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 6126844159724716032

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)));

Inputs: [
[ [ 0.08, -0.804 ], [ 0.496, -0.712 ], [ 0.048, 1.356 ], [ -1.72, 1.032 ], [ -0.852, -0.176 ] ],
[ [ 0.7, 1.108 ], [ -0.608, 1.048 ], [ 1.524, 1.512 ], [ -1.028, 0.3 ], [ 1.912, 1.556 ] ],
[ [ -0.128, 0.028 ], [ 1.764, -1.616 ], [ 1.208, 1.556 ], [ -0.384, 0.636 ], [ -1.688, 0.788 ] ]
]
Inputs Statistics: {meanExponent=-0.1863636075006468, negative=11, min=-1.72, max=1.912, mean=0.29786666666666667, count=30, sum=8.936, positive=19, stdDev=1.064993888349704, zeros=0}
Output: [
[ [ 8.936 ] ]
]
Outputs Statistics: {meanExponent=0.9511431601075526, negative=0, min=8.936, max=8.936, mean=8.936, count=1, sum=8.936, positive=1, stdDev=0.0, zeros=0}

Feedback Validation

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

SingleDerivativeTester.java:117 executed in 0.11 seconds (0.000 gc):

        return testFeedback(
            statistics,
            component.addRef(),
            RefUtil.addRef(inputPrototype),
            outputPrototype.addRef());
      },
      outputPrototype.addRef(),
      RefUtil.addRef(inputPrototype),
      component.addRef()));

Feedback for input 0
Inputs Values: [
[ [ 0.08, -0.804 ], [ 0.496, -0.712 ], [ 0.048, 1.356 ], [ -1.72, 1.032 ], [ -0.852, -0.176 ] ],
[ [ 0.7, 1.108 ], [ -0.608, 1.048 ], [ 1.524, 1.512 ], [ -1.028, 0.3 ], [ 1.912, 1.556 ] ],
[ [ -0.128, 0.028 ], [ 1.764, -1.616 ], [ 1.208, 1.556 ], [ -0.384, 0.636 ], [ -1.688, 0.788 ] ]
]
Value Statistics: {meanExponent=-0.1863636075006468, negative=11, min=-1.72, max=1.912, mean=0.29786666666666667, count=30, sum=8.936, positive=19, stdDev=1.064993888349704, zeros=0}
Implemented Feedback: [ [ 1.0 ], [ 1.0 ], [ 1.0 ], [ 1.0 ], [ 1.0 ], [ 1.0 ], [ 1.0 ], [ 1.0 ], ... ]
Implemented Statistics: {meanExponent=0.0, negative=0, min=1.0, max=1.0, mean=1.0, count=30, sum=30.0, positive=30, stdDev=0.0, zeros=0}
Measured Feedback: [ [ 0.9999999999976694 ], [ 0.9999999999976694 ], [ 0.9999999999976694 ], [ 0.9999999999976694 ], [ 0.9999999999976694 ], [ 0.9999999999976694 ], [ 0.9999999999976694 ], [ 0.9999999999976694 ], ... ]
Measured Statistics: {meanExponent=-1.0121581088955097E-12, negative=0, min=0.9999999999976694, max=0.9999999999976694, mean=0.9999999999976694, count=30, sum=29.999999999930083, positive=30, stdDev=0.0, zeros=0}
Feedback Error: [ [ -2.3305801732931286E-12 ], [ -2.3305801732931286E-12 ], [ -2.3305801732931286E-12 ], [ -2.3305801732931286E-12 ], [ -2.3305801732931286E-12 ], [ -2.3305801732931286E-12 ], [ -2.3305801732931286E-12 ], [ -2.3305801732931286E-12 ], ... ]
Error Statistics: {meanExponent=-11.63253595249544, negative=30, min=-2.3305801732931286E-12, max=-2.3305801732931286E-12, mean=-2.3305801732931286E-12, count=30, sum=-6.991740519879386E-11, positive=0, stdDev=0.0, zeros=0}

Returns

    {
      "absoluteTol" : {
        "count" : 30,
        "sum" : 6.991740519879386E-11,
        "min" : 2.3305801732931286E-12,
        "max" : 2.3305801732931286E-12,
        "sumOfSquare" : 1.6294811832441088E-22,
        "standardDeviation" : 0.0,
        "average" : 2.3305801732931286E-12
      },
      "relativeTol" : {
        "count" : 30,
        "sum" : 3.495870259943767E-11,
        "min" : 1.1652900866479222E-12,
        "max" : 1.1652900866479222E-12,
        "sumOfSquare" : 4.073702958119766E-23,
        "standardDeviation" : 0.0,
        "average" : 1.1652900866479222E-12
      }
    }

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" : 30,
        "sum" : 6.991740519879386E-11,
        "min" : 2.3305801732931286E-12,
        "max" : 2.3305801732931286E-12,
        "sumOfSquare" : 1.6294811832441088E-22,
        "standardDeviation" : 0.0,
        "average" : 2.3305801732931286E-12
      },
      "relativeTol" : {
        "count" : 30,
        "sum" : 3.495870259943767E-11,
        "min" : 1.1652900866479222E-12,
        "max" : 1.1652900866479222E-12,
        "sumOfSquare" : 4.073702958119766E-23,
        "standardDeviation" : 0.0,
        "average" : 1.1652900866479222E-12
      }
    }

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));

Finite-Difference Derivative Accuracy:
absoluteTol: 2.3306e-12 +- 0.0000e+00 [2.3306e-12 - 2.3306e-12] (30#)
relativeTol: 1.1653e-12 +- 0.0000e+00 [1.1653e-12 - 1.1653e-12] (30#)

Frozen and Alive Status

SingleDerivativeTester.java:156 executed in 0.02 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

  {
    "result": "OK",
    "performance": {
      "execution_time": "0.308",
      "gc_time": "0.139"
    },
    "created_on": 1586734906348,
    "file_name": "derivativeTest",
    "report": {
      "simpleName": "Asymmetric",
      "canonicalName": "com.simiacryptus.mindseye.layers.cudnn.SumReducerLayerTest.Asymmetric",
      "link": "https://github.com/SimiaCryptus/mindseye-cudnn/tree/59d5b3318556370acb2d83ee6ec123ce0fc6974f/src/test/java/com/simiacryptus/mindseye/layers/cudnn/SumReducerLayerTest.java",
      "javaDoc": ""
    },
    "archive": "s3://code.simiacrypt.us/tests/com/simiacryptus/mindseye/layers/cudnn/SumReducerLayer/Asymmetric/derivativeTest/202004124146",
    "id": "d7794c80-4440-4c84-9da1-7086c99147d9",
    "report_type": "Components",
    "display_name": "Derivative Validation",
    "target": {
      "simpleName": "SumReducerLayer",
      "canonicalName": "com.simiacryptus.mindseye.layers.cudnn.SumReducerLayer",
      "link": "https://github.com/SimiaCryptus/mindseye-cudnn/tree/59d5b3318556370acb2d83ee6ec123ce0fc6974f/src/main/java/com/simiacryptus/mindseye/layers/cudnn/SumReducerLayer.java",
      "javaDoc": ""
    }
  }