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 1611957424183790592

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 ], [ 1.208 ], [ 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: [
[ [ 1.2079999446868896 ], [ 1.1080000400543213 ], [ 1.8760000467300415 ], [ 1.3680000305175781 ] ],
[ [ 0.4959999918937683 ], [ 1.0479999780654907 ], [ 0.3919999897480011 ], [ -0.03200000151991844 ] ],
[ [ 1.9119999408721924 ], [ 1.555999994277954 ], [ -0.5559999942779541 ], [ 1.6519999504089355 ] ],
[ [ 1.5240000486373901 ], [ 1.555999994277954 ], [ 1.7039999961853027 ], [ 0.9959999918937683 ] ]
]
Outputs Statistics: {meanExponent=-0.038030598820032034, negative=2, min=-0.5559999942779541, max=1.9119999408721924, mean=1.1129999964032322, count=16, sum=17.807999942451715, positive=14, stdDev=0.6851503469707089, zeros=0}

Feedback Validation

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

SingleDerivativeTester.java:117 executed in 0.82 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: [ [ 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ... ], [ 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 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, 0.0, 0.0, 0.0, 0.0, 0.0, ... ], [ 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ... ], [ 0.0, 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, ... ], ... ]
Implemented Statistics: {meanExponent=0.0, negative=0, min=0.0, max=1.0, mean=0.015625, count=1024, sum=16.0, positive=16, stdDev=0.12401959270615269, zeros=1008}
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, 1.0000169277191162, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ... ], [ 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ... ], [ 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ... ], [ 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ... ], [ 0.0, 0.0, 0.0, 0.9999275207519531, 0.0, 0.0, 0.0, 0.0, ... ], ... ]
Measured Statistics: {meanExponent=1.284086200813479E-6, negative=0, min=0.0, max=1.000046730041504, mean=0.015625046216882765, count=1024, sum=16.00004732608795, positive=16, stdDev=0.12401995968722661, zeros=1008}
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, 1.6927719116210938E-5, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ... ], [ 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ... ], [ 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ... ], [ 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ... ], [ 0.0, 0.0, 0.0, -7.2479248046875E-5, 0.0, 0.0, 0.0, 0.0, ... ], ... ]
Error Statistics: {meanExponent=-4.493034281062041, negative=6, min=-7.2479248046875E-5, max=4.673004150390625E-5, mean=4.621688276529312E-8, count=1024, sum=4.7326087951660156E-5, positive=10, stdDev=6.027716653076009E-6, zeros=1008}

Returns

    {
      "absoluteTol" : {
        "count" : 1024,
        "sum" : 6.786584854125977E-4,
        "min" : 0.0,
        "max" : 7.2479248046875E-5,
        "sumOfSquare" : 3.720755614722293E-8,
        "standardDeviation" : 5.991349035927964E-6,
        "average" : 6.627524271607399E-7
      },
      "relativeTol" : {
        "count" : 16,
        "sum" : 3.393306133368825E-4,
        "min" : 1.0132779344484278E-6,
        "max" : 3.624093738138246E-5,
        "sumOfSquare" : 9.30208987700565E-9,
        "standardDeviation" : 1.1471461342396603E-5,
        "average" : 2.1208163333555158E-5
      }
    }

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" : 6.786584854125977E-4,
        "min" : 0.0,
        "max" : 7.2479248046875E-5,
        "sumOfSquare" : 3.720755614722293E-8,
        "standardDeviation" : 5.991349035927964E-6,
        "average" : 6.627524271607399E-7
      },
      "relativeTol" : {
        "count" : 16,
        "sum" : 3.393306133368825E-4,
        "min" : 1.0132779344484278E-6,
        "max" : 3.624093738138246E-5,
        "sumOfSquare" : 9.30208987700565E-9,
        "standardDeviation" : 1.1471461342396603E-5,
        "average" : 2.1208163333555158E-5
      }
    }

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: 6.6275e-07 +- 5.9913e-06 [0.0000e+00 - 7.2479e-05] (1024#)
relativeTol: 2.1208e-05 +- 1.1471e-05 [1.0133e-06 - 3.6241e-05] (16#)

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=6.6275e-07 +- 5.9913e-06 [0.0000e+00 - 7.2479e-05] (1024#), relativeTol=2.1208e-05 +- 1.1471e-05 [1.0133e-06 - 3.6241e-05] (16#)}OK
 
  {
    "result": "OK",
    "performance": {
      "execution_time": "1.046",
      "gc_time": "0.169"
    },
    "created_on": 1586736166969,
    "file_name": "derivativeTest",
    "report": {
      "simpleName": "Float",
      "canonicalName": "com.simiacryptus.mindseye.layers.cudnn.PoolingLayerTest.Float",
      "link": "https://github.com/SimiaCryptus/mindseye-cudnn/tree/59d5b3318556370acb2d83ee6ec123ce0fc6974f/src/test/java/com/simiacryptus/mindseye/layers/cudnn/PoolingLayerTest.java",
      "javaDoc": ""
    },
    "archive": "s3://code.simiacrypt.us/tests/com/simiacryptus/mindseye/layers/cudnn/PoolingLayer/Float/derivativeTest/202004130246",
    "id": "c99d425f-854c-4a32-8a4f-a6c330b5a0dc",
    "report_type": "Components",
    "display_name": "Derivative Validation",
    "target": {
      "simpleName": "PoolingLayer",
      "canonicalName": "com.simiacryptus.mindseye.layers.cudnn.PoolingLayer",
      "link": "https://github.com/SimiaCryptus/mindseye-cudnn/tree/59d5b3318556370acb2d83ee6ec123ce0fc6974f/src/main/java/com/simiacryptus/mindseye/layers/cudnn/PoolingLayer.java",
      "javaDoc": ""
    }
  }