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 6415746393373166592

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 ], [ -0.128 ], [ -0.608 ] ],
[ [ 0.7 ], [ 0.496 ], [ 1.764 ] ]
]
Inputs Statistics: {meanExponent=-0.403119694464533, negative=2, min=-0.608, max=1.764, mean=0.38399999999999995, count=6, sum=2.304, positive=4, stdDev=0.747821725636086, zeros=0}
Output: [
[ [ 0.08 ], [ -0.128 ], [ -0.608 ] ],
[ [ 0.7 ], [ 0.496 ], [ 1.764 ] ]
]
Outputs Statistics: {meanExponent=-0.403119694464533, negative=2, min=-0.608, max=1.764, mean=0.38399999999999995, count=6, sum=2.304, positive=4, stdDev=0.747821725636086, 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 ], [ -0.128 ], [ -0.608 ] ],
[ [ 0.7 ], [ 0.496 ], [ 1.764 ] ]
]
Value Statistics: {meanExponent=-0.403119694464533, negative=2, min=-0.608, max=1.764, mean=0.38399999999999995, count=6, sum=2.304, positive=4, stdDev=0.747821725636086, zeros=0}
Implemented Feedback: [ [ 1.0, 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, 1.0, 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, 1.0, 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=0.16666666666666666, count=36, sum=6.0, positive=6, stdDev=0.37267799624996495, zeros=30}
Measured Feedback: [ [ 1.0000000000000286, 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, 0.9999999999998899, 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, 0.9999999999998899, 0.0 ], [ 0.0, 0.0, 0.0, 0.0, 0.0, 0.9999999999998899 ] ]
Measured Statistics: {meanExponent=-3.7785564564448525E-14, negative=0, min=0.0, max=1.0000000000000286, mean=0.16666666666665217, count=36, sum=5.999999999999478, positive=6, stdDev=0.3726779962499325, zeros=30}
Feedback Error: [ [ 2.864375403532904E-14, 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, -1.1013412404281553E-13, 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, -1.1013412404281553E-13, 0.0 ], [ 0.0, 0.0, 0.0, 0.0, 0.0, -1.1013412404281553E-13 ] ]
Error Statistics: {meanExponent=-13.055560092401983, negative=5, min=-1.1013412404281553E-13, max=2.864375403532904E-14, mean=-1.4500746282743017E-14, count=36, sum=-5.220268661787486E-13, positive=1, stdDev=3.869334995239792E-14, zeros=30}

Returns

    {
      "absoluteTol" : {
        "count" : 36,
        "sum" : 5.793143742494067E-13,
        "min" : 0.0,
        "max" : 1.1013412404281553E-13,
        "sumOfSquare" : 6.146809103862782E-26,
        "standardDeviation" : 3.805906444872099E-14,
        "average" : 1.609206595137241E-14
      },
      "relativeTol" : {
        "count" : 6,
        "sum" : 2.8965718712471834E-13,
        "min" : 1.4321877017664317E-14,
        "max" : 5.50670620214108E-14,
        "sumOfSquare" : 1.536702275965862E-26,
        "standardDeviation" : 1.518483390403035E-14,
        "average" : 4.827619785411972E-14
      }
    }

Learning Validation

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

SingleDerivativeTester.java:133 executed in 0.01 seconds (0.000 gc):

        return testLearning(
            statistics,
            component.addRef(),
            RefUtil.addRef(inputPrototype),
            outputPrototype.addRef());
      },
      outputPrototype.addRef(),
      RefUtil.addRef(inputPrototype),
      component.addRef()));
Logging 
Learning Gradient for weight setByCoord 0
Weights: [ 1.0, 0.0 ]
Implemented Gradient: [ [ 0.08, 0.7, -0.128, 0.496, -0.608, 1.764 ], [ 1.0, 1.0, 1.0, 1.0, 1.0, 1.0 ] ]
Implemented Statistics: {meanExponent=-0.2015598472322665, negative=2, min=-0.608, max=1.764, mean=0.6920000000000001, count=12, sum=8.304, positive=10, stdDev=0.6119498890159769, zeros=0}
Measured Gradient: [ [ 0.07999999999994123, 0.700000000000145, -0.1280000000000725, 0.4959999999998299, -0.6079999999997199, 1.7639999999996547 ], [ 1.0000000000000286, 0.9999999999998899, 0.9999999999998899, 0.9999999999998899, 0.9999999999998899, 0.9999999999998899 ] ]
Measured Statistics: {meanExponent=-0.2015598472323202, negative=2, min=-0.6079999999997199, max=1.7639999999996547, mean=0.691999999999938, count=12, sum=8.303999999999256, positive=10, stdDev=0.6119498890158729, zeros=0}
Gradient Error: [ [ -5.877243136609422E-14, 1.4499512701604544E-13, -7.249756350802272E-14, -1.7008616737257398E-13, 2.80109269112927E-13, -3.452793606584237E-13 ], [ 2.864375403532904E-14, -1.1013412404281553E-13, -1.1013412404281553E-13, -1.1013412404281553E-13, -1.1013412404281553E-13, -1.1013412404281553E-13 ] ]
Error Statistics: {meanExponent=-12.943861915402332, negative=9, min=-3.452793606584237E-13, max=2.80109269112927E-13, mean=-6.196316607957424E-14, count=12, sum=-7.435579929548908E-13, positive=3, stdDev=1.5048170148011614E-13, zeros=0}

Returns

    {
      "absoluteTol" : {
        "count" : 48,
        "sum" : 2.2303686675329004E-12,
        "min" : 0.0,
        "max" : 3.452793606584237E-13,
        "sumOfSquare" : 3.7927820821009605E-25,
        "standardDeviation" : 7.577954136381941E-14,
        "average" : 4.6466013906935423E-14
      },
      "relativeTol" : {
        "count" : 18,
        "sum" : 1.833082772491115E-12,
        "min" : 1.4321877017664317E-14,
        "max" : 3.673276960382238E-13,
        "sumOfSquare" : 3.4862712505311213E-25,
        "standardDeviation" : 9.48536201719752E-14,
        "average" : 1.0183793180506194E-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: 4.6466e-14 +- 7.5780e-14 [0.0000e+00 - 3.4528e-13] (48#)
relativeTol: 1.0184e-13 +- 9.4854e-14 [1.4322e-14 - 3.6733e-13] (18#)

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=4.6466e-14 +- 7.5780e-14 [0.0000e+00 - 3.4528e-13] (48#), relativeTol=1.0184e-13 +- 9.4854e-14 [1.4322e-14 - 3.6733e-13] (18#)}OK
 
  {
    "result": "OK",
    "performance": {
      "execution_time": "0.160",
      "gc_time": "0.093"
    },
    "created_on": 1586739220296,
    "file_name": "derivativeTest",
    "report": {
      "simpleName": "Basic",
      "canonicalName": "com.simiacryptus.mindseye.layers.java.LinearActivationLayerTest.Basic",
      "link": "https://github.com/SimiaCryptus/mindseye-java/tree/93db34cedee48c0202777a2b25deddf1dfaf5731/src/test/java/com/simiacryptus/mindseye/layers/java/LinearActivationLayerTest.java",
      "javaDoc": ""
    },
    "archive": "s3://code.simiacrypt.us/tests/com/simiacryptus/mindseye/layers/java/LinearActivationLayer/Basic/derivativeTest/202004135340",
    "id": "cb39fbb9-fcd1-4062-be42-c51fe70ea4c2",
    "report_type": "Components",
    "display_name": "Derivative Validation",
    "target": {
      "simpleName": "LinearActivationLayer",
      "canonicalName": "com.simiacryptus.mindseye.layers.java.LinearActivationLayer",
      "link": "https://github.com/SimiaCryptus/mindseye-java/tree/93db34cedee48c0202777a2b25deddf1dfaf5731/src/main/java/com/simiacryptus/mindseye/layers/java/LinearActivationLayer.java",
      "javaDoc": ""
    }
  }