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 2852210666235256832

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)));

Inputs: [
[ [ 0.08 ], [ -0.608 ], [ 1.208 ], [ -0.852 ] ],
[ [ 0.7 ], [ 1.764 ], [ -1.72 ], [ 1.912 ] ],
[ [ -0.128 ], [ 0.048 ], [ -1.028 ], [ -1.688 ] ],
[ [ 0.496 ], [ 1.524 ], [ -0.384 ], [ -0.804 ] ]
]
Inputs Statistics: {meanExponent=-0.20600101677773192, negative=8, min=-1.72, max=1.912, mean=0.0325, count=16, sum=0.52, positive=8, stdDev=1.1193595266937248, zeros=0}
Output: [
[ [ -0.608 ], [ 1.208 ] ],
[ [ 1.764 ], [ -1.72 ] ]
]
Outputs Statistics: {meanExponent=0.08699938531529944, negative=2, min=-1.72, max=1.764, mean=0.16099999999999998, count=4, sum=0.6439999999999999, positive=2, stdDev=1.3960068051410064, zeros=0}

Feedback Validation

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

SingleDerivativeTester.java:117 executed in 0.13 seconds (0.000 gc):

        return testFeedback(
            statistics,
            component.addRef(),
            RefUtil.addRef(inputPrototype),
            outputPrototype.addRef());
      },
      outputPrototype.addRef(),
      RefUtil.addRef(inputPrototype),
      component.addRef()));

Feedback for input 0
Inputs Values: [
[ [ 0.08 ], [ -0.608 ], [ 1.208 ], [ -0.852 ] ],
[ [ 0.7 ], [ 1.764 ], [ -1.72 ], [ 1.912 ] ],
[ [ -0.128 ], [ 0.048 ], [ -1.028 ], [ -1.688 ] ],
[ [ 0.496 ], [ 1.524 ], [ -0.384 ], [ -0.804 ] ]
]
Value Statistics: {meanExponent=-0.20600101677773192, negative=8, min=-1.72, max=1.912, mean=0.0325, count=16, sum=0.52, positive=8, stdDev=1.1193595266937248, 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 ], [ 1.0, 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 ], ... ]
Implemented Statistics: {meanExponent=0.0, negative=0, min=0.0, max=1.0, mean=0.0625, count=64, sum=4.0, positive=4, stdDev=0.24206145913796356, zeros=60}
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 ], [ 0.9999999999998899, 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 ], ... ]
Measured Statistics: {meanExponent=-4.7830642341045674E-14, negative=0, min=0.0, max=0.9999999999998899, mean=0.06249999999999312, count=64, sum=3.9999999999995595, positive=4, stdDev=0.2420614591379369, zeros=60}
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 ], [ -1.1013412404281553E-13, 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 ], ... ]
Error Statistics: {meanExponent=-12.958078098036825, negative=4, min=-1.1013412404281553E-13, max=0.0, mean=-6.8833827526759706E-15, count=64, sum=-4.405364961712621E-13, positive=0, stdDev=2.66592267666854E-14, zeros=60}

Returns

    {
      "absoluteTol" : {
        "count" : 64,
        "sum" : 4.405364961712621E-13,
        "min" : 0.0,
        "max" : 1.1013412404281553E-13,
        "sumOfSquare" : 4.851810111471311E-26,
        "standardDeviation" : 2.66592267666854E-14,
        "average" : 6.8833827526759706E-15
      },
      "relativeTol" : {
        "count" : 4,
        "sum" : 2.202682480856432E-13,
        "min" : 5.50670620214108E-14,
        "max" : 5.50670620214108E-14,
        "sumOfSquare" : 1.2129525278679615E-26,
        "standardDeviation" : 0.0,
        "average" : 5.50670620214108E-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" : 64,
        "sum" : 4.405364961712621E-13,
        "min" : 0.0,
        "max" : 1.1013412404281553E-13,
        "sumOfSquare" : 4.851810111471311E-26,
        "standardDeviation" : 2.66592267666854E-14,
        "average" : 6.8833827526759706E-15
      },
      "relativeTol" : {
        "count" : 4,
        "sum" : 2.202682480856432E-13,
        "min" : 5.50670620214108E-14,
        "max" : 5.50670620214108E-14,
        "sumOfSquare" : 1.2129525278679615E-26,
        "standardDeviation" : 0.0,
        "average" : 5.50670620214108E-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));

Finite-Difference Derivative Accuracy:
absoluteTol: 6.8834e-15 +- 2.6659e-14 [0.0000e+00 - 1.1013e-13] (64#)
relativeTol: 5.5067e-14 +- 0.0000e+00 [5.5067e-14 - 5.5067e-14] (4#)

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

  {
    "result": "OK",
    "performance": {
      "execution_time": "0.329",
      "gc_time": "0.131"
    },
    "created_on": 1586736980740,
    "file_name": "derivativeTest",
    "report": {
      "simpleName": "Left",
      "canonicalName": "com.simiacryptus.mindseye.layers.cudnn.ImgCropLayerTest.Left",
      "link": "https://github.com/SimiaCryptus/mindseye-cudnn/tree/59d5b3318556370acb2d83ee6ec123ce0fc6974f/src/test/java/com/simiacryptus/mindseye/layers/cudnn/ImgCropLayerTest.java",
      "javaDoc": ""
    },
    "archive": "s3://code.simiacrypt.us/tests/com/simiacryptus/mindseye/layers/cudnn/ImgCropLayer/Left/derivativeTest/202004131620",
    "id": "f0086482-6c1d-4f58-ad0d-4786195bc178",
    "report_type": "Components",
    "display_name": "Derivative Validation",
    "target": {
      "simpleName": "ImgCropLayer",
      "canonicalName": "com.simiacryptus.mindseye.layers.cudnn.ImgCropLayer",
      "link": "https://github.com/SimiaCryptus/mindseye-cudnn/tree/59d5b3318556370acb2d83ee6ec123ce0fc6974f/src/main/java/com/simiacryptus/mindseye/layers/cudnn/ImgCropLayer.java",
      "javaDoc": ""
    }
  }