Subreport: Logs for com.simiacryptus.ref.lang.ReferenceCountingBase
Using Seed 6173538510172223488
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: [ -0.608, 1.764, 0.048, 1.524, 1.208 ]
Inputs Statistics: {meanExponent=-0.20466094025343645, negative=1, min=-0.608, max=1.764, mean=0.7872, count=5, sum=3.936, positive=4, stdDev=0.9129167322379408, zeros=0}
Output: [ -0.11199999999999999, 2.26, 0.544, 2.02, 1.704 ]
Outputs Statistics: {meanExponent=-0.06485073572138651, negative=1, min=-0.11199999999999999, max=2.26, mean=1.2832, count=5, sum=6.4159999999999995, positive=4, stdDev=0.9129167322379407, 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: [ -0.608, 1.764, 0.048, 1.524, 1.208 ]
Value Statistics: {meanExponent=-0.20466094025343645, negative=1, min=-0.608, max=1.764, mean=0.7872, count=5, sum=3.936, positive=4, stdDev=0.9129167322379408, zeros=0}
Implemented Feedback: [ [ 1.0, 0.0, 0.0, 0.0, 0.0 ], [ 0.0, 1.0, 0.0, 0.0, 0.0 ], [ 0.0, 0.0, 1.0, 0.0, 0.0 ], [ 0.0, 0.0, 0.0, 1.0, 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=0.2, count=25, sum=5.0, positive=5, stdDev=0.4, zeros=20}
Measured Feedback: [ [ 0.9999999999998899, 0.0, 0.0, 0.0, 0.0 ], [ 0.0, 1.0000000000021103, 0.0, 0.0, 0.0 ], [ 0.0, 0.0, 0.9999999999998899, 0.0, 0.0 ], [ 0.0, 0.0, 0.0, 1.0000000000021103, 0.0 ], [ 0.0, 0.0, 0.0, 0.0, 0.9999999999998899 ] ]
Measured Statistics: {meanExponent=3.379003442798834E-13, negative=0, min=0.0, max=1.0000000000021103, mean=0.2000000000001556, count=25, sum=5.00000000000389, positive=5, stdDev=0.4000000000003112, zeros=20}
Feedback Error: [ [ -1.1013412404281553E-13, 0.0, 0.0, 0.0, 0.0 ], [ 0.0, 2.1103119252074976E-12, 0.0, 0.0, 0.0 ], [ 0.0, 0.0, -1.1013412404281553E-13, 0.0, 0.0 ], [ 0.0, 0.0, 0.0, 2.1103119252074976E-12, 0.0 ], [ 0.0, 0.0, 0.0, 0.0, -1.1013412404281553E-13 ] ]
Error Statistics: {meanExponent=-12.445108197578056, negative=3, min=-1.1013412404281553E-13, max=2.1103119252074976E-12, mean=1.5560885913146193E-13, count=25, sum=3.8902214782865485E-12, positive=2, stdDev=5.775073503662826E-13, zeros=20}
Returns
{
"absoluteTol" : {
"count" : 25,
"sum" : 4.551026222543442E-12,
"min" : 0.0,
"max" : 2.1103119252074976E-12,
"sumOfSquare" : 8.943221419181984E-24,
"standardDeviation" : 5.697279291750006E-13,
"average" : 1.8204104890173767E-13
},
"relativeTol" : {
"count" : 5,
"sum" : 2.2755131112695035E-12,
"min" : 5.50670620214108E-14,
"max" : 1.0551559626026354E-12,
"sumOfSquare" : 2.235805354790798E-24,
"standardDeviation" : 4.899415007690031E-13,
"average" : 4.551026222539007E-13
}
}
We validate the agreement between the implemented derivative of the internal weights apply finite difference estimations:
SingleDerivativeTester.java:133 executed in 0.01 seconds (0.000 gc):
return testLearning(
statistics,
component.addRef(),
RefUtil.addRef(inputPrototype),
outputPrototype.addRef());
},
outputPrototype.addRef(),
RefUtil.addRef(inputPrototype),
component.addRef()));
Learning Gradient for weight setByCoord 0
Weights: [ 0.496 ]
Implemented Gradient: [ [ 1.0, 1.0, 1.0, 1.0, 1.0 ] ]
Implemented Statistics: {meanExponent=0.0, negative=0, min=1.0, max=1.0, mean=1.0, count=5, sum=5.0, positive=5, stdDev=0.0, zeros=0}
Measured Gradient: [ [ 0.9999999999998899, 1.0000000000021103, 0.9999999999998899, 1.0000000000021103, 0.9999999999998899 ] ]
Measured Statistics: {meanExponent=3.379003442798834E-13, negative=0, min=0.9999999999998899, max=1.0000000000021103, mean=1.000000000000778, count=5, sum=5.00000000000389, positive=5, stdDev=0.0, zeros=0}
Gradient Error: [ [ -1.1013412404281553E-13, 2.1103119252074976E-12, -1.1013412404281553E-13, 2.1103119252074976E-12, -1.1013412404281553E-13 ] ]
Error Statistics: {meanExponent=-12.445108197578056, negative=3, min=-1.1013412404281553E-13, max=2.1103119252074976E-12, mean=7.780442956573097E-13, count=5, sum=3.8902214782865485E-12, positive=2, stdDev=1.0877919644084146E-12, zeros=0}
Returns
{
"absoluteTol" : {
"count" : 30,
"sum" : 9.102052445086883E-12,
"min" : 0.0,
"max" : 2.1103119252074976E-12,
"sumOfSquare" : 1.788644283836397E-23,
"standardDeviation" : 7.100437595574319E-13,
"average" : 3.034017481695628E-13
},
"relativeTol" : {
"count" : 10,
"sum" : 4.551026222539007E-12,
"min" : 5.50670620214108E-14,
"max" : 1.0551559626026354E-12,
"sumOfSquare" : 4.471610709581596E-24,
"standardDeviation" : 4.899415007690031E-13,
"average" : 4.551026222539007E-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: 3.0340e-13 +- 7.1004e-13 [0.0000e+00 - 2.1103e-12] (30#)
relativeTol: 4.5510e-13 +- 4.8994e-13 [5.5067e-14 - 1.0552e-12] (10#)
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=3.0340e-13 +- 7.1004e-13 [0.0000e+00 - 2.1103e-12] (30#), relativeTol=4.5510e-13 +- 4.8994e-13 [5.5067e-14 - 1.0552e-12] (10#)} | OK |
{
"result": "OK",
"performance": {
"execution_time": "0.178",
"gc_time": "0.114"
},
"created_on": 1586735361906,
"file_name": "derivativeTest",
"report": {
"simpleName": "Reducing",
"canonicalName": "com.simiacryptus.mindseye.layers.java.BiasLayerTest.Reducing",
"link": "https://github.com/SimiaCryptus/mindseye-java/tree/93db34cedee48c0202777a2b25deddf1dfaf5731/src/test/java/com/simiacryptus/mindseye/layers/java/BiasLayerTest.java",
"javaDoc": ""
},
"archive": "s3://code.simiacrypt.us/tests/com/simiacryptus/mindseye/layers/java/BiasLayer/Reducing/derivativeTest/202004124921",
"id": "4a94f91e-948b-4f54-a4c0-08243a6a207e",
"report_type": "Components",
"display_name": "Derivative Validation",
"target": {
"simpleName": "BiasLayer",
"canonicalName": "com.simiacryptus.mindseye.layers.java.BiasLayer",
"link": "https://github.com/SimiaCryptus/mindseye-java/tree/93db34cedee48c0202777a2b25deddf1dfaf5731/src/main/java/com/simiacryptus/mindseye/layers/java/BiasLayer.java",
"javaDoc": ""
}
}