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 5297505304973458432

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, 0.7, -0.128, 0.496 ]
Inputs Statistics: {meanExponent=-0.6122800817139336, negative=1, min=-0.128, max=0.7, mean=0.287, count=4, sum=1.148, positive=3, stdDev=0.3276141022605712, zeros=0}
Output: [ -0.62, 0.20800000000000002, -0.416, 0.828, 0.20399999999999996, -0.624 ]
Outputs Statistics: {meanExponent=-0.37460109183650653, negative=3, min=-0.624, max=0.828, mean=-0.07, count=6, sum=-0.42000000000000004, positive=3, stdDev=0.5303923076365267, zeros=0}

Feedback Validation

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

SingleDerivativeTester.java:117 executed in 0.02 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, 0.7, -0.128, 0.496 ]
Value Statistics: {meanExponent=-0.6122800817139336, negative=1, min=-0.128, max=0.7, mean=0.287, count=4, sum=1.148, positive=3, stdDev=0.3276141022605712, zeros=0}
Implemented Feedback: [ [ 1.0, 1.0, 1.0, 0.0, 0.0, 0.0 ], [ -1.0, 0.0, 0.0, 1.0, 1.0, 0.0 ], [ 0.0, -1.0, 0.0, -1.0, 0.0, 1.0 ], [ 0.0, 0.0, -1.0, 0.0, -1.0, -1.0 ] ]
Implemented Statistics: {meanExponent=0.0, negative=6, min=-1.0, max=1.0, mean=0.0, count=24, sum=0.0, positive=6, stdDev=0.7071067811865476, zeros=12}
Measured Feedback: [ [ 1.000000000001, 0.9999999999998899, 0.9999999999998899, 0.0, 0.0, 0.0 ], [ -0.9999999999998899, 0.0, 0.0, 0.9999999999998899, 0.9999999999998899, 0.0 ], [ 0.0, -0.9999999999998899, 0.0, -0.9999999999998899, 0.0, 0.9999999999998899 ], [ 0.0, 0.0, -0.9999999999998899, 0.0, -0.9999999999998899, -0.9999999999998899 ] ]
Measured Statistics: {meanExponent=-7.650331234676596E-15, negative=6, min=-0.9999999999998899, max=1.000000000001, mean=4.625929269271486E-14, count=24, sum=1.1102230246251565E-12, positive=6, stdDev=0.707106781186535, zeros=12}
Feedback Error: [ [ 1.000088900582341E-12, -1.1013412404281553E-13, -1.1013412404281553E-13, 0.0, 0.0, 0.0 ], [ 1.1013412404281553E-13, 0.0, 0.0, -1.1013412404281553E-13, -1.1013412404281553E-13, 0.0 ], [ 0.0, 1.1013412404281553E-13, 0.0, 1.1013412404281553E-13, 0.0, -1.1013412404281553E-13 ], [ 0.0, 0.0, 1.1013412404281553E-13, 0.0, 1.1013412404281553E-13, 1.1013412404281553E-13 ] ]
Error Statistics: {meanExponent=-12.8782350392574, negative=5, min=-1.1013412404281553E-13, max=1.000088900582341E-12, mean=4.625929269271486E-14, count=24, sum=1.1102230246251565E-12, positive=7, stdDev=2.1235234627884485E-13, zeros=12}

Returns

    {
      "absoluteTol" : {
        "count" : 24,
        "sum" : 2.211564265053312E-12,
        "min" : 0.0,
        "max" : 1.000088900582341E-12,
        "sumOfSquare" : 1.1336025871334566E-24,
        "standardDeviation" : 1.9683011213469128E-13,
        "average" : 9.214851104388799E-14
      },
      "relativeTol" : {
        "count" : 12,
        "sum" : 1.1057821325264392E-12,
        "min" : 5.50670620214108E-14,
        "max" : 5.000444502909205E-13,
        "sumOfSquare" : 2.834006467831178E-25,
        "standardDeviation" : 1.22985253090188E-13,
        "average" : 9.214851104386993E-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" : 24,
        "sum" : 2.211564265053312E-12,
        "min" : 0.0,
        "max" : 1.000088900582341E-12,
        "sumOfSquare" : 1.1336025871334566E-24,
        "standardDeviation" : 1.9683011213469128E-13,
        "average" : 9.214851104388799E-14
      },
      "relativeTol" : {
        "count" : 12,
        "sum" : 1.1057821325264392E-12,
        "min" : 5.50670620214108E-14,
        "max" : 5.000444502909205E-13,
        "sumOfSquare" : 2.834006467831178E-25,
        "standardDeviation" : 1.22985253090188E-13,
        "average" : 9.214851104386993E-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));
Logging 
Finite-Difference Derivative Accuracy:
absoluteTol: 9.2149e-14 +- 1.9683e-13 [0.0000e+00 - 1.0001e-12] (24#)
relativeTol: 9.2149e-14 +- 1.2299e-13 [5.5067e-14 - 5.0004e-13] (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=9.2149e-14 +- 1.9683e-13 [0.0000e+00 - 1.0001e-12] (24#), relativeTol=9.2149e-14 +- 1.2299e-13 [5.5067e-14 - 5.0004e-13] (12#)}OK
 
  {
    "result": "OK",
    "performance": {
      "execution_time": "0.154",
      "gc_time": "0.099"
    },
    "created_on": 1586738378738,
    "file_name": "derivativeTest",
    "report": {
      "simpleName": "Basic",
      "canonicalName": "com.simiacryptus.mindseye.layers.java.CrossDifferenceLayerTest.Basic",
      "link": "https://github.com/SimiaCryptus/mindseye-java/tree/93db34cedee48c0202777a2b25deddf1dfaf5731/src/test/java/com/simiacryptus/mindseye/layers/java/CrossDifferenceLayerTest.java",
      "javaDoc": ""
    },
    "archive": "s3://code.simiacrypt.us/tests/com/simiacryptus/mindseye/layers/java/CrossDifferenceLayer/Basic/derivativeTest/202004133938",
    "id": "4c39c694-5f8d-48de-81aa-2eea1446ab72",
    "report_type": "Components",
    "display_name": "Derivative Validation",
    "target": {
      "simpleName": "CrossDifferenceLayer",
      "canonicalName": "com.simiacryptus.mindseye.layers.java.CrossDifferenceLayer",
      "link": "https://github.com/SimiaCryptus/mindseye-java/tree/93db34cedee48c0202777a2b25deddf1dfaf5731/src/main/java/com/simiacryptus/mindseye/layers/java/CrossDifferenceLayer.java",
      "javaDoc": ""
    }
  }