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 Img reshape layer.

Report Description: The type Expand.

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

Test Modules

Using Seed 3431835437765327872

Differential Validation

SingleDerivativeTester.java:153 executed in 0.01 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, 1.208, -0.852 ], [ -0.128, 0.048, -1.028, -1.688 ] ],
[ [ 0.7, 1.764, -1.72, 1.912 ], [ 0.496, 1.524, -0.384, -0.804 ] ]
]
Inputs Statistics: {meanExponent=-0.20600101677773192, negative=8, min=-1.72, max=1.912, mean=0.0325, count=16, sum=0.52, positive=8, stdDev=1.1193595266937248, zeros=0}
Output: [
[ [ 0.08 ], [ -0.608 ], [ -0.128 ], [ 0.048 ] ],
[ [ 1.208 ], [ -0.852 ], [ -1.028 ], [ -1.688 ] ],
[ [ 0.7 ], [ 1.764 ], [ 0.496 ], [ 1.524 ] ],
[ [ -1.72 ], [ 1.912 ], [ -0.384 ], [ -0.804 ] ]
]
Outputs Statistics: {meanExponent=-0.20600101677773192, negative=8, min=-1.72, max=1.912, mean=0.0325, count=16, sum=0.52, positive=8, stdDev=1.119359526693725, zeros=0}

Feedback Validation

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

SingleDerivativeTester.java:169 executed in 0.05 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, 1.208, -0.852 ], [ -0.128, 0.048, -1.028, -1.688 ] ],
[ [ 0.7, 1.764, -1.72, 1.912 ], [ 0.496, 1.524, -0.384, -0.804 ] ]
]
Value Statistics: {meanExponent=-0.20600101677773192, negative=8, min=-1.72, max=1.912, mean=0.0325, count=16, sum=0.52, positive=8, stdDev=1.1193595266937248, zeros=0}
Implemented Feedback: [ [ 1.0, 0.0, 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, 0.0, 0.0, 0.0, 0.0, 0.0, ... ], [ 0.0, 0.0, 0.0, 0.0, 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, 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, 0.0, ... ], ... ]
Implemented Statistics: {meanExponent=0.0, negative=0, min=0.0, max=1.0, mean=0.0625, count=256, sum=16.0, positive=16, stdDev=0.24206145913796356, zeros=240}
Measured Feedback: [ [ 1.0000000000000286, 0.0, 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, 0.0, 0.0, 0.0, 0.0, 0.0, ... ], [ 0.0, 0.0, 0.0, 0.0, 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, 0.0, 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, 0.0, ... ], ... ]
Measured Statistics: {meanExponent=-4.02968340085978E-14, negative=0, min=0.0, max=1.0000000000000286, mean=0.0624999999999942, count=256, sum=15.999999999998515, positive=16, stdDev=0.24206145913794108, zeros=240}
Feedback Error: [ [ 2.864375403532904E-14, 0.0, 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, 0.0, 0.0, 0.0, 0.0, 0.0, ... ], [ 0.0, 0.0, 0.0, 0.0, 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, 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, 0.0, ... ], ... ]
Error Statistics: {meanExponent=-13.031189593810693, negative=14, min=-1.1013412404281553E-13, max=2.864375403532904E-14, mean=-5.799180580190466E-15, count=256, sum=-1.4845902285287593E-12, positive=2, stdDev=2.5221276698790836E-14, zeros=240}

Returns

    {
      "absoluteTol" : {
        "count" : 256,
        "sum" : 1.5991652446700755E-12,
        "min" : 0.0,
        "max" : 1.1013412404281553E-13,
        "sumOfSquare" : 1.7145428319196874E-25,
        "standardDeviation" : 2.5114170155982587E-14,
        "average" : 6.246739236992482E-15
      },
      "relativeTol" : {
        "count" : 16,
        "sum" : 7.995826223350798E-13,
        "min" : 1.4321877017664317E-14,
        "max" : 5.50670620214108E-14,
        "sumOfSquare" : 4.286357079799686E-26,
        "standardDeviation" : 1.347520333040395E-14,
        "average" : 4.997391389594249E-14
      }
    }

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" : 256,
        "sum" : 1.5991652446700755E-12,
        "min" : 0.0,
        "max" : 1.1013412404281553E-13,
        "sumOfSquare" : 1.7145428319196874E-25,
        "standardDeviation" : 2.5114170155982587E-14,
        "average" : 6.246739236992482E-15
      },
      "relativeTol" : {
        "count" : 16,
        "sum" : 7.995826223350798E-13,
        "min" : 1.4321877017664317E-14,
        "max" : 5.50670620214108E-14,
        "sumOfSquare" : 4.286357079799686E-26,
        "standardDeviation" : 1.347520333040395E-14,
        "average" : 4.997391389594249E-14
      }
    }

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: 6.2467e-15 +- 2.5114e-14 [0.0000e+00 - 1.1013e-13] (256#)
relativeTol: 4.9974e-14 +- 1.3475e-14 [1.4322e-14 - 5.5067e-14] (16#)

Frozen and Alive Status

SingleDerivativeTester.java:208 executed in 0.00 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=6.2467e-15 +- 2.5114e-14 [0.0000e+00 - 1.1013e-13] (256#), relativeTol=4.9974e-14 +- 1.3475e-14 [1.4322e-14 - 5.5067e-14] (16#)}OK
  {
    "result": "OK",
    "performance": {
      "execution_time": "0.208",
      "gc_time": "0.120"
    },
    "created_on": 1587005162746,
    "file_name": "derivativeTest",
    "report": {
      "simpleName": "Expand",
      "canonicalName": "com.simiacryptus.mindseye.layers.java.ImgReshapeLayerTest.Expand",
      "link": "https://github.com/SimiaCryptus/mindseye-java/tree/c9a1867488dc7e77a975f095285b5882c0486db6/src/test/java/com/simiacryptus/mindseye/layers/java/ImgReshapeLayerTest.java",
      "javaDoc": "The type Expand."
    },
    "archive": "s3://code.simiacrypt.us/tests/com/simiacryptus/mindseye/layers/java/ImgReshapeLayer/Expand/derivativeTest/202004164602",
    "id": "aaa4a1d2-228f-494a-a319-4f3c5c051a7a",
    "report_type": "Components",
    "display_name": "Derivative Validation",
    "target": {
      "simpleName": "ImgReshapeLayer",
      "canonicalName": "com.simiacryptus.mindseye.layers.java.ImgReshapeLayer",
      "link": "https://github.com/SimiaCryptus/mindseye-java/tree/c9a1867488dc7e77a975f095285b5882c0486db6/src/main/java/com/simiacryptus/mindseye/layers/java/ImgReshapeLayer.java",
      "javaDoc": "The type Img reshape layer."
    }
  }