics: {meanExponent=-0.44431149530100367, negative=2, min=-0.608, max=1.764, mean=0.4845, count=8, sum=3.876, positive=6, stdDev=0.7654343538148781, zeros=0}
Implemented Feedback: [ [ 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ... ], [ 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ... ], [ 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, ... ], [ 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, ... ], [ 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, ... ], [ 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, ... ], [ 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, ... ], [ 0.0, 0.0, 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=0.07142857142857142, count=112, sum=8.0, positive=8, stdDev=0.25753937681885636, zeros=104}
Measured Feedback: [ [ 1.0000000000000286, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ... ], [ 0.0, 0.9999999999998899, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ... ], [ 0.0, 0.0, 0.9999999999998899, 0.0, 0.0, 0.0, 0.0, 0.0, ... ], [ 0.0, 0.0, 0.0, 0.9999999999998899, 0.0, 0.0, 0.0, 0.0, ... ], [ 0.0, 0.0, 0.0, 0.0, 0.9999999999998899, 0.0, 0.0, 0.0, ... ], [ 0.0, 0.0, 0.0, 0.0, 0.0, 0.9999999999998899, 0.0, 0.0, ... ], [ 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0000000000000286, 0.0, ... ], [ 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.9999999999998899, ... ] ]
Measured Statistics: {meanExponent=-3.276302567614995E-14, negative=0, min=0.0, max=1.0000000000000286, mean=0.07142857142856604, count=112, sum=7.999999999999396, positive=8, stdDev=0.25753937681883693, zeros=104}
Feedback Error: [ [ 2.864375403532904E-14, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ... ], [ 0.0, -1.1013412404281553E-13, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ... ], [ 0.0, 0.0, -1.1013412404281553E-13, 0.0, 0.0, 0.0, 0.0, 0.0, ... ], [ 0.0, 0.0, 0.0, -1.1013412404281553E-13, 0.0, 0.0, 0.0, 0.0, ... ], [ 0.0, 0.0, 0.0, 0.0, -1.1013412404281553E-13, 0.0, 0.0, 0.0, ... ], [ 0.0, 0.0, 0.0, 0.0, 0.0, -1.1013412404281553E-13, 0.0, 0.0, ... ], [ 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 2.864375403532904E-14, 0.0, ... ], [ 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, -1.1013412404281553E-13, ... ] ]
Error Statistics: {meanExponent=-13.104301089584562, negative=6, min=-1.1013412404281553E-13, max=2.864375403532904E-14, mean=-5.388546751662813E-15, count=112, sum=-6.035172361862351E-13, positive=2, stdDev=2.520735439026509E-14, zeros=104}
Feedback for input 1
Inputs Values: [ 1.208, -1.72, -1.028, -0.384, -0.852, 1.912 ]
Value Statistics: {meanExponent=0.0209745338210385, negative=4, min=-1.72, max=1.912, mean=-0.144, count=6, sum=-0.8639999999999999, positive=2, stdDev=1.2831689938066093, zeros=0}
Implemented Feedback: [ [ 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, 0.0, 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, 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, ... ], [ 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ... ] ]
Implemented Statistics: {meanExponent=0.0, negative=0, min=0.0, max=1.0, mean=0.07142857142857142, count=84, sum=6.0, positive=6, stdDev=0.25753937681885636, zeros=78}
Measured Feedback: [ [ 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, 0.0, 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, 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, ... ], [ 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ... ] ]
Measured Statistics: {meanExponent=-4.7830642341045674E-14, negative=0, min=0.0, max=0.9999999999998899, mean=0.07142857142856356, count=84, sum=5.999999999999339, positive=6, stdDev=0.257539376818828, zeros=78}
Feedback Error: [ [ 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, 0.0, 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, 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, ... ], [ 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ... ] ]
Error Statistics: {meanExponent=-12.958078098036824, negative=6, min=-1.1013412404281553E-13, max=0.0, mean=-7.866723145915395E-15, count=84, sum=-6.608047442568932E-13, positive=0, stdDev=2.836387367247734E-14, zeros=78}

Returns

    {
      "absoluteTol" : {
        "count" : 196,
        "sum" : 1.3788969965844444E-12,
        "min" : 0.0,
        "max" : 1.1013412404281553E-13,
        "sumOfSquare" : 1.4719523263461219E-25,
        "standardDeviation" : 2.6485886883557445E-14,
        "average" : 7.0351887580839E-15
      },
      "relativeTol" : {
        "count" : 14,
        "sum" : 6.894484982922583E-13,
        "min" : 1.4321877017664317E-14,
        "max" : 5.50670620214108E-14,
        "sumOfSquare" : 3.679880815865705E-26,
        "standardDeviation" : 1.4257844676354286E-14,
        "average" : 4.9246321306589874E-14
      }
    }

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" : 196,
        "sum" : 1.3788969965844444E-12,
        "min" : 0.0,
        "max" : 1.1013412404281553E-13,
        "sumOfSquare" : 1.4719523263461219E-25,
        "standardDeviation" : 2.6485886883557445E-14,
        "average" : 7.0351887580839E-15
      },
      "relativeTol" : {
        "count" : 14,
        "sum" : 6.894484982922583E-13,
        "min" : 1.4321877017664317E-14,
        "max" : 5.50670620214108E-14,
        "sumOfSquare" : 3.679880815865705E-26,
        "standardDeviation" : 1.4257844676354286E-14,
        "average" : 4.9246321306589874E-14
      }
    }

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: 7.0352e-15 +- 2.6486e-14 [0.0000e+00 - 1.1013e-13] (196#)
relativeTol: 4.9246e-14 +- 1.4258e-14 [1.4322e-14 - 5.5067e-14] (14#)

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=7.0352e-15 +- 2.6486e-14 [0.0000e+00 - 1.1013e-13] (196#), relativeTol=4.9246e-14 +- 1.4258e-14 [1.4322e-14 - 5.5067e-14] (14#)}OK
 
  {
    "result": "OK",
    "performance": {
      "execution_time": "0.196",
      "gc_time": "0.108"
    },
    "created_on": 1586738345298,
    "file_name": "derivativeTest",
    "report": {
      "simpleName": "Basic",
      "canonicalName": "com.simiacryptus.mindseye.layers.java.TensorConcatLayerTest.Basic",
      "link": "https://github.com/SimiaCryptus/mindseye-java/tree/93db34cedee48c0202777a2b25deddf1dfaf5731/src/test/java/com/simiacryptus/mindseye/layers/java/TensorConcatLayerTest.java",
      "javaDoc": ""
    },
    "archive": "s3://code.simiacrypt.us/tests/com/simiacryptus/mindseye/layers/java/TensorConcatLayer/Basic/derivativeTest/202004133905",
    "id": "b73a725d-6a0e-4151-bb48-2d707aaf2945",
    "report_type": "Components",
    "display_name": "Derivative Validation",
    "target": {
      "simpleName": "TensorConcatLayer",
      "canonicalName": "com.simiacryptus.mindseye.layers.java.TensorConcatLayer",
      "link": "https://github.com/SimiaCryptus/mindseye-java/tree/93db34cedee48c0202777a2b25deddf1dfaf5731/src/main/java/com/simiacryptus/mindseye/layers/java/TensorConcatLayer.java",
      "javaDoc": ""
    }
  }