Subreport: Logs for com.simiacryptus.ref.lang.ReferenceCountingBase
Using Seed 1109530941261942784
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, 0.7, -0.128 ],
[ 0.496, -0.608, 1.764 ]
Inputs Statistics: {meanExponent=-0.7148673344486438, negative=1, min=-0.128, max=0.7, mean=0.2173333333333333, count=3, sum=0.6519999999999999, positive=2, stdDev=0.3517018939701949, zeros=0},
{meanExponent=-0.09137205448042225, negative=1, min=-0.608, max=1.764, mean=0.5506666666666667, count=3, sum=1.6520000000000001, positive=2, stdDev=0.9691361560115735, zeros=0}
Output: [ 0.576, 0.09199999999999997, 1.6360000000000001 ]
Outputs Statistics: {meanExponent=-0.35400212996530955, negative=0, min=0.09199999999999997, max=1.6360000000000001, mean=0.7680000000000001, count=3, sum=2.3040000000000003, positive=3, stdDev=0.6447904052222447, zeros=0}
We validate the agreement between the implemented derivative of the inputs apply finite difference estimations:
SingleDerivativeTester.java:117 executed in 0.02 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, 0.7, -0.128 ]
Value Statistics: {meanExponent=-0.7148673344486438, negative=1, min=-0.128, max=0.7, mean=0.2173333333333333, count=3, sum=0.6519999999999999, positive=2, stdDev=0.3517018939701949, zeros=0}
Implemented Feedback: [ [ 1.0, 0.0, 0.0 ], [ 0.0, 1.0, 0.0 ], [ 0.0, 0.0, 1.0 ] ]
Implemented Statistics: {meanExponent=0.0, negative=0, min=0.0, max=1.0, mean=0.3333333333333333, count=9, sum=3.0, positive=3, stdDev=0.4714045207910317, zeros=6}
Measured Feedback: [ [ 1.000000000001, 0.0, 0.0 ], [ 0.0, 0.9999999999998899, 0.0 ], [ 0.0, 0.0, 0.9999999999976694 ] ]
Measured Statistics: {meanExponent=-2.085518867670573E-13, negative=0, min=0.0, max=1.000000000001, mean=0.3333333333331733, count=9, sum=2.9999999999985594, positive=3, stdDev=0.4714045207908053, zeros=6}
Feedback Error: [ [ 1.000088900582341E-12, 0.0, 0.0 ], [ 0.0, -1.1013412404281553E-13, 0.0 ], [ 0.0, 0.0, -2.3305801732931286E-12 ] ]
Error Statistics: {meanExponent=-12.196858481071999, negative=2, min=-2.3305801732931286E-12, max=1.000088900582341E-12, mean=-1.6006948852817813E-13, count=9, sum=-1.4406253967536031E-12, positive=1, stdDev=8.308838070977093E-13, zeros=6}
Feedback for input 1
Inputs Values: [ 0.496, -0.608, 1.764 ]
Value Statistics: {meanExponent=-0.09137205448042225, negative=1, min=-0.608, max=1.764, mean=0.5506666666666667, count=3, sum=1.6520000000000001, positive=2, stdDev=0.9691361560115735, zeros=0}
Implemented Feedback: [ [ 1.0, 0.0, 0.0 ], [ 0.0, 1.0, 0.0 ], [ 0.0, 0.0, 1.0 ] ]
Implemented Statistics: {meanExponent=0.0, negative=0, min=0.0, max=1.0, mean=0.3333333333333333, count=9, sum=3.0, positive=3, stdDev=0.4714045207910317, zeros=6}
Measured Feedback: [ [ 0.9999999999998899, 0.0, 0.0 ], [ 0.0, 0.9999999999998899, 0.0 ], [ 0.0, 0.0, 0.9999999999976694 ] ]
Measured Statistics: {meanExponent=-3.692731311925336E-13, negative=0, min=0.0, max=0.9999999999998899, mean=0.33333333333304993, count=9, sum=2.999999999997449, positive=3, stdDev=0.47140452079063083, zeros=6}
Feedback Error: [ [ -1.1013412404281553E-13, 0.0, 0.0 ], [ 0.0, -1.1013412404281553E-13, 0.0 ], [ 0.0, 0.0, -2.3305801732931286E-12 ] ]
Error Statistics: {meanExponent=-12.516230716189696, negative=3, min=-2.3305801732931286E-12, max=0.0, mean=-2.834276023754177E-13, count=9, sum=-2.5508484213787597E-12, positive=0, stdDev=7.251729404930389E-13, zeros=6}
Returns
{
"absoluteTol" : {
"count" : 18,
"sum" : 5.991651619297045E-12,
"min" : 0.0,
"max" : 2.3305801732931286E-12,
"sumOfSquare" : 1.1899774273198089E-23,
"standardDeviation" : 7.418196841537471E-13,
"average" : 3.3286953440539137E-13
},
"relativeTol" : {
"count" : 6,
"sum" : 2.9958258096509975E-12,
"min" : 5.50670620214108E-14,
"max" : 1.1652900866479222E-12,
"sumOfSquare" : 2.9749435683056025E-24,
"standardDeviation" : 4.965069409848992E-13,
"average" : 4.993043016084996E-13
}
}
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" : 18,
"sum" : 5.991651619297045E-12,
"min" : 0.0,
"max" : 2.3305801732931286E-12,
"sumOfSquare" : 1.1899774273198089E-23,
"standardDeviation" : 7.418196841537471E-13,
"average" : 3.3286953440539137E-13
},
"relativeTol" : {
"count" : 6,
"sum" : 2.9958258096509975E-12,
"min" : 5.50670620214108E-14,
"max" : 1.1652900866479222E-12,
"sumOfSquare" : 2.9749435683056025E-24,
"standardDeviation" : 4.965069409848992E-13,
"average" : 4.993043016084996E-13
}
}
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: 3.3287e-13 +- 7.4182e-13 [0.0000e+00 - 2.3306e-12] (18#)
relativeTol: 4.9930e-13 +- 4.9651e-13 [5.5067e-14 - 1.1653e-12] (6#)
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());
| class | details | result |
|---|---|---|
| com.simiacryptus.mindseye.test.unit.SingleDerivativeTester | ToleranceStatistics{absoluteTol=3.3287e-13 +- 7.4182e-13 [0.0000e+00 - 2.3306e-12] (18#), relativeTol=4.9930e-13 +- 4.9651e-13 [5.5067e-14 - 1.1653e-12] (6#)} | OK |
{
"result": "OK",
"performance": {
"execution_time": "0.167",
"gc_time": "0.105"
},
"created_on": 1586736364119,
"file_name": "derivativeTest",
"report": {
"simpleName": "NNTest",
"canonicalName": "com.simiacryptus.mindseye.layers.java.SumInputsLayerTest.NNTest",
"link": "https://github.com/SimiaCryptus/mindseye-java/tree/93db34cedee48c0202777a2b25deddf1dfaf5731/src/test/java/com/simiacryptus/mindseye/layers/java/SumInputsLayerTest.java",
"javaDoc": ""
},
"archive": "s3://code.simiacrypt.us/tests/com/simiacryptus/mindseye/layers/java/SumInputsLayer/NNTest/derivativeTest/202004130604",
"id": "0a4f7966-3d1c-4a66-8aba-d74c1233fa5e",
"report_type": "Components",
"display_name": "Derivative Validation",
"target": {
"simpleName": "SumInputsLayer",
"canonicalName": "com.simiacryptus.mindseye.layers.java.SumInputsLayer",
"link": "https://github.com/SimiaCryptus/mindseye-java/tree/93db34cedee48c0202777a2b25deddf1dfaf5731/src/main/java/com/simiacryptus/mindseye/layers/java/SumInputsLayer.java",
"javaDoc": ""
}
}