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 8701302996177231872

Differential Validation

SingleDerivativeTester.java:101 executed in 0.01 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 ], [ -0.608, -1.616 ], [ 1.208, 1.032 ], [ -0.852, 1.556 ] ],
[ [ 0.7, 0.028 ], [ 1.764, 1.356 ], [ -1.72, 0.3 ], [ 1.912, 0.788 ] ],
[ [ -0.128, -0.712 ], [ 0.048, 1.512 ], [ -1.028, 0.636 ], [ -1.688, -0.768 ] ],
[ [ 0.496, 1.048 ], [ 1.524, 1.556 ], [ -0.384, -0.176 ], [ -0.804, -0.068 ] ]
],
[
[ [ 1.612, -1.248 ], [ 1.552, 0.56 ], [ -0.804, -0.312 ], [ -1.832, 0.812 ], [ -0.472, 1.168 ], [ -0.316, -1.34 ], [ -1.552, 0.656 ], [ -1.484, -1.104 ] ],
[ [ 1.64, 1.324 ], [ 1.876, -1.656 ], [ 0.148, -0.968 ], [ 1.368, -1.808 ], [ -0.504, 1.288 ], [ 1.156, -1.176 ], [ 0.016, -0.848 ], [ 1.352, -0.924 ] ],
[ [ 0.392, 0.344 ], [ -0.408, -0.856 ], [ -0.032, -0.892 ], [ -1.54, 0.644 ], [ -1.156, 1.972 ], [ 0.972, -1.536 ], [ 1.288, 1.82 ], [ -0.888, 1.66 ] ],
[ [ 0.092, -1.34 ], [ -0.384, -1.16 ], [ -0.892, -0.808 ], [ -0.876, 0.66 ], [ 0.184, 0.672 ], [ -1.116, -1.808 ], [ 1.628, -1.976 ], [ -1.256, 1.132 ] ],
[ [ -0.556, 1.776 ], [ -1.572, 0.688 ], [ 1.62, -1.1 ], [ 1.652, -1.76 ], [ 1.98, 1.256 ], [ 0.52, -0.684 ], [ -1.564, 0.488 ], [ 1.916, 1.012 ] ],
[ [ -1.476, 0.52 ], [ -1.516, -0.124 ], [ -1.856, -0.968 ], [ -1.424, 0.82 ], [ -0.628, 1.144 ], [ -1.456, -1.552 ], [ -1.764, 0.688 ], [ -1.724, -0.26 ] ],
[ [ 1.704, 1.444 ], [ -0.636, -1.176 ], [ 0.996, -0.784 ], [ -0.464, 1.24 ], [ -0.368, 1.66 ], [ -2.0, 1.144 ], [ 0.692, -0.488 ], [ -1.664, 1.596 ] ],
[ [ -1.228, 1.956 ], [ -1.492, -1.256 ], [ 0.048, 0.012 ], [ -0.012, -1.58 ], [ -1.16, 1.836 ], [ 1.42, -1.524 ], [ 1.628, 0.072 ], [ 0.82, 0.248 ] ]
]
Inputs Statistics: {meanExponent=-0.21478244038380173, negative=13, min=-1.72, max=1.912, mean=0.253125, count=32, sum=8.1, positive=19, stdDev=1.0492889184466785, zeros=0},
{meanExponent=-0.08120815300031158, negative=68, min=-2.0, max=1.98, mean=-0.08206249999999997, count=128, sum=-10.503999999999996, positive=60, stdDev=1.2194777554731164, zeros=0}
Output: [
[ [ 0.0, 0.0 ], [ 0.0, 0.0 ], [ 0.0, 0.0 ], [ 0.0, -1.616 ], [ 0.0, 0.0 ], [ 0.0, 0.0 ], [ 0.0, 1.556 ], [ 0.0, 0.0 ] ],
[ [ 0.0, 1.108 ], [ 0.08, 0.0 ], [ 0.0, 0.0 ], [ -0.608, 0.0 ], [ 0.0, 1.032 ], [ 1.208, 0.0 ], [ 0.0, 0.0 ], [ -0.852, 0.0 ] ],
[ [ 0.7, 0.028 ], [ 0.0, 0.0 ], [ 1.764, 0.0 ], [ 0.0, 0.0 ], [ 0.0, 0.3 ], [ -1.72, 0.0 ], [ 0.0, 0.788 ], [ 0.0, 0.0 ] ],
[ [ 0.0, 0.0 ], [ 0.0, 0.0 ], [ 0.0, 0.0 ], [ 0.0, 1.356 ], [ 0.0, 0.0 ], [ 0.0, 0.0 ], [ 1.912, 0.0 ], [ 0.0, 0.0 ] ],
[ [ -0.128, -0.712 ], [ 0.0, 0.0 ], [ 0.0, 0.0 ], [ 0.048, 0.0 ], [ -1.028, 0.636 ], [ 0.0, 0.0 ], [ 0.0, 0.0 ], [ -1.688, -0.768 ] ],
[ [ 0.0, 0.0 ], [ 0.0, 0.0 ], [ 0.0, 0.0 ], [ 0.0, 1.512 ], [ 0.0, 0.0 ], [ 0.0, 0.0 ], [ 0.0, 0.0 ], [ 0.0, 0.0 ] ],
[ [ 0.496, 0.0 ], [ 0.0, 0.0 ], [ 1.524, 0.0 ], [ 0.0, 1.556 ], [ 0.0, 0.0 ], [ 0.0, 0.0 ], [ 0.0, 0.0 ], [ 0.0, -0.068 ] ],
[ [ 0.0, 1.048 ], [ 0.0, 0.0 ], [ 0.0, 0.0 ], [ 0.0, 0.0 ], [ 0.0, -0.176 ], [ -0.384, 0.0 ], [ -0.804, 0.0 ], [ 0.0, 0.0 ] ]
]
Outputs Statistics: {meanExponent=-0.2147824403838017, negative=13, min=-1.72, max=1.912, mean=0.06328125, count=128, sum=8.1, positive=19, stdDev=0.5359714156542655, zeros=96}

Feedback Validation

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

SingleDerivativeTester.java:117 executed in 0.27 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 ], [ -0.608, -1.616 ], [ 1.208, 1.032 ], [ -0.852, 1.556 ] ],
[ [ 0.7, 0.028 ], [ 1.764, 1.356 ], [ -1.72, 0.3 ], [ 1.912, 0.788 ] ],
[ [ -0.128, -0.712 ], [ 0.048, 1.512 ], [ -1.028, 0.636 ], [ -1.688, -0.768 ] ],
[ [ 0.496, 1.048 ], [ 1.524, 1.556 ], [ -0.384, -0.176 ], [ -0.804, -0.068 ] ]
]
Value Statistics: {meanExponent=-0.21478244038380173, negative=13, min=-1.72, max=1.912, mean=0.253125, count=32, sum=8.1, positive=19, stdDev=1.0492889184466785, zeros=0}
Implemented Feedback: [ [ 0.0, 0.0, 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, 0.0, 1.0, 0.0, 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, 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, 0.0, ... ], [ 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=0.0078125, count=4096, sum=32.0, positive=32, stdDev=0.08804240366863003, zeros=4064}
Measured Feedback: [ [ 0.0, 0.0, 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, 0.0, 0.9999999999998899, 0.0, 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, 0.0, ... ], [ 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, ... ], [ 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ... ], ... ]
Measured Statistics: {meanExponent=-4.076694364854251E-14, negative=0, min=0.0, max=1.0000000000000286, mean=0.007812499999999266, count=4096, sum=31.999999999996994, positive=32, stdDev=0.08804240366862177, zeros=4064}
Feedback Error: [ [ 0.0, 0.0, 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, 0.0, -1.1013412404281553E-13, 0.0, 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, 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, 0.0, ... ], [ 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ... ], ... ]
Error Statistics: {meanExponent=-13.052415404627574, negative=29, min=-1.1013412404281553E-13, max=2.864375403532904E-14, mean=-7.333543494691952E-16, count=4096, sum=-3.0038194154258235E-12, positive=3, stdDev=9.109806220769671E-15, zeros=4064}
Feedback for input 1
Inputs Values: [
[ [ 1.612, -1.248 ], [ 1.552, 0.56 ], [ -0.804, -0.312 ], [ -1.832, 0.812 ], [ -0.472, 1.168 ], [ -0.316, -1.34 ], [ -1.552, 0.656 ], [ -1.484, -1.104 ] ],
[ [ 1.64, 1.324 ], [ 1.876, -1.656 ], [ 0.148, -0.968 ], [ 1.368, -1.808 ], [ -0.504, 1.288 ], [ 1.156, -1.176 ], [ 0.016, -0.848 ], [ 1.352, -0.924 ] ],
[ [ 0.392, 0.344 ], [ -0.408, -0.856 ], [ -0.032, -0.892 ], [ -1.54, 0.644 ], [ -1.156, 1.972 ], [ 0.972, -1.536 ], [ 1.288, 1.82 ], [ -0.888, 1.66 ] ],
[ [ 0.092, -1.34 ], [ -0.384, -1.16 ], [ -0.892, -0.808 ], [ -0.876, 0.66 ], [ 0.184, 0.672 ], [ -1.116, -1.808 ], [ 1.628, -1.976 ], [ -1.256, 1.132 ] ],
[ [ -0.556, 1.776 ], [ -1.572, 0.688 ], [ 1.62, -1.1 ], [ 1.652, -1.76 ], [ 1.98, 1.256 ], [ 0.52, -0.684 ], [ -1.564, 0.488 ], [ 1.916, 1.012 ] ],
[ [ -1.476, 0.52 ], [ -1.516, -0.124 ], [ -1.856, -0.968 ], [ -1.424, 0.82 ], [ -0.628, 1.144 ], [ -1.456, -1.552 ], [ -1.764, 0.688 ], [ -1.724, -0.26 ] ],
[ [ 1.704, 1.444 ], [ -0.636, -1.176 ], [ 0.996, -0.784 ], [ -0.464, 1.24 ], [ -0.368, 1.66 ], [ -2.0, 1.144 ], [ 0.692, -0.488 ], [ -1.664, 1.596 ] ],
[ [ -1.228, 1.956 ], [ -1.492, -1.256 ], [ 0.048, 0.012 ], [ -0.012, -1.58 ], [ -1.16, 1.836 ], [ 1.42, -1.524 ], [ 1.628, 0.072 ], [ 0.82, 0.248 ] ]
]
Value Statistics: {meanExponent=-0.08120815300031158, negative=68, min=-2.0, max=1.98, mean=-0.08206249999999997, count=128, sum=-10.503999999999996, positive=60, stdDev=1.2194777554731164, zeros=0}
Implemented Feedback: [ [ 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, ... ], [ 0.0, 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, 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, 0.0, 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=NaN, negative=0, min=0.0, max=0.0, mean=0.0, count=16384, sum=0.0, positive=0, stdDev=0.0, zeros=16384}
Measured Feedback: [ [ 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, ... ], [ 0.0, 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, 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, 0.0, 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=NaN, negative=0, min=0.0, max=0.0, mean=0.0, count=16384, sum=0.0, positive=0, stdDev=0.0, zeros=16384}
Feedback Error: [ [ 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, ... ], [ 0.0, 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, 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, 0.0, 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=NaN, negative=0, min=0.0, max=0.0, mean=0.0, count=16384, sum=0.0, positive=0, stdDev=0.0, zeros=16384}

Returns

    {
      "absoluteTol" : {
        "count" : 20480,
        "sum" : 3.1756819396377978E-12,
        "min" : 0.0,
        "max" : 1.1013412404281553E-13,
        "sumOfSquare" : 3.421240442136952E-25,
        "standardDeviation" : 4.084266297402018E-15,
        "average" : 1.5506259470887685E-16
      },
      "relativeTol" : {
        "count" : 32,
        "sum" : 1.5878409698189833E-12,
        "min" : 2.9976021664879317E-15,
        "max" : 5.50670620214108E-14,
        "sumOfSquare" : 8.553101105343314E-26,
        "standardDeviation" : 1.451539485400742E-14,
        "average" : 4.962003030684323E-14
      }
    }

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" : 20480,
        "sum" : 3.1756819396377978E-12,
        "min" : 0.0,
        "max" : 1.1013412404281553E-13,
        "sumOfSquare" : 3.421240442136952E-25,
        "standardDeviation" : 4.084266297402018E-15,
        "average" : 1.5506259470887685E-16
      },
      "relativeTol" : {
        "count" : 32,
        "sum" : 1.5878409698189833E-12,
        "min" : 2.9976021664879317E-15,
        "max" : 5.50670620214108E-14,
        "sumOfSquare" : 8.553101105343314E-26,
        "standardDeviation" : 1.451539485400742E-14,
        "average" : 4.962003030684323E-14
      }
    }

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: 1.5506e-16 +- 4.0843e-15 [0.0000e+00 - 1.1013e-13] (20480#)
relativeTol: 4.9620e-14 +- 1.4515e-14 [2.9976e-15 - 5.5067e-14] (32#)

Frozen and Alive Status

SingleDerivativeTester.java:156 executed in 0.00 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=1.5506e-16 +- 4.0843e-15 [0.0000e+00 - 1.1013e-13] (20480#), relativeTol=4.9620e-14 +- 1.4515e-14 [2.9976e-15 - 5.5067e-14] (32#)}OK
 
  {
    "result": "OK",
    "performance": {
      "execution_time": "0.453",
      "gc_time": "0.136"
    },
    "created_on": 1586739440807,
    "file_name": "derivativeTest",
    "report": {
      "simpleName": "Basic",
      "canonicalName": "com.simiacryptus.mindseye.layers.java.PhotoUnpoolingLayerTest.Basic",
      "link": "https://github.com/SimiaCryptus/mindseye-java/tree/93db34cedee48c0202777a2b25deddf1dfaf5731/src/test/java/com/simiacryptus/mindseye/layers/java/PhotoUnpoolingLayerTest.java",
      "javaDoc": ""
    },
    "archive": "s3://code.simiacrypt.us/tests/com/simiacryptus/mindseye/layers/java/PhotoUnpoolingLayer/Basic/derivativeTest/202004135720",
    "id": "dd30f414-5116-44fd-a9d2-5ad90f386904",
    "report_type": "Components",
    "display_name": "Derivative Validation",
    "target": {
      "simpleName": "PhotoUnpoolingLayer",
      "canonicalName": "com.simiacryptus.mindseye.layers.java.PhotoUnpoolingLayer",
      "link": "https://github.com/SimiaCryptus/mindseye-java/tree/93db34cedee48c0202777a2b25deddf1dfaf5731/src/main/java/com/simiacryptus/mindseye/layers/java/PhotoUnpoolingLayer.java",
      "javaDoc": ""
    }
  }