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 1586189103474599936

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 ] ]
]
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}
Output: [
[ [ 1.288 ], [ 0.048 ] ],
[ [ 2.464 ], [ 2.02 ] ]
]
Outputs Statistics: {meanExponent=-0.12796270666540197, negative=0, min=0.048, max=2.464, mean=1.455, count=4, sum=5.82, positive=4, stdDev=0.9144457337644479, zeros=0}

Feedback Validation

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

SingleDerivativeTester.java:117 executed in 0.19 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: [ [ 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, 1.0 ], [ 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, 1.0 ], ... ]
Implemented Statistics: {meanExponent=0.0, negative=0, min=0.0, max=1.0, mean=0.14583333333333334, count=48, sum=7.0, positive=7, stdDev=0.3529390488770295, zeros=41}
Measured Feedback: [ [ 0.9999999999998899, 0.0, 0.0, 0.0 ], [ 0.0, 1.0000000000021103, 0.0, 0.0 ], [ 0.0, 0.0, 0.0, 0.0 ], [ 0.0, 0.0, 0.0, 1.0000000000021103 ], [ 0.0, 0.0, 0.0, 0.0 ], [ 0.0, 1.0000000000021103, 0.0, 0.0 ], [ 0.0, 0.0, 1.0000000000000286, 0.0 ], [ 0.0, 0.0, 0.0, 1.0000000000021103 ], ... ]
Measured Statistics: {meanExponent=5.118236909259363E-13, negative=0, min=0.0, max=1.0000000000021103, mean=0.1458333333335052, count=48, sum=7.000000000008249, positive=7, stdDev=0.3529390488774454, zeros=41}
Feedback Error: [ [ -1.1013412404281553E-13, 0.0, 0.0, 0.0 ], [ 0.0, 2.1103119252074976E-12, 0.0, 0.0 ], [ 0.0, 0.0, 0.0, 0.0 ], [ 0.0, 0.0, 0.0, 2.1103119252074976E-12 ], [ 0.0, 0.0, 0.0, 0.0 ], [ 0.0, 2.1103119252074976E-12, 0.0, 0.0 ], [ 0.0, 0.0, 2.864375403532904E-14, 0.0 ], [ 0.0, 0.0, 0.0, 2.1103119252074976E-12 ], ... ]
Error Statistics: {meanExponent=-12.308819949694435, negative=2, min=-1.1013412404281553E-13, max=2.1103119252074976E-12, mean=1.718671501412435E-13, count=48, sum=8.249623206779688E-12, positive=5, stdDev=5.84895039936052E-13, zeros=41}

Returns

    {
      "absoluteTol" : {
        "count" : 48,
        "sum" : 8.69015970295095E-12,
        "min" : 0.0,
        "max" : 2.1103119252074976E-12,
        "sumOfSquare" : 1.7838745201894492E-23,
        "standardDeviation" : 5.821196056269454E-13,
        "average" : 1.8104499381147812E-13
      },
      "relativeTol" : {
        "count" : 7,
        "sum" : 4.3450798514710274E-12,
        "min" : 1.4321877017664317E-14,
        "max" : 1.0551559626026354E-12,
        "sumOfSquare" : 4.459686300464226E-24,
        "standardDeviation" : 5.017944368858565E-13,
        "average" : 6.207256930672896E-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" : 48,
        "sum" : 8.69015970295095E-12,
        "min" : 0.0,
        "max" : 2.1103119252074976E-12,
        "sumOfSquare" : 1.7838745201894492E-23,
        "standardDeviation" : 5.821196056269454E-13,
        "average" : 1.8104499381147812E-13
      },
      "relativeTol" : {
        "count" : 7,
        "sum" : 4.3450798514710274E-12,
        "min" : 1.4321877017664317E-14,
        "max" : 1.0551559626026354E-12,
        "sumOfSquare" : 4.459686300464226E-24,
        "standardDeviation" : 5.017944368858565E-13,
        "average" : 6.207256930672896E-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: 1.8104e-13 +- 5.8212e-13 [0.0000e+00 - 2.1103e-12] (48#)
relativeTol: 6.2073e-13 +- 5.0179e-13 [1.4322e-14 - 1.0552e-12] (7#)

Frozen and Alive Status

SingleDerivativeTester.java:156 executed in 0.03 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=1.8104e-13 +- 5.8212e-13 [0.0000e+00 - 2.1103e-12] (48#), relativeTol=6.2073e-13 +- 5.0179e-13 [1.4322e-14 - 1.0552e-12] (7#)}OK
 
  {
    "result": "OK",
    "performance": {
      "execution_time": "0.443",
      "gc_time": "0.170"
    },
    "created_on": 1586745680514,
    "file_name": "derivativeTest",
    "report": {
      "simpleName": "Basic",
      "canonicalName": "com.simiacryptus.mindseye.layers.cudnn.ImgLinearSubnetLayerTest.Basic",
      "link": "https://github.com/SimiaCryptus/mindseye-cudnn/tree/59d5b3318556370acb2d83ee6ec123ce0fc6974f/src/test/java/com/simiacryptus/mindseye/layers/cudnn/ImgLinearSubnetLayerTest.java",
      "javaDoc": ""
    },
    "archive": "s3://code.simiacrypt.us/tests/com/simiacryptus/mindseye/layers/cudnn/ImgLinearSubnetLayer/Basic/derivativeTest/202004134120",
    "id": "621cc415-f0e9-4291-bf18-85e2efceec08",
    "report_type": "Components",
    "display_name": "Derivative Validation",
    "target": {
      "simpleName": "ImgLinearSubnetLayer",
      "canonicalName": "com.simiacryptus.mindseye.layers.cudnn.ImgLinearSubnetLayer",
      "link": "https://github.com/SimiaCryptus/mindseye-cudnn/tree/59d5b3318556370acb2d83ee6ec123ce0fc6974f/src/main/java/com/simiacryptus/mindseye/layers/cudnn/ImgLinearSubnetLayer.java",
      "javaDoc": ""
    }
  }