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 7467300452140989440

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 ] ],
[ [ 0.7 ], [ 0.496 ] ]
],
[
[ [ -0.608 ], [ 0.048 ] ],
[ [ 1.764 ], [ 1.524 ] ]
]
Inputs Statistics: {meanExponent=-0.6122800817139336, negative=1, min=-0.128, max=0.7, mean=0.287, count=4, sum=1.148, positive=3, stdDev=0.3276141022605712, zeros=0},
{meanExponent=-0.27634290888807383, negative=1, min=-0.608, max=1.764, mean=0.682, count=4, sum=2.728, positive=3, stdDev=0.993194844932252, zeros=0}
Output: [
[ [ -0.528 ], [ -0.08 ] ],
[ [ 2.464 ], [ 2.02 ] ]
]
Outputs Statistics: {meanExponent=-0.16932100438380815, negative=2, min=-0.528, max=2.464, mean=0.969, count=4, sum=3.876, positive=2, stdDev=1.2923850045555312, zeros=0}

Feedback Validation

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

SingleDerivativeTester.java:117 executed in 0.09 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 ] ],
[ [ 0.7 ], [ 0.496 ] ]
]
Value Statistics: {meanExponent=-0.6122800817139336, negative=1, min=-0.128, max=0.7, mean=0.287, count=4, sum=1.148, positive=3, stdDev=0.3276141022605712, 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 ] ]
Implemented Statistics: {meanExponent=0.0, negative=0, min=0.0, max=1.0, mean=0.25, count=16, sum=4.0, positive=4, stdDev=0.4330127018922193, zeros=12}
Measured Feedback: [ [ 0.9999999999998899, 0.0, 0.0, 0.0 ], [ 0.0, 1.0000000000021103, 0.0, 0.0 ], [ 0.0, 0.0, 0.9999999999998899, 0.0 ], [ 0.0, 0.0, 0.0, 1.0000000000021103 ] ]
Measured Statistics: {meanExponent=4.343330909351157E-13, negative=0, min=0.0, max=1.0000000000021103, mean=0.25000000000025, count=16, sum=4.000000000004, positive=4, stdDev=0.4330127018926524, zeros=12}
Feedback Error: [ [ -1.1013412404281553E-13, 0.0, 0.0, 0.0 ], [ 0.0, 2.1103119252074976E-12, 0.0, 0.0 ], [ 0.0, 0.0, -1.1013412404281553E-13, 0.0 ], [ 0.0, 0.0, 0.0, 2.1103119252074976E-12 ] ]
Error Statistics: {meanExponent=-12.316865722463366, negative=2, min=-1.1013412404281553E-13, max=2.1103119252074976E-12, mean=2.5002222514558525E-13, count=16, sum=4.000355602329364E-12, positive=2, stdDev=7.040469659775596E-13, zeros=12}
Feedback for input 1
Inputs Values: [
[ [ -0.608 ], [ 0.048 ] ],
[ [ 1.764 ], [ 1.524 ] ]
]
Value Statistics: {meanExponent=-0.27634290888807383, negative=1, min=-0.608, max=1.764, mean=0.682, count=4, sum=2.728, positive=3, stdDev=0.993194844932252, 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 ] ]
Implemented Statistics: {meanExponent=0.0, negative=0, min=0.0, max=1.0, mean=0.25, count=16, sum=4.0, positive=4, stdDev=0.4330127018922193, zeros=12}
Measured Feedback: [ [ 0.9999999999998899, 0.0, 0.0, 0.0 ], [ 0.0, 1.0000000000021103, 0.0, 0.0 ], [ 0.0, 0.0, 1.0000000000000286, 0.0 ], [ 0.0, 0.0, 0.0, 1.0000000000021103 ] ]
Measured Statistics: {meanExponent=4.494007076000114E-13, negative=0, min=0.0, max=1.0000000000021103, mean=0.2500000000002587, count=16, sum=4.000000000004139, positive=4, stdDev=0.4330127018926674, zeros=12}
Feedback Error: [ [ -1.1013412404281553E-13, 0.0, 0.0, 0.0 ], [ 0.0, 2.1103119252074976E-12, 0.0, 0.0 ], [ 0.0, 0.0, 2.864375403532904E-14, 0.0 ], [ 0.0, 0.0, 0.0, 2.1103119252074976E-12 ] ]
Error Statistics: {meanExponent=-12.463088714011102, negative=1, min=-1.1013412404281553E-13, max=2.1103119252074976E-12, mean=2.586958425254693E-13, count=16, sum=4.139133480407509E-12, positive=3, stdDev=7.004019475554049E-13, zeros=12}

Returns

    {
      "absoluteTol" : {
        "count" : 32,
        "sum" : 8.800293826993766E-12,
        "min" : 0.0,
        "max" : 2.1103119252074976E-12,
        "sumOfSquare" : 1.785087472717317E-23,
        "standardDeviation" : 6.944132667139903E-13,
        "average" : 2.750091820935552E-13
      },
      "relativeTol" : {
        "count" : 8,
        "sum" : 4.4001469134924384E-12,
        "min" : 1.4321877017664317E-14,
        "max" : 1.0551559626026354E-12,
        "sumOfSquare" : 4.462718681783896E-24,
        "standardDeviation" : 5.052916329017663E-13,
        "average" : 5.500183641865548E-13
      }
    }

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" : 8.800293826993766E-12,
        "min" : 0.0,
        "max" : 2.1103119252074976E-12,
        "sumOfSquare" : 1.785087472717317E-23,
        "standardDeviation" : 6.944132667139903E-13,
        "average" : 2.750091820935552E-13
      },
      "relativeTol" : {
        "count" : 8,
        "sum" : 4.4001469134924384E-12,
        "min" : 1.4321877017664317E-14,
        "max" : 1.0551559626026354E-12,
        "sumOfSquare" : 4.462718681783896E-24,
        "standardDeviation" : 5.052916329017663E-13,
        "average" : 5.500183641865548E-13
      }
    }

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: 2.7501e-13 +- 6.9441e-13 [0.0000e+00 - 2.1103e-12] (32#)
relativeTol: 5.5002e-13 +- 5.0529e-13 [1.4322e-14 - 1.0552e-12] (8#)

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=2.7501e-13 +- 6.9441e-13 [0.0000e+00 - 2.1103e-12] (32#), relativeTol=5.5002e-13 +- 5.0529e-13 [1.4322e-14 - 1.0552e-12] (8#)}OK
 
  {
    "result": "OK",
    "performance": {
      "execution_time": "0.255",
      "gc_time": "0.119"
    },
    "created_on": 1586741049739,
    "file_name": "derivativeTest",
    "report": {
      "simpleName": "Double_Add",
      "canonicalName": "com.simiacryptus.mindseye.layers.cudnn.SumInputsLayerTest.Double_Add",
      "link": "https://github.com/SimiaCryptus/mindseye-cudnn/tree/59d5b3318556370acb2d83ee6ec123ce0fc6974f/src/test/java/com/simiacryptus/mindseye/layers/cudnn/SumInputsLayerTest.java",
      "javaDoc": ""
    },
    "archive": "s3://code.simiacrypt.us/tests/com/simiacryptus/mindseye/layers/cudnn/SumInputsLayer/Double_Add/derivativeTest/202004132409",
    "id": "f9deb476-8deb-4684-8f43-b04f5a7142dd",
    "report_type": "Components",
    "display_name": "Derivative Validation",
    "target": {
      "simpleName": "SumInputsLayer",
      "canonicalName": "com.simiacryptus.mindseye.layers.cudnn.SumInputsLayer",
      "link": "https://github.com/SimiaCryptus/mindseye-cudnn/tree/59d5b3318556370acb2d83ee6ec123ce0fc6974f/src/main/java/com/simiacryptus/mindseye/layers/cudnn/SumInputsLayer.java",
      "javaDoc": ""
    }
  }