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 2592694347362073600

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: [
[ [ 0.6799999999999999 ], [ -1.108 ] ],
[ [ 0.744 ], [ 1.6360000000000001 ] ]
]
Outputs Statistics: {meanExponent=-0.009398773005042459, negative=1, min=-1.108, max=1.6360000000000001, mean=0.488, count=4, sum=1.952, positive=3, stdDev=0.9959317245675028, zeros=0}

Feedback Validation

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

SingleDerivativeTester.java:117 executed in 0.03 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, 1.0, 0.0 ], [ 0.0, 0.0, 0.0, 1.0 ], [ 1.0, 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=0.25, count=48, sum=12.0, positive=12, stdDev=0.4330127018922193, zeros=36}
Measured Feedback: [ [ 1.0000000000021103, 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, 0.9999999999976694 ], [ 0.9999999999998899, 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, 0.9999999999976694 ], ... ]
Measured Statistics: {meanExponent=-2.0855188676696804E-13, negative=0, min=0.0, max=1.0000000000021103, mean=0.24999999999987996, count=48, sum=11.999999999994237, positive=12, stdDev=0.4330127018920114, zeros=36}
Feedback Error: [ [ 2.1103119252074976E-12, 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, -2.3305801732931286E-12 ], [ -1.1013412404281553E-13, 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, -2.3305801732931286E-12 ], ... ]
Error Statistics: {meanExponent=-12.519823832389234, negative=11, min=-2.3305801732931286E-12, max=2.1103119252074976E-12, mean=-1.2005211639613359E-13, count=48, sum=-5.7625015870144125E-12, positive=1, stdDev=6.479690055776576E-13, zeros=36}

Returns

    {
      "absoluteTol" : {
        "count" : 48,
        "sum" : 9.983125437429408E-12,
        "min" : 0.0,
        "max" : 2.3305801732931286E-12,
        "sumOfSquare" : 2.084526445634349E-23,
        "standardDeviation" : 6.253158578277041E-13,
        "average" : 2.0798177994644598E-13
      },
      "relativeTol" : {
        "count" : 12,
        "sum" : 4.991562718717689E-12,
        "min" : 5.50670620214108E-14,
        "max" : 1.1652900866479222E-12,
        "sumOfSquare" : 5.211316114093019E-24,
        "standardDeviation" : 5.11126852828249E-13,
        "average" : 4.159635598931407E-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" : 9.983125437429408E-12,
        "min" : 0.0,
        "max" : 2.3305801732931286E-12,
        "sumOfSquare" : 2.084526445634349E-23,
        "standardDeviation" : 6.253158578277041E-13,
        "average" : 2.0798177994644598E-13
      },
      "relativeTol" : {
        "count" : 12,
        "sum" : 4.991562718717689E-12,
        "min" : 5.50670620214108E-14,
        "max" : 1.1652900866479222E-12,
        "sumOfSquare" : 5.211316114093019E-24,
        "standardDeviation" : 5.11126852828249E-13,
        "average" : 4.159635598931407E-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: 2.0798e-13 +- 6.2532e-13 [0.0000e+00 - 2.3306e-12] (48#)
relativeTol: 4.1596e-13 +- 5.1113e-13 [5.5067e-14 - 1.1653e-12] (12#)

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=2.0798e-13 +- 6.2532e-13 [0.0000e+00 - 2.3306e-12] (48#), relativeTol=4.1596e-13 +- 5.1113e-13 [5.5067e-14 - 1.1653e-12] (12#)}OK
 
  {
    "result": "OK",
    "performance": {
      "execution_time": "0.157",
      "gc_time": "0.094"
    },
    "created_on": 1586735733736,
    "file_name": "derivativeTest",
    "report": {
      "simpleName": "Basic",
      "canonicalName": "com.simiacryptus.mindseye.layers.java.ImgPixelSumLayerTest.Basic",
      "link": "https://github.com/SimiaCryptus/mindseye-java/tree/93db34cedee48c0202777a2b25deddf1dfaf5731/src/test/java/com/simiacryptus/mindseye/layers/java/ImgPixelSumLayerTest.java",
      "javaDoc": ""
    },
    "archive": "s3://code.simiacrypt.us/tests/com/simiacryptus/mindseye/layers/java/ImgPixelSumLayer/Basic/derivativeTest/202004125533",
    "id": "fc96af6b-0c0a-48f6-a9dd-a6705f381f2d",
    "report_type": "Components",
    "display_name": "Derivative Validation",
    "target": {
      "simpleName": "ImgPixelSumLayer",
      "canonicalName": "com.simiacryptus.mindseye.layers.java.ImgPixelSumLayer",
      "link": "https://github.com/SimiaCryptus/mindseye-java/tree/93db34cedee48c0202777a2b25deddf1dfaf5731/src/main/java/com/simiacryptus/mindseye/layers/java/ImgPixelSumLayer.java",
      "javaDoc": ""
    }
  }