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 4806313770687352832

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, -0.608 ], [ -0.128, 0.048 ] ],
[ [ 0.7, 1.764 ], [ 0.496, 1.524 ] ]
],
[
[ [ 1.208, -0.852, 1.108 ], [ -1.028, -1.688, -0.712 ] ],
[ [ -1.72, 1.912, 0.028 ], [ -0.384, -0.804, 1.048 ] ]
],
[
[ [ -1.616, 1.032, 1.556, 1.612 ], [ 1.512, 0.636, -0.768, 0.392 ] ],
[ [ 1.356, 0.3, 0.788, 1.64 ], [ 1.556, -0.176, -0.068, 0.092 ] ]
]
Inputs Statistics: {meanExponent=-0.44431149530100367, negative=2, min=-0.608, max=1.764, mean=0.4845, count=8, sum=3.876, positive=6, stdDev=0.7654343538148781, zeros=0},
{meanExponent=-0.11474876983470726, negative=7, min=-1.72, max=1.912, mean=-0.15699999999999995, count=12, sum=-1.8839999999999995, positive=5, stdDev=1.156042819276172, zeros=0},
{meanExponent=-0.18514238199839722, negative=4, min=-1.616, max=1.64, mean=0.6152500000000001, count=16, sum=9.844000000000001, positive=12, stdDev=0.9279765285286045, zeros=0}
Output: [
[ [ 0.08, -0.608, 1.208, -0.852, 1.108, -1.616, 1.032, 1.556 ], [ -0.128, 0.048, -1.028, -1.688, -0.712, 1.512, 0.636, -0.768 ] ],
[ [ 0.7, 1.764, -1.72, 1.912, 0.028, 1.356, 0.3, 0.788 ], [ 0.496, 1.524, -0.384, -0.804, 1.048, 1.556, -0.176, -0.068 ] ]
]
Outputs Statistics: {meanExponent=-0.21478244038380173, negative=13, min=-1.72, max=1.912, mean=0.253125, count=32, sum=8.1, positive=19, stdDev=1.0492889184466785, zeros=0}

Feedback Validation

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

SingleDerivativeTester.java:117 executed in 1.17 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, -0.608 ], [ -0.128, 0.048 ] ],
[ [ 0.7, 1.764 ], [ 0.496, 1.524 ] ]
]
Value Statistics: {meanExponent=-0.44431149530100367, negative=2, min=-0.608, max=1.764, mean=0.4845, count=8, sum=3.876, positive=6, stdDev=0.7654343538148781, 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=0.03125, count=256, sum=8.0, positive=8, stdDev=0.17399263633843817, zeros=248}
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=-3.276302567614995E-14, negative=0, min=0.0, max=1.0000000000000286, mean=0.03124999999999764, count=256, sum=7.999999999999396, positive=8, stdDev=0.17399263633842504, zeros=248}
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=-13.104301089584562, negative=6, min=-1.1013412404281553E-13, max=2.864375403532904E-14, mean=-2.357489203852481E-15, count=256, sum=-6.035172361862351E-13, positive=2, stdDev=1.6886025980486074E-14, zeros=248}
Feedback for input 1
Inputs Values: [
[ [ 1.208, -0.852, 1.108 ], [ -1.028, -1.688, -0.712 ] ],
[ [ -1.72, 1.912, 0.028 ], [ -0.384, -0.804, 1.048 ] ]
]
Value Statistics: {meanExponent=-0.11474876983470726, negative=7, min=-1.72, max=1.912, mean=-0.15699999999999995, count=12, sum=-1.8839999999999995, positive=5, stdDev=1.156042819276172, zeros=0}
Implemented Feedback: [ [ 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, 0.0, 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, 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, 0.0, ... ], [ 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ... ], ... ]
Implemented Statistics: {meanExponent=0.0, negative=0, min=0.0, max=1.0, mean=0.03125, count=384, sum=12.0, positive=12, stdDev=0.17399263633843817, zeros=372}
Measured Feedback: [ [ 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, 0.0, 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, 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, 0.0, ... ], [ 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ... ], ... ]
Measured Statistics: {meanExponent=-4.4061729159266355E-14, negative=0, min=0.0, max=0.999999999999994, mean=0.031249999999996832, count=384, sum=11.999999999998783, positive=12, stdDev=0.17399263633842052, zeros=372}
Feedback Error: [ [ 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, 0.0, 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, 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, 0.0, ... ], [ 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ... ], ... ]
Error Statistics: {meanExponent=-13.063421257397756, negative=12, min=-1.1013412404281553E-13, max=0.0, mean=-3.1704962729269446E-15, count=384, sum=-1.2174705688039467E-12, positive=0, stdDev=1.8371225139443615E-14, zeros=372}
Feedback for input 2
Inputs Values: [
[ [ -1.616, 1.032, 1.556, 1.612 ], [ 1.512, 0.636, -0.768, 0.392 ] ],
[ [ 1.356, 0.3, 0.788, 1.64 ], [ 1.556, -0.176, -0.068, 0.092 ] ]
]
Value Statistics: {meanExponent=-0.18514238199839722, negative=4, min=-1.616, max=1.64, mean=0.6152500000000001, count=16, sum=9.844000000000001, positive=12, stdDev=0.9279765285286045, zeros=0}
Implemented Feedback: [ [ 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, 0.0, 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, 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, 0.0, ... ], [ 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ... ], ... ]
Implemented Statistics: {meanExponent=0.0, negative=0, min=0.0, max=1.0, mean=0.0234375, count=512, sum=12.0, positive=12, stdDev=0.15128841196122722, zeros=500}
Measured Feedback: [ [ 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, 0.0, 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, 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, 0.0, ... ], [ 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ... ], ... ]
Measured Statistics: {meanExponent=-4.2808103452747087E-14, negative=0, min=0.0, max=1.0000000000000286, mean=0.02343749999999769, count=512, sum=11.999999999998817, positive=12, stdDev=0.15128841196121232, zeros=500}
Feedback Error: [ [ 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, 0.0, 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, 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, 0.0, ... ], [ 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ... ], ... ]
Error Statistics: {meanExponent=-13.006819095219404, negative=11, min=-1.1013412404281553E-13, max=2.864375403532904E-14, mean=-2.310217989132113E-15, count=512, sum=-1.1828316104356418E-12, positive=1, stdDev=1.602687219990293E-14, zeros=500}

Returns

    {
      "absoluteTol" : {
        "count" : 1152,
        "sum" : 3.1756819396377978E-12,
        "min" : 0.0,
        "max" : 1.1013412404281553E-13,
        "sumOfSquare" : 3.421240442136952E-25,
        "standardDeviation" : 1.70112744047836E-14,
        "average" : 2.7566683503800327E-15
      },
      "relativeTol" : {
        "count" : 32,
        "sum" : 1.5878409698189833E-12,
        "min" : 2.9976021664879317E-15,
        "max" : 5.50670620214108E-14,
        "sumOfSquare" : 8.553101105343315E-26,
        "standardDeviation" : 1.4515394854007434E-14,
        "average" : 4.962003030684323E-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" : 1152,
        "sum" : 3.1756819396377978E-12,
        "min" : 0.0,
        "max" : 1.1013412404281553E-13,
        "sumOfSquare" : 3.421240442136952E-25,
        "standardDeviation" : 1.70112744047836E-14,
        "average" : 2.7566683503800327E-15
      },
      "relativeTol" : {
        "count" : 32,
        "sum" : 1.5878409698189833E-12,
        "min" : 2.9976021664879317E-15,
        "max" : 5.50670620214108E-14,
        "sumOfSquare" : 8.553101105343315E-26,
        "standardDeviation" : 1.4515394854007434E-14,
        "average" : 4.962003030684323E-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: 2.7567e-15 +- 1.7011e-14 [0.0000e+00 - 1.1013e-13] (1152#)
relativeTol: 4.9620e-14 +- 1.4515e-14 [2.9976e-15 - 5.5067e-14] (32#)

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=2.7567e-15 +- 1.7011e-14 [0.0000e+00 - 1.1013e-13] (1152#), relativeTol=4.9620e-14 +- 1.4515e-14 [2.9976e-15 - 5.5067e-14] (32#)}OK
 
  {
    "result": "OK",
    "performance": {
      "execution_time": "1.478",
      "gc_time": "0.243"
    },
    "created_on": 1586739087788,
    "file_name": "derivativeTest",
    "report": {
      "simpleName": "BandConcatLimitTest",
      "canonicalName": "com.simiacryptus.mindseye.layers.cudnn.ImgConcatLayerTest.BandConcatLimitTest",
      "link": "https://github.com/SimiaCryptus/mindseye-cudnn/tree/59d5b3318556370acb2d83ee6ec123ce0fc6974f/src/test/java/com/simiacryptus/mindseye/layers/cudnn/ImgConcatLayerTest.java",
      "javaDoc": ""
    },
    "archive": "s3://code.simiacrypt.us/tests/com/simiacryptus/mindseye/layers/cudnn/ImgConcatLayer/BandConcatLimitTest/derivativeTest/202004135127",
    "id": "cb994efb-e251-4369-94b3-21ec66a030c0",
    "report_type": "Components",
    "display_name": "Derivative Validation",
    "target": {
      "simpleName": "ImgConcatLayer",
      "canonicalName": "com.simiacryptus.mindseye.layers.cudnn.ImgConcatLayer",
      "link": "https://github.com/SimiaCryptus/mindseye-cudnn/tree/59d5b3318556370acb2d83ee6ec123ce0fc6974f/src/main/java/com/simiacryptus/mindseye/layers/cudnn/ImgConcatLayer.java",
      "javaDoc": ""
    }
  }