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 8606111137786749952

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, -0.608 ], [ -0.128, 0.048 ] ],
[ [ 0.7, 1.764 ], [ 0.496, 1.524 ] ]
]
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}
Output: [
[ [ 0.07999999821186066 ], [ -0.12800000607967377 ] ],
[ [ 0.699999988079071 ], [ 0.4959999918937683 ] ]
]
Outputs Statistics: {meanExponent=-0.612280082607212, negative=1, min=-0.12800000607967377, max=0.699999988079071, mean=0.28699999302625656, count=4, sum=1.1479999721050262, positive=3, stdDev=0.3276140994185563, zeros=0}

Feedback Validation

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

SingleDerivativeTester.java:117 executed in 0.08 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, -0.608 ], [ -0.128, 0.048 ] ],
[ [ 0.7, 1.764 ], [ 0.496, 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, 1.0, 0.0, 0.0 ], [ 0.0, 0.0, 1.0, 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 ], [ 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=0.125, count=32, sum=4.0, positive=4, stdDev=0.33071891388307384, zeros=28}
Measured Feedback: [ [ 1.0000169277191162, 0.0, 0.0, 0.0 ], [ 0.0, 1.0001659393310547, 0.0, 0.0 ], [ 0.0, 0.0, 1.0000169277191162, 0.0 ], [ 0.0, 0.0, 0.0, 1.0001659393310547 ], [ 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=3.9706054956914476E-5, negative=0, min=0.0, max=1.0001659393310547, mean=0.12501142919063568, count=32, sum=4.000365734100342, positive=4, stdDev=0.33074915372814884, zeros=28}
Feedback Error: [ [ 1.6927719116210938E-5, 0.0, 0.0, 0.0 ], [ 0.0, 1.659393310546875E-4, 0.0, 0.0 ], [ 0.0, 0.0, 1.6927719116210938E-5, 0.0 ], [ 0.0, 0.0, 0.0, 1.659393310546875E-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, 0.0, 0.0, 0.0 ] ]
Error Statistics: {meanExponent=-4.2757261104427675, negative=0, min=0.0, max=1.659393310546875E-4, mean=1.1429190635681152E-5, count=32, sum=3.657341003417969E-4, positive=4, stdDev=4.0103293885888165E-5, zeros=28}

Returns

    {
      "absoluteTol" : {
        "count" : 32,
        "sum" : 3.657341003417969E-4,
        "min" : 0.0,
        "max" : 1.659393310546875E-4,
        "sumOfSquare" : 5.564481853070902E-8,
        "standardDeviation" : 4.0103293885888165E-5,
        "average" : 1.1429190635681152E-5
      },
      "relativeTol" : {
        "count" : 4,
        "sum" : 1.8285314010970424E-4,
        "min" : 8.463787921793169E-6,
        "max" : 8.296278213305896E-5,
        "sumOfSquare" : 1.3908917850484998E-8,
        "standardDeviation" : 3.72494971056329E-5,
        "average" : 4.571328502742606E-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" : 32,
        "sum" : 3.657341003417969E-4,
        "min" : 0.0,
        "max" : 1.659393310546875E-4,
        "sumOfSquare" : 5.564481853070902E-8,
        "standardDeviation" : 4.0103293885888165E-5,
        "average" : 1.1429190635681152E-5
      },
      "relativeTol" : {
        "count" : 4,
        "sum" : 1.8285314010970424E-4,
        "min" : 8.463787921793169E-6,
        "max" : 8.296278213305896E-5,
        "sumOfSquare" : 1.3908917850484998E-8,
        "standardDeviation" : 3.72494971056329E-5,
        "average" : 4.571328502742606E-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: 1.1429e-05 +- 4.0103e-05 [0.0000e+00 - 1.6594e-04] (32#)
relativeTol: 4.5713e-05 +- 3.7249e-05 [8.4638e-06 - 8.2963e-05] (4#)

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=1.1429e-05 +- 4.0103e-05 [0.0000e+00 - 1.6594e-04] (32#), relativeTol=4.5713e-05 +- 3.7249e-05 [8.4638e-06 - 8.2963e-05] (4#)}OK
  {
    "result": "OK",
    "performance": {
      "execution_time": "0.260",
      "gc_time": "0.119"
    },
    "created_on": 1586745933766,
    "file_name": "derivativeTest",
    "report": {
      "simpleName": "Float",
      "canonicalName": "com.simiacryptus.mindseye.layers.cudnn.ImgBandSelectLayerTest.Float",
      "link": "https://github.com/SimiaCryptus/mindseye-cudnn/tree/59d5b3318556370acb2d83ee6ec123ce0fc6974f/src/test/java/com/simiacryptus/mindseye/layers/cudnn/ImgBandSelectLayerTest.java",
      "javaDoc": ""
    },
    "archive": "s3://code.simiacrypt.us/tests/com/simiacryptus/mindseye/layers/cudnn/ImgBandSelectLayer/Float/derivativeTest/202004134533",
    "id": "f7d4a8de-12c4-471d-b94c-5c83fe519d09",
    "report_type": "Components",
    "display_name": "Derivative Validation",
    "target": {
      "simpleName": "ImgBandSelectLayer",
      "canonicalName": "com.simiacryptus.mindseye.layers.cudnn.ImgBandSelectLayer",
      "link": "https://github.com/SimiaCryptus/mindseye-cudnn/tree/59d5b3318556370acb2d83ee6ec123ce0fc6974f/src/main/java/com/simiacryptus/mindseye/layers/cudnn/ImgBandSelectLayer.java",
      "javaDoc": ""
    }
  }