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 4748537955811446784

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, -0.608, 1.764, 0.048, 1.524, ... ] ]
]
Inputs Statistics: {meanExponent=-0.21478244038380173, negative=13, min=-1.72, max=1.912, mean=0.253125, count=32, sum=8.1, positive=19, stdDev=1.0492889184466785, zeros=0}
Output: [
[ [ 0.08, 0.7, -0.128, 0.496, -0.608, 1.764, 0.048, 1.524, ... ] ]
]
Outputs Statistics: {meanExponent=-0.21478244038380173, negative=13, min=-1.72, max=1.912, mean=0.253125, count=32, sum=8.1, positive=19, stdDev=1.0492889184466785, zeros=0}

Feedback Validation

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

SingleDerivativeTester.java:117 executed in 0.07 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, -0.608, 1.764, 0.048, 1.524, ... ] ]
]
Value Statistics: {meanExponent=-0.21478244038380173, negative=13, min=-1.72, max=1.912, mean=0.253125, count=32, sum=8.1, positive=19, stdDev=1.0492889184466785, zeros=0}
Implemented Feedback: [ [ 1.0, 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, 1.0, 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, 1.0, 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, 1.0, 0.0, ... ], [ 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, ... ], ... ]
Implemented Statistics: {meanExponent=0.0, negative=0, min=0.0, max=1.0, mean=0.03125, count=1024, sum=32.0, positive=32, stdDev=0.17399263633843817, zeros=992}
Measured Feedback: [ [ 1.0000000000000286, 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, 0.0, 0.0, ... ], [ 0.0, 0.0, 0.9999999999998899, 0.0, 0.0, 0.0, 0.0, 0.0, ... ], [ 0.0, 0.0, 0.0, 0.9999999999998899, 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, 0.0, 0.0, 0.0, 0.0, 0.9999999999998899, 0.0, 0.0, ... ], [ 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0000000000000286, 0.0, ... ], [ 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.9999999999998899, ... ], ... ]
Measured Statistics: {meanExponent=-4.076694364854251E-14, negative=0, min=0.0, max=1.0000000000000286, mean=0.031249999999997065, count=1024, sum=31.999999999996994, positive=32, stdDev=0.17399263633842185, zeros=992}
Feedback Error: [ [ 2.864375403532904E-14, 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, 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, -1.1013412404281553E-13, 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, 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, 2.864375403532904E-14, 0.0, ... ], [ 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, -1.1013412404281553E-13, ... ], ... ]
Error Statistics: {meanExponent=-13.052415404627574, negative=29, min=-1.1013412404281553E-13, max=2.864375403532904E-14, mean=-2.9334173978767808E-15, count=1024, sum=-3.0038194154258235E-12, positive=3, stdDev=1.804163446856383E-14, zeros=992}

Returns

    {
      "absoluteTol" : {
        "count" : 1024,
        "sum" : 3.1756819396377978E-12,
        "min" : 0.0,
        "max" : 1.1013412404281553E-13,
        "sumOfSquare" : 3.421240442136952E-25,
        "standardDeviation" : 1.801354347751427E-14,
        "average" : 3.101251894177537E-15
      },
      "relativeTol" : {
        "count" : 32,
        "sum" : 1.5878409698189833E-12,
        "min" : 2.9976021664879317E-15,
        "max" : 5.50670620214108E-14,
        "sumOfSquare" : 8.553101105343314E-26,
        "standardDeviation" : 1.451539485400742E-14,
        "average" : 4.962003030684323E-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" : 1024,
        "sum" : 3.1756819396377978E-12,
        "min" : 0.0,
        "max" : 1.1013412404281553E-13,
        "sumOfSquare" : 3.421240442136952E-25,
        "standardDeviation" : 1.801354347751427E-14,
        "average" : 3.101251894177537E-15
      },
      "relativeTol" : {
        "count" : 32,
        "sum" : 1.5878409698189833E-12,
        "min" : 2.9976021664879317E-15,
        "max" : 5.50670620214108E-14,
        "sumOfSquare" : 8.553101105343314E-26,
        "standardDeviation" : 1.451539485400742E-14,
        "average" : 4.962003030684323E-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: 3.1013e-15 +- 1.8014e-14 [0.0000e+00 - 1.1013e-13] (1024#)
relativeTol: 4.9620e-14 +- 1.4515e-14 [2.9976e-15 - 5.5067e-14] (32#)

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=3.1013e-15 +- 1.8014e-14 [0.0000e+00 - 1.1013e-13] (1024#), relativeTol=4.9620e-14 +- 1.4515e-14 [2.9976e-15 - 5.5067e-14] (32#)}OK
 
  {
    "result": "OK",
    "performance": {
      "execution_time": "0.204",
      "gc_time": "0.104"
    },
    "created_on": 1586735871141,
    "file_name": "derivativeTest",
    "report": {
      "simpleName": "Basic1",
      "canonicalName": "com.simiacryptus.mindseye.layers.java.ReshapeLayerTest.Basic1",
      "link": "https://github.com/SimiaCryptus/mindseye-java/tree/93db34cedee48c0202777a2b25deddf1dfaf5731/src/test/java/com/simiacryptus/mindseye/layers/java/ReshapeLayerTest.java",
      "javaDoc": ""
    },
    "archive": "s3://code.simiacrypt.us/tests/com/simiacryptus/mindseye/layers/java/ReshapeLayer/Basic1/derivativeTest/202004125751",
    "id": "95eb1d74-0e07-43f1-89b9-ca284c96b01a",
    "report_type": "Components",
    "display_name": "Derivative Validation",
    "target": {
      "simpleName": "ReshapeLayer",
      "canonicalName": "com.simiacryptus.mindseye.layers.java.ReshapeLayer",
      "link": "https://github.com/SimiaCryptus/mindseye-java/tree/93db34cedee48c0202777a2b25deddf1dfaf5731/src/main/java/com/simiacryptus/mindseye/layers/java/ReshapeLayer.java",
      "javaDoc": ""
    }
  }