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 6500032426335515648

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, 1.208 ], [ -0.128, 0.048, -1.028 ] ],
[ [ 0.7, 1.764, -1.72 ], [ 0.496, 1.524, -0.384 ] ]
],
[
[ [ -0.852 ], [ -1.688 ] ],
[ [ 1.912 ], [ -0.804 ] ]
]
Inputs Statistics: {meanExponent=-0.3033810201823816, negative=5, min=-1.72, max=1.764, mean=0.16266666666666665, count=12, sum=1.952, positive=7, stdDev=0.9945442283891763, zeros=0},
{meanExponent=0.08613899343621721, negative=3, min=-1.688, max=1.912, mean=-0.358, count=4, sum=-1.432, positive=1, stdDev=1.35690382857445, zeros=0}
Output: [
[ [ -0.06816, 0.5180159999999999, -1.029216 ], [ 0.216064, -0.081024, 1.735264 ] ],
[ [ 1.3383999999999998, 3.3727679999999998, -3.28864 ], [ -0.398784, -1.2252960000000002, 0.308736 ] ]
]
Outputs Statistics: {meanExponent=-0.2172420267461644, negative=6, min=-3.28864, max=3.3727679999999998, mean=0.11651066666666661, count=12, sum=1.3981279999999994, positive=6, stdDev=1.5803874334296075, zeros=0}

Feedback Validation

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

SingleDerivativeTester.java:117 executed in 0.07 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, 1.208 ], [ -0.128, 0.048, -1.028 ] ],
[ [ 0.7, 1.764, -1.72 ], [ 0.496, 1.524, -0.384 ] ]
]
Value Statistics: {meanExponent=-0.3033810201823816, negative=5, min=-1.72, max=1.764, mean=0.16266666666666665, count=12, sum=1.952, positive=7, stdDev=0.9945442283891763, zeros=0}
Implemented Feedback: [ [ -0.852, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ... ], [ 0.0, 1.912, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ... ], [ 0.0, 0.0, -1.688, 0.0, 0.0, 0.0, 0.0, 0.0, ... ], [ 0.0, 0.0, 0.0, -0.804, 0.0, 0.0, 0.0, 0.0, ... ], [ 0.0, 0.0, 0.0, 0.0, -0.852, 0.0, 0.0, 0.0, ... ], [ 0.0, 0.0, 0.0, 0.0, 0.0, 1.912, 0.0, 0.0, ... ], [ 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, -1.688, 0.0, ... ], [ 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, -0.804, ... ], ... ]
Implemented Statistics: {meanExponent=0.08613899343621721, negative=9, min=-1.688, max=1.912, mean=-0.029833333333333337, count=144, sum=-4.296, positive=3, stdDev=0.40400821636309436, zeros=132}
Measured Feedback: [ [ -0.8520000000000749, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ... ], [ 0.0, 1.9119999999994697, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ... ], [ 0.0, 0.0, -1.6879999999999673, 0.0, 0.0, 0.0, 0.0, 0.0, ... ], [ 0.0, 0.0, 0.0, -0.8039999999998049, 0.0, 0.0, 0.0, 0.0, ... ], [ 0.0, 0.0, 0.0, 0.0, -0.8519999999989647, 0.0, 0.0, 0.0, ... ], [ 0.0, 0.0, 0.0, 0.0, 0.0, 1.9120000000016901, 0.0, 0.0, ... ], [ 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, -1.688000000000106, 0.0, ... ], [ 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, -0.8039999999986946, ... ], ... ]
Measured Statistics: {meanExponent=0.08613899343615215, negative=9, min=-1.688000000000106, max=1.9120000000016901, mean=-0.029833333333293972, count=144, sum=-4.295999999994332, positive=3, stdDev=0.40400821636314765, zeros=132}
Feedback Error: [ [ -7.494005416219807E-14, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ... ], [ 0.0, -5.302425165609748E-13, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ... ], [ 0.0, 0.0, 3.26405569239796E-14, 0.0, 0.0, 0.0, 0.0, 0.0, ... ], [ 0.0, 0.0, 0.0, 1.9517720772910252E-13, 0.0, 0.0, 0.0, 0.0, ... ], [ 0.0, 0.0, 0.0, 0.0, 1.0352829704629585E-12, 0.0, 0.0, 0.0, ... ], [ 0.0, 0.0, 0.0, 0.0, 0.0, 1.6902035326893383E-12, 0.0, 0.0, ... ], [ 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, -1.0613732115416497E-13, 0.0, ... ], [ 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.305400232354259E-12, ... ], ... ]
Error Statistics: {meanExponent=-12.52727468047292, negative=4, min=-5.302425165609748E-13, max=1.6902035326893383E-12, mean=3.9361261164019526E-14, count=144, sum=5.668021607618812E-12, positive=8, stdDev=2.464115488815371E-13, zeros=132}
Feedback for input 1
Inputs Values: [
[ [ -0.852 ], [ -1.688 ] ],
[ [ 1.912 ], [ -0.804 ] ]
]
Value Statistics: {meanExponent=0.08613899343621721, negative=3, min=-1.688, max=1.912, mean=-0.358, count=4, sum=-1.432, positive=1, stdDev=1.35690382857445, zeros=0}
Implemented Feedback: [ [ 0.08, 0.0, 0.0, 0.0, -0.608, 0.0, 0.0, 0.0, ... ], [ 0.0, 0.7, 0.0, 0.0, 0.0, 1.764, 0.0, 0.0, ... ], [ 0.0, 0.0, -0.128, 0.0, 0.0, 0.0, 0.048, 0.0, ... ], [ 0.0, 0.0, 0.0, 0.496, 0.0, 0.0, 0.0, 1.524, ... ] ]
Implemented Statistics: {meanExponent=-0.3033810201823816, negative=5, min=-1.72, max=1.764, mean=0.04066666666666666, count=48, sum=1.952, positive=7, stdDev=0.5022358896862, zeros=36}
Measured Feedback: [ [ 0.07999999999994123, 0.0, 0.0, 0.0, -0.6079999999997199, 0.0, 0.0, 0.0, ... ], [ 0.0, 0.700000000000145, 0.0, 0.0, 0.0, 1.7639999999996547, 0.0, 0.0, ... ], [ 0.0, 0.0, -0.1280000000000725, 0.0, 0.0, 0.0, 0.04799999999999249, 0.0, ... ], [ 0.0, 0.0, 0.0, 0.4959999999998299, 0.0, 0.0, 0.0, 1.5240000000016352, ... ] ]
Measured Statistics: {meanExponent=-0.3033810201825469, negative=5, min=-1.719999999996169, max=1.7639999999996547, mean=0.04066666666679234, count=48, sum=1.9520000000060325, positive=7, stdDev=0.5022358896858828, zeros=36}
Feedback Error: [ [ -5.877243136609422E-14, 0.0, 0.0, 0.0, 2.80109269112927E-13, 0.0, 0.0, 0.0, ... ], [ 0.0, 1.4499512701604544E-13, 0.0, 0.0, 0.0, -3.452793606584237E-13, 0.0, 0.0, ... ], [ 0.0, 0.0, -7.249756350802272E-14, 0.0, 0.0, 0.0, -7.507883204027621E-15, 0.0, ... ], [ 0.0, 0.0, 0.0, -1.7008616737257398E-13, 0.0, 0.0, 0.0, 1.6351364706679306E-12, ... ] ]
Error Statistics: {meanExponent=-12.621736116539353, negative=6, min=-7.902567489281864E-13, max=3.830935568771565E-12, mean=1.2567262045829844E-13, count=48, sum=6.0322857819983255E-12, positive=6, stdDev=6.423126181413851E-13, zeros=36}

Returns

    {
      "absoluteTol" : {
        "count" : 192,
        "sum" : 1.6161627591770866E-11,
        "min" : 0.0,
        "max" : 3.830935568771565E-12,
        "sumOfSquare" : 2.9527823417489144E-23,
        "standardDeviation" : 3.830212685321425E-13,
        "average" : 8.417514370713992E-14
      },
      "relativeTol" : {
        "count" : 24,
        "sum" : 7.035071889520595E-12,
        "min" : 9.668411411131492E-15,
        "max" : 1.1136440606906488E-12,
        "sumOfSquare" : 3.9965843942118924E-24,
        "standardDeviation" : 2.839019691258694E-13,
        "average" : 2.9312799539669146E-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" : 192,
        "sum" : 1.6161627591770866E-11,
        "min" : 0.0,
        "max" : 3.830935568771565E-12,
        "sumOfSquare" : 2.9527823417489144E-23,
        "standardDeviation" : 3.830212685321425E-13,
        "average" : 8.417514370713992E-14
      },
      "relativeTol" : {
        "count" : 24,
        "sum" : 7.035071889520595E-12,
        "min" : 9.668411411131492E-15,
        "max" : 1.1136440606906488E-12,
        "sumOfSquare" : 3.9965843942118924E-24,
        "standardDeviation" : 2.839019691258694E-13,
        "average" : 2.9312799539669146E-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: 8.4175e-14 +- 3.8302e-13 [0.0000e+00 - 3.8309e-12] (192#)
relativeTol: 2.9313e-13 +- 2.8390e-13 [9.6684e-15 - 1.1136e-12] (24#)

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=8.4175e-14 +- 3.8302e-13 [0.0000e+00 - 3.8309e-12] (192#), relativeTol=2.9313e-13 +- 2.8390e-13 [9.6684e-15 - 1.1136e-12] (24#)}OK
 
  {
    "result": "OK",
    "performance": {
      "execution_time": "0.209",
      "gc_time": "0.102"
    },
    "created_on": 1586735667551,
    "file_name": "derivativeTest",
    "report": {
      "simpleName": "Basic",
      "canonicalName": "com.simiacryptus.mindseye.layers.java.ImgPixelGateLayerTest.Basic",
      "link": "https://github.com/SimiaCryptus/mindseye-java/tree/93db34cedee48c0202777a2b25deddf1dfaf5731/src/test/java/com/simiacryptus/mindseye/layers/java/ImgPixelGateLayerTest.java",
      "javaDoc": ""
    },
    "archive": "s3://code.simiacrypt.us/tests/com/simiacryptus/mindseye/layers/java/ImgPixelGateLayer/Basic/derivativeTest/202004125427",
    "id": "55c4c844-1c2b-4426-a9a2-6a312835538c",
    "report_type": "Components",
    "display_name": "Derivative Validation",
    "target": {
      "simpleName": "ImgPixelGateLayer",
      "canonicalName": "com.simiacryptus.mindseye.layers.java.ImgPixelGateLayer",
      "link": "https://github.com/SimiaCryptus/mindseye-java/tree/93db34cedee48c0202777a2b25deddf1dfaf5731/src/main/java/com/simiacryptus/mindseye/layers/java/ImgPixelGateLayer.java",
      "javaDoc": ""
    }
  }