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 6250682673752050688

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, 1.108, 1.612 ], [ -0.608, -1.616, -0.556 ], [ 1.208, 1.032, 1.552 ], [ -0.852, 1.556, -1.572 ] ],
[ [ 0.7, 0.028, 1.64 ], [ 1.764, 1.356, -1.476 ], [ -1.72, 0.3, 1.876 ], [ 1.912, 0.788, -1.516 ] ],
[ [ -0.128, -0.712, 0.392 ], [ 0.048, 1.512, 1.704 ], [ -1.028, 0.636, -0.408 ], [ -1.688, -0.768, -0.636 ] ],
[ [ 0.496, 1.048, 0.092 ], [ 1.524, 1.556, -1.228 ], [ -0.384, -0.176, -0.384 ], [ -0.804, -0.068, -1.492 ] ]
],
[
[ [ -0.804, 0.148, -0.032 ] ]
]
Inputs Statistics: {meanExponent=-0.15927984553930932, negative=22, min=-1.72, max=1.912, mean=0.1604166666666667, count=48, sum=7.700000000000002, positive=26, stdDev=1.1368718015042663, zeros=0},
{meanExponent=-0.8064440858455617, negative=2, min=-0.804, max=0.148, mean=-0.22933333333333336, count=3, sum=-0.6880000000000001, positive=1, stdDev=0.41294174999494654, zeros=0}
Output: [
[ [ -0.7240000000000001, 1.256, 1.58 ], [ -1.412, -1.4680000000000002, -0.5880000000000001 ], [ 0.4039999999999999, 1.18, 1.52 ], [ -1.6560000000000001, 1.704, -1.604 ] ],
[ [ -0.10400000000000009, 0.176, 1.6079999999999999 ], [ 0.96, 1.504, -1.508 ], [ -2.524, 0.44799999999999995, 1.8439999999999999 ], [ 1.1079999999999999, 0.936, -1.548 ] ],
[ [ -0.932, -0.564, 0.36 ], [ -0.756, 1.66, 1.672 ], [ -1.832, 0.784, -0.43999999999999995 ], [ -2.492, -0.62, -0.668 ] ],
[ [ -0.30800000000000005, 1.196, 0.06 ], [ 0.72, 1.704, -1.26 ], [ -1.1880000000000002, -0.027999999999999997, -0.41600000000000004 ], [ -1.608, 0.07999999999999999, -1.524 ] ]
]
Outputs Statistics: {meanExponent=-0.09435540460450394, negative=25, min=-2.524, max=1.8439999999999999, mean=-0.06891666666666672, count=48, sum=-3.308000000000003, positive=23, stdDev=1.2515721419034898, zeros=0}

Feedback Validation

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

SingleDerivativeTester.java:117 executed in 0.56 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, 1.108, 1.612 ], [ -0.608, -1.616, -0.556 ], [ 1.208, 1.032, 1.552 ], [ -0.852, 1.556, -1.572 ] ],
[ [ 0.7, 0.028, 1.64 ], [ 1.764, 1.356, -1.476 ], [ -1.72, 0.3, 1.876 ], [ 1.912, 0.788, -1.516 ] ],
[ [ -0.128, -0.712, 0.392 ], [ 0.048, 1.512, 1.704 ], [ -1.028, 0.636, -0.408 ], [ -1.688, -0.768, -0.636 ] ],
[ [ 0.496, 1.048, 0.092 ], [ 1.524, 1.556, -1.228 ], [ -0.384, -0.176, -0.384 ], [ -0.804, -0.068, -1.492 ] ]
]
Value Statistics: {meanExponent=-0.15927984553930932, negative=22, min=-1.72, max=1.912, mean=0.1604166666666667, count=48, sum=7.700000000000002, positive=26, stdDev=1.1368718015042663, 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=0.020833333333333332, count=2304, sum=48.0, positive=48, stdDev=0.1428261375083551, zeros=2256}
Measured Feedback: [ [ 1.000000000001, 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, 0.9999999999976694, 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, ... ], ... ]
Measured Statistics: {meanExponent=-5.536445067361131E-14, negative=0, min=0.0, max=1.0000000000021103, mean=0.020833333333330657, count=2304, sum=47.99999999999383, positive=48, stdDev=0.1428261375083369, zeros=2256}
Feedback Error: [ [ 1.000088900582341E-12, 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, -2.3305801732931286E-12, 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, ... ], ... ]
Error Statistics: {meanExponent=-12.8262070832058, negative=43, min=-2.3305801732931286E-12, max=2.1103119252074976E-12, mean=-2.6558616417204917E-15, count=2304, sum=-6.119105222524013E-12, positive=5, stdDev=1.075944210019789E-13, zeros=2256}
Feedback for input 1
Inputs Values: [
[ [ -0.804, 0.148, -0.032 ] ]
]
Value Statistics: {meanExponent=-0.8064440858455617, negative=2, min=-0.804, max=0.148, mean=-0.22933333333333336, count=3, sum=-0.6880000000000001, positive=1, stdDev=0.41294174999494654, zeros=0}
Implemented Feedback: [ [ 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 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=0.3333333333333333, count=144, sum=48.0, positive=48, stdDev=0.4714045207910317, zeros=96}
Measured Feedback: [ [ 0.9999999999998899, 0.9999999999998899, 0.9999999999998899, 0.9999999999998899, 0.9999999999976694, 0.9999999999998899, 0.9999999999998899, 0.9999999999998899, ... ], [ 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=-5.662008539568316E-14, negative=0, min=0.0, max=1.0000000000021103, mean=0.33333333333328974, count=144, sum=47.99999999999372, positive=48, stdDev=0.4714045207909703, zeros=96}
Feedback Error: [ [ -1.1013412404281553E-13, -1.1013412404281553E-13, -1.1013412404281553E-13, -1.1013412404281553E-13, -2.3305801732931286E-12, -1.1013412404281553E-13, -1.1013412404281553E-13, -1.1013412404281553E-13, ... ], [ 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=-12.808715263575634, negative=43, min=-2.3305801732931286E-12, max=2.1103119252074976E-12, mean=-4.3457521531959425E-14, count=144, sum=-6.257883100602157E-12, positive=5, stdDev=4.2337683351088444E-13, zeros=96}

Returns

    {
      "absoluteTol" : {
        "count" : 2448,
        "sum" : 3.321143360324186E-11,
        "min" : 0.0,
        "max" : 2.3305801732931286E-12,
        "sumOfSquare" : 5.27723003290148E-23,
        "standardDeviation" : 1.4619594793365975E-13,
        "average" : 1.3566762092827557E-14
      },
      "relativeTol" : {
        "count" : 96,
        "sum" : 1.6605716801624515E-11,
        "min" : 1.4321877017664317E-14,
        "max" : 1.1652900866479222E-12,
        "sumOfSquare" : 1.3193075082263023E-23,
        "standardDeviation" : 3.278827441358755E-13,
        "average" : 1.729762166835887E-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" : 2448,
        "sum" : 3.321143360324186E-11,
        "min" : 0.0,
        "max" : 2.3305801732931286E-12,
        "sumOfSquare" : 5.27723003290148E-23,
        "standardDeviation" : 1.4619594793365975E-13,
        "average" : 1.3566762092827557E-14
      },
      "relativeTol" : {
        "count" : 96,
        "sum" : 1.6605716801624515E-11,
        "min" : 1.4321877017664317E-14,
        "max" : 1.1652900866479222E-12,
        "sumOfSquare" : 1.3193075082263023E-23,
        "standardDeviation" : 3.278827441358755E-13,
        "average" : 1.729762166835887E-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: 1.3567e-14 +- 1.4620e-13 [0.0000e+00 - 2.3306e-12] (2448#)
relativeTol: 1.7298e-13 +- 3.2788e-13 [1.4322e-14 - 1.1653e-12] (96#)

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=1.3567e-14 +- 1.4620e-13 [0.0000e+00 - 2.3306e-12] (2448#), relativeTol=1.7298e-13 +- 3.2788e-13 [1.4322e-14 - 1.1653e-12] (96#)}OK
 
  {
    "result": "OK",
    "performance": {
      "execution_time": "0.827",
      "gc_time": "0.204"
    },
    "created_on": 1586739831617,
    "file_name": "derivativeTest",
    "report": {
      "simpleName": "Double",
      "canonicalName": "com.simiacryptus.mindseye.layers.cudnn.GateBiasLayerTest.Double",
      "link": "https://github.com/SimiaCryptus/mindseye-cudnn/tree/59d5b3318556370acb2d83ee6ec123ce0fc6974f/src/test/java/com/simiacryptus/mindseye/layers/cudnn/GateBiasLayerTest.java",
      "javaDoc": ""
    },
    "archive": "s3://code.simiacrypt.us/tests/com/simiacryptus/mindseye/layers/cudnn/GateBiasLayer/Double/derivativeTest/202004130351",
    "id": "411f81d2-22a3-4bc8-8e50-52a757be3e30",
    "report_type": "Components",
    "display_name": "Derivative Validation",
    "target": {
      "simpleName": "GateBiasLayer",
      "canonicalName": "com.simiacryptus.mindseye.layers.cudnn.GateBiasLayer",
      "link": "https://github.com/SimiaCryptus/mindseye-cudnn/tree/59d5b3318556370acb2d83ee6ec123ce0fc6974f/src/main/java/com/simiacryptus/mindseye/layers/cudnn/GateBiasLayer.java",
      "javaDoc": ""
    }
  }