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 9016466520870906880

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 ], [ 0.048 ], [ -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: [
[ [ 0.08, 0.7, -0.128, 0.496, -0.608, 1.764, 0.048, 1.524, ... ] ]
]
Outputs 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}

Feedback Validation

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

SingleDerivativeTester.java:117 executed in 0.08 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 ], [ 0.048 ], [ -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: [ [ 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=0.027777777777777776, count=1296, sum=36.0, positive=36, stdDev=0.16433554953054488, zeros=1260}
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=-3.987761942938778E-14, negative=0, min=0.0, max=1.0000000000000286, mean=0.027777777777775223, count=1296, sum=35.99999999999669, positive=36, stdDev=0.1643355495305298, zeros=1260}
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=-13.058180480733906, negative=32, min=-1.1013412404281553E-13, max=2.864375403532904E-14, mean=-2.550600334505356E-15, count=1296, sum=-3.305578033518941E-12, positive=4, stdDev=1.691714276507881E-14, zeros=1260}

Returns

    {
      "absoluteTol" : {
        "count" : 1296,
        "sum" : 3.5347280658015734E-12,
        "min" : 0.0,
        "max" : 1.1013412404281553E-13,
        "sumOfSquare" : 3.7933308469496645E-25,
        "standardDeviation" : 1.688953807200673E-14,
        "average" : 2.7274136310197325E-15
      },
      "relativeTol" : {
        "count" : 36,
        "sum" : 1.76736403290088E-12,
        "min" : 2.9976021664879317E-15,
        "max" : 5.50670620214108E-14,
        "sumOfSquare" : 9.483327117375196E-26,
        "standardDeviation" : 1.496967452719532E-14,
        "average" : 4.9093445358357775E-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" : 1296,
        "sum" : 3.5347280658015734E-12,
        "min" : 0.0,
        "max" : 1.1013412404281553E-13,
        "sumOfSquare" : 3.7933308469496645E-25,
        "standardDeviation" : 1.688953807200673E-14,
        "average" : 2.7274136310197325E-15
      },
      "relativeTol" : {
        "count" : 36,
        "sum" : 1.76736403290088E-12,
        "min" : 2.9976021664879317E-15,
        "max" : 5.50670620214108E-14,
        "sumOfSquare" : 9.483327117375196E-26,
        "standardDeviation" : 1.496967452719532E-14,
        "average" : 4.9093445358357775E-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: 2.7274e-15 +- 1.6890e-14 [0.0000e+00 - 1.1013e-13] (1296#)
relativeTol: 4.9093e-14 +- 1.4970e-14 [2.9976e-15 - 5.5067e-14] (36#)

Frozen and Alive Status

SingleDerivativeTester.java:156 executed in 0.00 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=2.7274e-15 +- 1.6890e-14 [0.0000e+00 - 1.1013e-13] (1296#), relativeTol=4.9093e-14 +- 1.4970e-14 [2.9976e-15 - 5.5067e-14] (36#)}OK
  {
    "result": "OK",
    "performance": {
      "execution_time": "0.248",
      "gc_time": "0.136"
    },
    "created_on": 1586735833649,
    "file_name": "derivativeTest",
    "report": {
      "simpleName": "Basic",
      "canonicalName": "com.simiacryptus.mindseye.layers.java.ReshapeLayerTest.Basic",
      "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/Basic/derivativeTest/202004125713",
    "id": "04b8b82d-4053-46f7-8850-1b23f9b77c85",
    "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": ""
    }
  }