1. Test Modules
  2. Differential Validation
    1. Feedback Validation
    2. Learning Validation
    3. Total Accuracy
    4. Frozen and Alive Status
  3. Results

Target Description: The type Stochastic sampling subnet layer.

Report Description: The type Basic.

Subreport: Logs for com.simiacryptus.ref.lang.ReferenceCountingBase

Test Modules

Using Seed 1478627569245393920

Differential Validation

SingleDerivativeTester.java:153 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 ]
Inputs Statistics: {meanExponent=-0.5330433495165998, negative=2, min=-0.608, max=0.7, mean=0.10799999999999998, count=5, sum=0.5399999999999999, positive=3, stdDev=0.4626324675160618, zeros=0}
Output: [ 0.0, 1.3999999999999997, 0.0, 0.0, 0.0 ]
Outputs Statistics: {meanExponent=0.14612803567823793, negative=0, min=0.0, max=1.3999999999999997, mean=0.2799999999999999, count=5, sum=1.3999999999999997, positive=1, stdDev=0.5599999999999998, zeros=4}

Feedback Validation

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

SingleDerivativeTester.java:169 executed in 0.10 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 ]
Value Statistics: {meanExponent=-0.5330433495165998, negative=2, min=-0.608, max=0.7, mean=0.10799999999999998, count=5, sum=0.5399999999999999, positive=3, stdDev=0.4626324675160618, zeros=0}
Implemented Feedback: [ [ 0.0, 0.0, 0.0, 0.0, 0.0 ], [ 0.0, 2.0, 0.0, 0.0, 0.0 ], [ 0.0, 0.0, 0.0, 0.0, 0.0 ], [ 0.0, 0.0, 0.0, 0.0, 0.0 ], [ 0.0, 0.0, 0.0, 0.0, 0.0 ] ]
Implemented Statistics: {meanExponent=0.3010299956639812, negative=0, min=0.0, max=2.0, mean=0.08, count=25, sum=2.0, positive=1, stdDev=0.3919183588453085, zeros=24}
Measured Feedback: [ [ 0.0, 0.0, 0.0, 0.0, 0.0 ], [ 0.0, 2.000000000002, 0.0, 0.0, 0.0 ], [ 0.0, 0.0, 0.0, 0.0, 0.0 ], [ 0.0, 0.0, 0.0, 0.0, 0.0 ], [ 0.0, 0.0, 0.0, 0.0, 0.0 ] ]
Measured Statistics: {meanExponent=0.3010299956644155, negative=0, min=0.0, max=2.000000000002, mean=0.08000000000008001, count=25, sum=2.000000000002, positive=1, stdDev=0.3919183588457004, zeros=24}
Feedback Error: [ [ 0.0, 0.0, 0.0, 0.0, 0.0 ], [ 0.0, 2.000177801164682E-12, 0.0, 0.0, 0.0 ], [ 0.0, 0.0, 0.0, 0.0, 0.0 ], [ 0.0, 0.0, 0.0, 0.0, 0.0 ], [ 0.0, 0.0, 0.0, 0.0, 0.0 ] ]
Error Statistics: {meanExponent=-11.69893139701975, negative=0, min=0.0, max=2.000177801164682E-12, mean=8.000711204658728E-14, count=25, sum=2.000177801164682E-12, positive=1, stdDev=3.9195320061563997E-13, zeros=24}

Returns

    {
      "absoluteTol" : {
        "count" : 25,
        "sum" : 2.000177801164682E-12,
        "min" : 0.0,
        "max" : 2.000177801164682E-12,
        "sumOfSquare" : 4.000711236271982E-24,
        "standardDeviation" : 3.9195320061563997E-13,
        "average" : 8.000711204658728E-14
      },
      "relativeTol" : {
        "count" : 1,
        "sum" : 5.000444502909205E-13,
        "min" : 5.000444502909205E-13,
        "max" : 5.000444502909205E-13,
        "sumOfSquare" : 2.5004445226674887E-25,
        "standardDeviation" : 0.0,
        "average" : 5.000444502909205E-13
      }
    }

Learning Validation

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

SingleDerivativeTester.java:185 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" : 25,
        "sum" : 2.000177801164682E-12,
        "min" : 0.0,
        "max" : 2.000177801164682E-12,
        "sumOfSquare" : 4.000711236271982E-24,
        "standardDeviation" : 3.9195320061563997E-13,
        "average" : 8.000711204658728E-14
      },
      "relativeTol" : {
        "count" : 1,
        "sum" : 5.000444502909205E-13,
        "min" : 5.000444502909205E-13,
        "max" : 5.000444502909205E-13,
        "sumOfSquare" : 2.5004445226674887E-25,
        "standardDeviation" : 0.0,
        "average" : 5.000444502909205E-13
      }
    }

Total Accuracy

The overall agreement accuracy between the implemented derivative and the finite difference estimations:

SingleDerivativeTester.java:200 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: 8.0007e-14 +- 3.9195e-13 [0.0000e+00 - 2.0002e-12] (25#)
relativeTol: 5.0004e-13 +- 0.0000e+00 [5.0004e-13 - 5.0004e-13] (1#)

Frozen and Alive Status

SingleDerivativeTester.java:208 executed in 0.02 seconds (0.000 gc):

    testFrozen(component.addRef(), RefUtil.addRef(inputPrototype));
    testUnFrozen(component.addRef(), RefUtil.addRef(inputPrototype));

LayerTests.java:605 executed in 0.00 seconds (0.000 gc):

    throwException(exceptions.addRef());

Results

classdetailsresult
com.simiacryptus.mindseye.test.unit.SingleDerivativeTesterToleranceStatistics{absoluteTol=8.0007e-14 +- 3.9195e-13 [0.0000e+00 - 2.0002e-12] (25#), relativeTol=5.0004e-13 +- 0.0000e+00 [5.0004e-13 - 5.0004e-13] (1#)}OK
  {
    "result": "OK",
    "performance": {
      "execution_time": "0.290",
      "gc_time": "0.110"
    },
    "created_on": 1587006202027,
    "file_name": "derivativeTest",
    "report": {
      "simpleName": "Basic",
      "canonicalName": "com.simiacryptus.mindseye.layers.java.BinaryNoiseLayerTest.Basic",
      "link": "https://github.com/SimiaCryptus/mindseye-java/tree/c9a1867488dc7e77a975f095285b5882c0486db6/src/test/java/com/simiacryptus/mindseye/layers/java/BinaryNoiseLayerTest.java",
      "javaDoc": "The type Basic."
    },
    "archive": "s3://code.simiacrypt.us/tests/com/simiacryptus/mindseye/layers/java/StochasticSamplingSubnetLayer/Basic/derivativeTest/202004160322",
    "id": "3645d28c-15b5-4eee-beb6-d92bb413a223",
    "report_type": "Components",
    "display_name": "Derivative Validation",
    "target": {
      "simpleName": "StochasticSamplingSubnetLayer",
      "canonicalName": "com.simiacryptus.mindseye.layers.java.StochasticSamplingSubnetLayer",
      "link": "https://github.com/SimiaCryptus/mindseye-java/tree/c9a1867488dc7e77a975f095285b5882c0486db6/src/main/java/com/simiacryptus/mindseye/layers/java/StochasticSamplingSubnetLayer.java",
      "javaDoc": "The type Stochastic sampling subnet layer."
    }
  }