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 6987997859789337600

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)));

Inputs: [
[ [ 0.08 ], [ 0.048 ], [ -0.852 ], [ -0.712 ], [ 1.032 ], [ -0.768 ] ],
[ [ 0.7 ], [ 1.524 ], [ 1.912 ], [ 1.048 ], [ 0.3 ], [ -0.068 ] ],
[ [ -0.128 ], [ 1.208 ], [ -1.688 ], [ -1.616 ], [ 0.636 ], [ 1.612 ] ],
[ [ 0.496 ], [ -1.72 ], [ -0.804 ], [ 1.356 ], [ -0.176 ], [ 1.64 ] ],
[ [ -0.608 ], [ -1.028 ], [ 1.108 ], [ 1.512 ], [ 1.556 ], [ 0.392 ] ],
[ [ 1.764 ], [ -0.384 ], [ 0.028 ], [ 1.556 ], [ 0.788 ], [ 0.092 ] ]
]
Inputs Statistics: {meanExponent=-0.2192709808999687, negative=13, min=-1.72, max=1.912, mean=0.3287777777777778, count=36, sum=11.836, positive=23, stdDev=1.0387145129948714, zeros=0}
Output: [ 0.0 ]
Outputs Statistics: {meanExponent=NaN, negative=0, min=0.0, max=0.0, mean=0.0, count=1, sum=0.0, positive=0, stdDev=0.0, zeros=1}

Feedback Validation

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

SingleDerivativeTester.java:117 executed in 0.11 seconds (0.000 gc):

        return testFeedback(
            statistics,
            component.addRef(),
            RefUtil.addRef(inputPrototype),
            outputPrototype.addRef());
      },
      outputPrototype.addRef(),
      RefUtil.addRef(inputPrototype),
      component.addRef()));

Feedback for input 0
Inputs Values: [
[ [ 0.08 ], [ 0.048 ], [ -0.852 ], [ -0.712 ], [ 1.032 ], [ -0.768 ] ],
[ [ 0.7 ], [ 1.524 ], [ 1.912 ], [ 1.048 ], [ 0.3 ], [ -0.068 ] ],
[ [ -0.128 ], [ 1.208 ], [ -1.688 ], [ -1.616 ], [ 0.636 ], [ 1.612 ] ],
[ [ 0.496 ], [ -1.72 ], [ -0.804 ], [ 1.356 ], [ -0.176 ], [ 1.64 ] ],
[ [ -0.608 ], [ -1.028 ], [ 1.108 ], [ 1.512 ], [ 1.556 ], [ 0.392 ] ],
[ [ 1.764 ], [ -0.384 ], [ 0.028 ], [ 1.556 ], [ 0.788 ], [ 0.092 ] ]
]
Value Statistics: {meanExponent=-0.2192709808999687, negative=13, min=-1.72, max=1.912, mean=0.3287777777777778, count=36, sum=11.836, positive=23, stdDev=1.0387145129948714, zeros=0}
Implemented Feedback: [ [ 0.0 ], [ 0.0 ], [ 0.0 ], [ 0.0 ], [ 0.0 ], [ 0.0 ], [ 0.0 ], [ 0.0 ], ... ]
Implemented Statistics: {meanExponent=NaN, negative=0, min=0.0, max=0.0, mean=0.0, count=36, sum=0.0, positive=0, stdDev=0.0, zeros=36}
Measured Feedback: [ [ 0.0 ], [ 0.0 ], [ 0.0 ], [ 0.0 ], [ 0.0 ], [ 0.0 ], [ 0.0 ], [ 0.0 ], ... ]
Measured Statistics: {meanExponent=NaN, negative=0, min=0.0, max=0.0, mean=0.0, count=36, sum=0.0, positive=0, stdDev=0.0, zeros=36}
Feedback Error: [ [ 0.0 ], [ 0.0 ], [ 0.0 ], [ 0.0 ], [ 0.0 ], [ 0.0 ], [ 0.0 ], [ 0.0 ], ... ]
Error Statistics: {meanExponent=NaN, negative=0, min=0.0, max=0.0, mean=0.0, count=36, sum=0.0, positive=0, stdDev=0.0, zeros=36}

Returns

    {
      "absoluteTol" : {
        "count" : 36,
        "sum" : 0.0,
        "min" : 0.0,
        "max" : 0.0,
        "sumOfSquare" : 0.0,
        "standardDeviation" : 0.0,
        "average" : 0.0
      },
      "relativeTol" : {
        "count" : 0,
        "sum" : 0.0,
        "min" : "Infinity",
        "max" : "-Infinity",
        "sumOfSquare" : 0.0,
        "standardDeviation" : 0.0,
        "average" : 0.0
      }
    }

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" : 36,
        "sum" : 0.0,
        "min" : 0.0,
        "max" : 0.0,
        "sumOfSquare" : 0.0,
        "standardDeviation" : 0.0,
        "average" : 0.0
      },
      "relativeTol" : {
        "count" : 0,
        "sum" : 0.0,
        "min" : "Infinity",
        "max" : "-Infinity",
        "sumOfSquare" : 0.0,
        "standardDeviation" : 0.0,
        "average" : 0.0
      }
    }

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));

Finite-Difference Derivative Accuracy:
absoluteTol: 0.0000e+00 +- 0.0000e+00 [0.0000e+00 - 0.0000e+00] (36#)
relativeTol: 0.0000e+00 +- 0.0000e+00 [Infinity - -Infinity] (0#)

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

  {
    "result": "OK",
    "performance": {
      "execution_time": "0.262",
      "gc_time": "0.104"
    },
    "created_on": 1586735451104,
    "file_name": "derivativeTest",
    "report": {
      "simpleName": "Basic",
      "canonicalName": "com.simiacryptus.mindseye.layers.java.StochasticSamplingSubnetLayerTest.Basic",
      "link": "https://github.com/SimiaCryptus/mindseye-java/tree/93db34cedee48c0202777a2b25deddf1dfaf5731/src/test/java/com/simiacryptus/mindseye/layers/java/StochasticSamplingSubnetLayerTest.java",
      "javaDoc": ""
    },
    "archive": "s3://code.simiacrypt.us/tests/com/simiacryptus/mindseye/layers/java/StochasticSamplingSubnetLayer/Basic/derivativeTest/202004125051",
    "id": "c04d73fc-ff68-4da1-9e3b-5770e4d4585d",
    "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/93db34cedee48c0202777a2b25deddf1dfaf5731/src/main/java/com/simiacryptus/mindseye/layers/java/StochasticSamplingSubnetLayer.java",
      "javaDoc": ""
    }
  }