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 5077843170694076416

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 ], [ 1.208 ], [ 1.108 ], [ 1.032 ], [ 1.612 ], [ 1.552 ], [ -0.804 ], [ -1.832 ] ],
[ [ 0.7 ], [ -1.72 ], [ 0.028 ], [ 0.3 ], [ 1.64 ], [ 1.876 ], [ 0.148 ], [ 1.368 ] ],
[ [ -0.128 ], [ -1.028 ], [ -0.712 ], [ 0.636 ], [ 0.392 ], [ -0.408 ], [ -0.032 ], [ -1.54 ] ],
[ [ 0.496 ], [ -0.384 ], [ 1.048 ], [ -0.176 ], [ 0.092 ], [ -0.384 ], [ -0.892 ], [ -0.876 ] ],
[ [ -0.608 ], [ -0.852 ], [ -1.616 ], [ 1.556 ], [ -0.556 ], [ -1.572 ], [ 1.62 ], [ 1.652 ] ],
[ [ 1.764 ], [ 1.912 ], [ 1.356 ], [ 0.788 ], [ -1.476 ], [ -1.516 ], [ -1.856 ], [ -1.424 ] ],
[ [ 0.048 ], [ -1.688 ], [ 1.512 ], [ -0.768 ], [ 1.704 ], [ -0.636 ], [ 0.996 ], [ -0.464 ] ],
[ [ 1.524 ], [ -0.804 ], [ 1.556 ], [ -0.068 ], [ -1.228 ], [ -1.492 ], [ 0.048 ], [ -0.012 ] ]
]
Inputs Statistics: {meanExponent=-0.1923523558471011, negative=32, min=-1.856, max=1.912, mean=0.059375000000000025, count=64, sum=3.8000000000000016, positive=32, stdDev=1.1518728703181615, zeros=0}
Output: [
[ [ 0.06699999999999995 ], [ 0.617 ], [ 1.67 ], [ -0.28 ] ],
[ [ -0.261 ], [ 0.199 ], [ -0.07699999999999999 ], [ -0.835 ] ],
[ [ 0.5539999999999999 ], [ 0.521 ], [ -1.28 ], [ -0.0020000000000000018 ] ],
[ [ -0.22999999999999998 ], [ 0.558 ], [ -0.41300000000000003 ], [ 0.142 ] ]
]
Outputs Statistics: {meanExponent=-0.5903066788773516, negative=8, min=-1.28, max=1.67, mean=0.059375000000000004, count=16, sum=0.9500000000000001, positive=8, stdDev=0.6508986936344242, zeros=0}

Feedback Validation

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

SingleDerivativeTester.java:117 executed in 0.05 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 ], [ 1.208 ], [ 1.108 ], [ 1.032 ], [ 1.612 ], [ 1.552 ], [ -0.804 ], [ -1.832 ] ],
[ [ 0.7 ], [ -1.72 ], [ 0.028 ], [ 0.3 ], [ 1.64 ], [ 1.876 ], [ 0.148 ], [ 1.368 ] ],
[ [ -0.128 ], [ -1.028 ], [ -0.712 ], [ 0.636 ], [ 0.392 ], [ -0.408 ], [ -0.032 ], [ -1.54 ] ],
[ [ 0.496 ], [ -0.384 ], [ 1.048 ], [ -0.176 ], [ 0.092 ], [ -0.384 ], [ -0.892 ], [ -0.876 ] ],
[ [ -0.608 ], [ -0.852 ], [ -1.616 ], [ 1.556 ], [ -0.556 ], [ -1.572 ], [ 1.62 ], [ 1.652 ] ],
[ [ 1.764 ], [ 1.912 ], [ 1.356 ], [ 0.788 ], [ -1.476 ], [ -1.516 ], [ -1.856 ], [ -1.424 ] ],
[ [ 0.048 ], [ -1.688 ], [ 1.512 ], [ -0.768 ], [ 1.704 ], [ -0.636 ], [ 0.996 ], [ -0.464 ] ],
[ [ 1.524 ], [ -0.804 ], [ 1.556 ], [ -0.068 ], [ -1.228 ], [ -1.492 ], [ 0.048 ], [ -0.012 ] ]
]
Value Statistics: {meanExponent=-0.1923523558471011, negative=32, min=-1.856, max=1.912, mean=0.059375000000000025, count=64, sum=3.8000000000000016, positive=32, stdDev=1.1518728703181615, zeros=0}
Implemented Feedback: [ [ 0.25, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ... ], [ 0.25, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ... ], [ 0.0, 0.25, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ... ], [ 0.0, 0.25, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ... ], [ 0.0, 0.0, 0.25, 0.0, 0.0, 0.0, 0.0, 0.0, ... ], [ 0.0, 0.0, 0.25, 0.0, 0.0, 0.0, 0.0, 0.0, ... ], [ 0.0, 0.0, 0.0, 0.25, 0.0, 0.0, 0.0, 0.0, ... ], [ 0.0, 0.0, 0.0, 0.25, 0.0, 0.0, 0.0, 0.0, ... ], ... ]
Implemented Statistics: {meanExponent=-0.6020599913279624, negative=0, min=0.0, max=0.25, mean=0.015625, count=1024, sum=16.0, positive=64, stdDev=0.06051536478449089, zeros=960}
Measured Feedback: [ [ 0.2500000000005276, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ... ], [ 0.2500000000005276, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ... ], [ 0.0, 0.24999999999997247, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ... ], [ 0.0, 0.24999999999997247, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ... ], [ 0.0, 0.0, 0.2500000000005276, 0.0, 0.0, 0.0, 0.0, 0.0, ... ], [ 0.0, 0.0, 0.2500000000005276, 0.0, 0.0, 0.0, 0.0, 0.0, ... ], [ 0.0, 0.0, 0.0, 0.24999999999997247, 0.0, 0.0, 0.0, 0.0, ... ], [ 0.0, 0.0, 0.0, 0.24999999999997247, 0.0, 0.0, 0.0, 0.0, ... ], ... ]
Measured Statistics: {meanExponent=-0.602059991327935, negative=0, min=0.0, max=0.2500000000010827, mean=0.01562500000000098, count=1024, sum=16.000000000001002, positive=64, stdDev=0.06051536478449472, zeros=960}
Feedback Error: [ [ 5.275779813018744E-13, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ... ], [ 5.275779813018744E-13, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ... ], [ 0.0, -2.7533531010703882E-14, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ... ], [ 0.0, -2.7533531010703882E-14, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ... ], [ 0.0, 0.0, 5.275779813018744E-13, 0.0, 0.0, 0.0, 0.0, 0.0, ... ], [ 0.0, 0.0, 5.275779813018744E-13, 0.0, 0.0, 0.0, 0.0, 0.0, ... ], [ 0.0, 0.0, 0.0, -2.7533531010703882E-14, 0.0, 0.0, 0.0, 0.0, ... ], [ 0.0, 0.0, 0.0, -2.7533531010703882E-14, 0.0, 0.0, 0.0, 0.0, ... ], ... ]
Error Statistics: {meanExponent=-12.823116423548578, negative=44, min=-5.826450433232822E-13, max=1.0826894936144527E-12, mean=9.896597430447684E-16, count=1024, sum=1.0134115768778429E-12, positive=20, stdDev=1.0785767269708976E-13, zeros=960}

Returns

    {
      "absoluteTol" : {
        "count" : 1024,
        "sum" : 2.119993069982229E-11,
        "min" : 0.0,
        "max" : 1.0826894936144527E-12,
        "sumOfSquare" : 1.1913479153704494E-23,
        "standardDeviation" : 1.0585669748994082E-13,
        "average" : 2.0703057324045204E-14
      },
      "relativeTol" : {
        "count" : 64,
        "sum" : 4.2399861399640546E-11,
        "min" : 5.50670620214108E-14,
        "max" : 2.1653789872242165E-12,
        "sumOfSquare" : 4.765391661480367E-23,
        "standardDeviation" : 5.528915504528752E-13,
        "average" : 6.624978343693835E-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" : 1024,
        "sum" : 2.119993069982229E-11,
        "min" : 0.0,
        "max" : 1.0826894936144527E-12,
        "sumOfSquare" : 1.1913479153704494E-23,
        "standardDeviation" : 1.0585669748994082E-13,
        "average" : 2.0703057324045204E-14
      },
      "relativeTol" : {
        "count" : 64,
        "sum" : 4.2399861399640546E-11,
        "min" : 5.50670620214108E-14,
        "max" : 2.1653789872242165E-12,
        "sumOfSquare" : 4.765391661480367E-23,
        "standardDeviation" : 5.528915504528752E-13,
        "average" : 6.624978343693835E-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: 2.0703e-14 +- 1.0586e-13 [0.0000e+00 - 1.0827e-12] (1024#)
relativeTol: 6.6250e-13 +- 5.5289e-13 [5.5067e-14 - 2.1654e-12] (64#)

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=2.0703e-14 +- 1.0586e-13 [0.0000e+00 - 1.0827e-12] (1024#), relativeTol=6.6250e-13 +- 5.5289e-13 [5.5067e-14 - 2.1654e-12] (64#)}OK
 
  {
    "result": "OK",
    "performance": {
      "execution_time": "0.273",
      "gc_time": "0.182"
    },
    "created_on": 1586736597705,
    "file_name": "derivativeTest",
    "report": {
      "simpleName": "Basic",
      "canonicalName": "com.simiacryptus.mindseye.layers.java.AvgPoolingLayerTest.Basic",
      "link": "https://github.com/SimiaCryptus/mindseye-java/tree/93db34cedee48c0202777a2b25deddf1dfaf5731/src/test/java/com/simiacryptus/mindseye/layers/java/AvgPoolingLayerTest.java",
      "javaDoc": ""
    },
    "archive": "s3://code.simiacrypt.us/tests/com/simiacryptus/mindseye/layers/java/AvgPoolingLayer/Basic/derivativeTest/202004130957",
    "id": "31dad6b4-0751-45bf-b7fe-c30591e5810f",
    "report_type": "Components",
    "display_name": "Derivative Validation",
    "target": {
      "simpleName": "AvgPoolingLayer",
      "canonicalName": "com.simiacryptus.mindseye.layers.java.AvgPoolingLayer",
      "link": "https://github.com/SimiaCryptus/mindseye-java/tree/93db34cedee48c0202777a2b25deddf1dfaf5731/src/main/java/com/simiacryptus/mindseye/layers/java/AvgPoolingLayer.java",
      "javaDoc": ""
    }
  }