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 2351499022376689664

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 ]
Inputs Statistics: {meanExponent=-0.7148673344486438, negative=1, min=-0.128, max=0.7, mean=0.2173333333333333, count=3, sum=0.6519999999999999, positive=2, stdDev=0.3517018939701949, zeros=0}
Output: [ 0.08, 0.7, -0.128 ]
Outputs Statistics: {meanExponent=-0.7148673344486438, negative=1, min=-0.128, max=0.7, mean=0.2173333333333333, count=3, sum=0.6519999999999999, positive=2, stdDev=0.3517018939701949, 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 
Input 0 for key => x + 0.0e+00!: 
[3]
Output for key => x + 0.0e+00!: 
[3]
Input 0 for key => x + 0.0e+00!: 
[3]
Output for key => x + 0.0e+00!: 
[3]
Input 0 for key => x + 0.0e+00!: 
[3]
Output for key => x + 0.0e+00!: 
[3]
Input 0 for key => x + 0.0e+00!: 
[3]
Output for key => x + 0.0e+00!: 
[3]
Input 0 for key => x + 0.0e+00!: 
[3]
Output for key => x + 0.0e+00!: 
[3]
Feedback Input for key => x + 0.0e+00!: 
[3]
Feedback Output 0 for key => x + 0.0e+00!: 
[3]
Input 0 for key => x + 0.0e+00!: 
[3]
Output for key => x + 0.0e+00!: 
[3]
Feedback Input for key => x + 0.0e+00!: 
[3]
Feedback Output 0 for key => x + 0.0e+00!: 
[3]
Input 0 for key => x + 0.0e+00!: 
[3]
Output for key => x + 0.0e+00!: 
[3]
Feedback Input for key => x + 0.0e+00!: 
[3]
Feedback Output 0 for key => x + 0.0e+00!: 
[3]
Feedback for input 0
Inputs Values: [ 0.08, 0.7, -0.128 ]
Value Statistics: {meanExponent=-0.7148673344486438, negative=1, min=-0.128, max=0.7, mean=0.2173333333333333, count=3, sum=0.6519999999999999, positive=2, stdDev=0.3517018939701949, zeros=0}
Implemented Feedback: [ [ 1.0, 0.0, 0.0 ], [ 0.0, 1.0, 0.0 ], [ 0.0, 0.0, 1.0 ] ]
Implemented Statistics: {meanExponent=0.0, negative=0, min=0.0, max=1.0, mean=0.3333333333333333, count=9, sum=3.0, positive=3, stdDev=0.4714045207910317, zeros=6}
Measured Feedback: [ [ 1.0000000000000286, 0.0, 0.0 ], [ 0.0, 0.9999999999998899, 0.0 ], [ 0.0, 0.0, 0.9999999999998899 ] ]
Measured Statistics: {meanExponent=-2.7740486787851373E-14, negative=0, min=0.0, max=1.0000000000000286, mean=0.333333333333312, count=9, sum=2.999999999999808, positive=3, stdDev=0.4714045207910016, zeros=6}
Feedback Error: [ [ 2.864375403532904E-14, 0.0, 0.0 ], [ 0.0, -1.1013412404281553E-13, 0.0 ], [ 0.0, 0.0, -1.1013412404281553E-13 ] ]
Error Statistics: {meanExponent=-13.153042086767142, negative=2, min=-1.1013412404281553E-13, max=2.864375403532904E-14, mean=-2.1291610450033557E-14, count=9, sum=-1.9162449405030202E-13, positive=1, stdDev=4.8304038389477654E-14, zeros=6}

Returns

    {
      "absoluteTol" : {
        "count" : 9,
        "sum" : 2.489120021209601E-13,
        "min" : 0.0,
        "max" : 1.1013412404281553E-13,
        "sumOfSquare" : 2.5079515202592984E-26,
        "standardDeviation" : 4.4963421625143194E-14,
        "average" : 2.7656889124551122E-14
      },
      "relativeTol" : {
        "count" : 3,
        "sum" : 1.244560010604859E-13,
        "min" : 1.4321877017664317E-14,
        "max" : 5.50670620214108E-14,
        "sumOfSquare" : 6.269878800648909E-27,
        "standardDeviation" : 1.920746441123305E-14,
        "average" : 4.1485333686828635E-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 
Input 0 for key => x + 0.0e+00!: 
[3]
Output for key => x + 0.0e+00!: 
[3]
Input 0 for key => 1.0e+00 x!: 
[3]
Output for key => 1.0e+00 x!: 
[3]
Input 0 for key => x + 1.0e-04!: 
[3]
Output for key => x + 1.0e-04!: 
[3]
Input 0 for key => x + 0.0e+00!: 
[3]
Output for key => x + 0.0e+00!: 
[3]
Feedback Input for key => x + 0.0e+00!: 
[3]
Input 0 for key => x + 0.0e+00!: 
[3]
Output for key => x + 0.0e+00!: 
[3]
Feedback Input for key => x + 0.0e+00!: 
[3]
Input 0 for key => x + 0.0e+00!: 
[3]
Output for key => x + 0.0e+00!: 
[3]
Feedback Input for key => x + 0.0e+00!: 
[3]
Learning Gradient for weight setByCoord 0
Weights: [ 1.0, 0.0 ]
Implemented Gradient: [ [ 0.08, 0.7, -0.128 ], [ 1.0, 1.0, 1.0 ] ]
Implemented Statistics: {meanExponent=-0.3574336672243219, negative=1, min=-0.128, max=1.0, mean=0.6086666666666667, count=6, sum=3.652, positive=5, stdDev=0.4636689432007376, zeros=0}
Measured Gradient: [ [ 0.07999999999994123, 0.700000000000145, -0.1280000000000725 ], [ 1.0000000000000286, 0.9999999999998899, 0.9999999999998899 ] ]
Measured Statistics: {meanExponent=-0.35743366722433295, negative=1, min=-0.1280000000000725, max=1.0000000000000286, mean=0.608666666666637, count=6, sum=3.651999999999822, positive=5, stdDev=0.46366894320074575, zeros=0}
Gradient Error: [ [ -5.877243136609422E-14, 1.4499512701604544E-13, -7.249756350802272E-14 ], [ 2.864375403532904E-14, -1.1013412404281553E-13, -1.1013412404281553E-13 ] ]
Error Statistics: {meanExponent=-13.111379297497587, negative=4, min=-1.1013412404281553E-13, max=1.4499512701604544E-13, mean=-2.964989365139559E-14, count=6, sum=-1.7789936190837352E-13, positive=2, stdDev=9.08648266226998E-14, zeros=0}

Returns

    {
      "absoluteTol" : {
        "count" : 15,
        "sum" : 7.740891261320826E-13,
        "min" : 0.0,
        "max" : 1.4499512701604544E-13,
        "sumOfSquare" : 7.989271266686716E-26,
        "standardDeviation" : 5.160433723404248E-14,
        "average" : 5.160594174213884E-14
      },
      "relativeTol" : {
        "count" : 9,
        "sum" : 1.0030012534809224E-12,
        "min" : 1.4321877017664317E-14,
        "max" : 3.673276960382238E-13,
        "sumOfSquare" : 2.383943330060787E-25,
        "standardDeviation" : 1.1861013439985092E-13,
        "average" : 1.1144458372010249E-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: 5.1606e-14 +- 5.1604e-14 [0.0000e+00 - 1.4500e-13] (15#)
relativeTol: 1.1144e-13 +- 1.1861e-13 [1.4322e-14 - 3.6733e-13] (9#)

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));
Logging 
Input 0 for key == x + 0.0e+00: 
[3]
Output for key == x + 0.0e+00: 
[3]
Feedback Input for key == x + 0.0e+00: 
[3]
Feedback Output 0 for key == x + 0.0e+00: 
[3]
Input 0 for key => x + 0.0e+00!: 
[3]
Output for key => x + 0.0e+00!: 
[3]
Feedback Input for key => x + 0.0e+00!: 
[3]
Feedback Output 0 for key => x + 0.0e+00!: 
[3]

LayerTests.java:425 executed in 0.00 seconds (0.000 gc):

    throwException(exceptions.addRef());

Results

classdetailsresult
com.simiacryptus.mindseye.test.unit.SingleDerivativeTesterToleranceStatistics{absoluteTol=5.1606e-14 +- 5.1604e-14 [0.0000e+00 - 1.4500e-13] (15#), relativeTol=1.1144e-13 +- 1.1861e-13 [1.4322e-14 - 3.6733e-13] (9#)}OK
 
  {
    "result": "OK",
    "performance": {
      "execution_time": "0.184",
      "gc_time": "0.102"
    },
    "created_on": 1586737158342,
    "file_name": "derivativeTest",
    "report": {
      "simpleName": "Basic",
      "canonicalName": "com.simiacryptus.mindseye.layers.java.LoggingWrapperLayerTest.Basic",
      "link": "https://github.com/SimiaCryptus/mindseye-java/tree/93db34cedee48c0202777a2b25deddf1dfaf5731/src/test/java/com/simiacryptus/mindseye/layers/java/LoggingWrapperLayerTest.java",
      "javaDoc": ""
    },
    "archive": "s3://code.simiacrypt.us/tests/com/simiacryptus/mindseye/layers/LoggingWrapperLayer/Basic/derivativeTest/202004131918",
    "id": "708779da-a33e-447b-a8f8-eb748600c225",
    "report_type": "Components",
    "display_name": "Derivative Validation",
    "target": {
      "simpleName": "LoggingWrapperLayer",
      "canonicalName": "com.simiacryptus.mindseye.layers.LoggingWrapperLayer",
      "link": "https://github.com/SimiaCryptus/mindseye-core/tree/a09d39d97a7eff18f17aef61a4891b6f93a18cbe/src/main/java/com/simiacryptus/mindseye/layers/LoggingWrapperLayer.java",
      "javaDoc": ""
    }
  }