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 6738153558346440704

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 ], [ -0.128 ] ],
[ [ 0.7 ], [ 0.496 ] ]
]
Inputs Statistics: {meanExponent=-0.6122800817139336, negative=1, min=-0.128, max=0.7, mean=0.287, count=4, sum=1.148, positive=3, stdDev=0.3276141022605712, zeros=0}
Output: [
[ [ 0.7 ], [ 0.7 ], [ 0.496 ], [ 0.496 ] ],
[ [ 0.08 ], [ 0.08 ], [ -0.128 ], [ -0.128 ] ],
[ [ 0.08 ], [ 0.08 ], [ -0.128 ], [ -0.128 ] ],
[ [ 0.7 ], [ 0.7 ], [ 0.496 ], [ 0.496 ] ]
]
Outputs Statistics: {meanExponent=-0.6122800817139333, negative=4, min=-0.128, max=0.7, mean=0.28700000000000003, count=16, sum=4.5920000000000005, positive=12, stdDev=0.32761410226057114, zeros=0}

Feedback Validation

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

SingleDerivativeTester.java:117 executed in 0.19 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 ], [ -0.128 ] ],
[ [ 0.7 ], [ 0.496 ] ]
]
Value Statistics: {meanExponent=-0.6122800817139336, negative=1, min=-0.128, max=0.7, mean=0.287, count=4, sum=1.148, positive=3, stdDev=0.3276141022605712, zeros=0}
Implemented Feedback: [ [ 0.0, 1.0, 1.0, 0.0, 0.0, 1.0, 1.0, 0.0, ... ], [ 1.0, 0.0, 0.0, 1.0, 1.0, 0.0, 0.0, 1.0, ... ], [ 0.0, 0.0, 0.0, 0.0, 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=0.25, count=64, sum=16.0, positive=16, stdDev=0.4330127018922193, zeros=48}
Measured Feedback: [ [ 0.0, 1.0000000000000286, 1.0000000000000286, 0.0, 0.0, 1.0000000000000286, 1.0000000000000286, 0.0, ... ], [ 0.9999999999998899, 0.0, 0.0, 0.9999999999998899, 0.9999999999998899, 0.0, 0.0, 0.9999999999998899, ... ], [ 0.0, 0.0, 0.0, 0.0, 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=-3.2763025676149944E-14, negative=0, min=0.0, max=1.0000000000000286, mean=0.24999999999998113, count=64, sum=15.999999999998792, positive=16, stdDev=0.43301270189218666, zeros=48}
Feedback Error: [ [ 0.0, 2.864375403532904E-14, 2.864375403532904E-14, 0.0, 0.0, 2.864375403532904E-14, 2.864375403532904E-14, 0.0, ... ], [ -1.1013412404281553E-13, 0.0, 0.0, -1.1013412404281553E-13, -1.1013412404281553E-13, 0.0, 0.0, -1.1013412404281553E-13, ... ], [ 0.0, 0.0, 0.0, 0.0, 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=-13.10430108958456, negative=12, min=-1.1013412404281553E-13, max=2.864375403532904E-14, mean=-1.8859913630819847E-14, count=64, sum=-1.2070344723724702E-12, positive=4, stdDev=4.4383202767685317E-14, zeros=48}

Returns

    {
      "absoluteTol" : {
        "count" : 64,
        "sum" : 1.4361845046551025E-12,
        "min" : 0.0,
        "max" : 1.1013412404281553E-13,
        "sumOfSquare" : 1.4883616192508504E-25,
        "standardDeviation" : 4.26848245403851E-14,
        "average" : 2.2440382885235977E-14
      },
      "relativeTol" : {
        "count" : 16,
        "sum" : 7.180922523275869E-13,
        "min" : 1.4321877017664317E-14,
        "max" : 5.50670620214108E-14,
        "sumOfSquare" : 3.720904048127525E-26,
        "standardDeviation" : 1.7643182647570603E-14,
        "average" : 4.488076577047418E-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" : 64,
        "sum" : 1.4361845046551025E-12,
        "min" : 0.0,
        "max" : 1.1013412404281553E-13,
        "sumOfSquare" : 1.4883616192508504E-25,
        "standardDeviation" : 4.26848245403851E-14,
        "average" : 2.2440382885235977E-14
      },
      "relativeTol" : {
        "count" : 16,
        "sum" : 7.180922523275869E-13,
        "min" : 1.4321877017664317E-14,
        "max" : 5.50670620214108E-14,
        "sumOfSquare" : 3.720904048127525E-26,
        "standardDeviation" : 1.7643182647570603E-14,
        "average" : 4.488076577047418E-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: 2.2440e-14 +- 4.2685e-14 [0.0000e+00 - 1.1013e-13] (64#)
relativeTol: 4.4881e-14 +- 1.7643e-14 [1.4322e-14 - 5.5067e-14] (16#)

Frozen and Alive Status

SingleDerivativeTester.java:156 executed in 0.02 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=2.2440e-14 +- 4.2685e-14 [0.0000e+00 - 1.1013e-13] (64#), relativeTol=4.4881e-14 +- 1.7643e-14 [1.4322e-14 - 5.5067e-14] (16#)}OK
  {
    "result": "OK",
    "performance": {
      "execution_time": "0.392",
      "gc_time": "0.133"
    },
    "created_on": 1586739021646,
    "file_name": "derivativeTest",
    "report": {
      "simpleName": "Right",
      "canonicalName": "com.simiacryptus.mindseye.layers.cudnn.ImgPaddingLayerTest.Right",
      "link": "https://github.com/SimiaCryptus/mindseye-cudnn/tree/59d5b3318556370acb2d83ee6ec123ce0fc6974f/src/test/java/com/simiacryptus/mindseye/layers/cudnn/ImgPaddingLayerTest.java",
      "javaDoc": ""
    },
    "archive": "s3://code.simiacrypt.us/tests/com/simiacryptus/mindseye/layers/cudnn/ImgPaddingLayer/Right/derivativeTest/202004135021",
    "id": "717ce93e-d4d8-4fa3-9b49-bc769fd33a01",
    "report_type": "Components",
    "display_name": "Derivative Validation",
    "target": {
      "simpleName": "ImgPaddingLayer",
      "canonicalName": "com.simiacryptus.mindseye.layers.cudnn.ImgPaddingLayer",
      "link": "https://github.com/SimiaCryptus/mindseye-cudnn/tree/59d5b3318556370acb2d83ee6ec123ce0fc6974f/src/main/java/com/simiacryptus/mindseye/layers/cudnn/ImgPaddingLayer.java",
      "javaDoc": ""
    }
  }