Subreport: Logs for com.simiacryptus.ref.lang.ReferenceCountingBase
Using Seed 5390004519365971968
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: [ -1.616, 1.356, 1.512, 1.556, 1.032 ]
Inputs Statistics: {meanExponent=0.14518842541593246, negative=1, min=-1.616, max=1.556, mean=0.768, count=5, sum=3.84, positive=4, stdDev=1.206099166735472, zeros=0}
Output: [ -2.42, 2.4640000000000004, 1.54, 0.8440000000000001, 2.08 ]
Outputs Statistics: {meanExponent=0.2414765143795398, negative=1, min=-2.42, max=2.4640000000000004, mean=0.9016000000000002, count=5, sum=4.508000000000001, positive=4, stdDev=1.7477768278587515, 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: [ -1.616, 1.356, 1.512, 1.556, 1.032 ]
Value Statistics: {meanExponent=0.14518842541593246, negative=1, min=-1.616, max=1.556, mean=0.768, count=5, sum=3.84, positive=4, stdDev=1.206099166735472, 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: [ [ 0.9999999999976694, 0.0, 0.0, 0.0, 0.0 ], [ 0.0, 0.9999999999976694, 0.0, 0.0, 0.0 ], [ 0.0, 0.0, 0.9999999999998899, 0.0, 0.0 ], [ 0.0, 0.0, 0.0, 0.9999999999998899, 0.0 ], [ 0.0, 0.0, 0.0, 0.0, 0.9999999999976694 ] ]
Measured Statistics: {meanExponent=-6.26427122273724E-13, negative=0, min=0.0, max=0.9999999999998899, mean=0.19999999999971152, count=25, sum=4.999999999992788, positive=5, stdDev=0.39999999999942304, zeros=20}
Feedback Error: [ [ -2.3305801732931286E-12, 0.0, 0.0, 0.0, 0.0 ], [ 0.0, -2.3305801732931286E-12, 0.0, 0.0, 0.0 ], [ 0.0, 0.0, -1.1013412404281553E-13, 0.0, 0.0 ], [ 0.0, 0.0, 0.0, -1.1013412404281553E-13, 0.0 ], [ 0.0, 0.0, 0.0, 0.0, -2.3305801732931286E-12 ] ]
Error Statistics: {meanExponent=-12.162752810711991, negative=5, min=-2.3305801732931286E-12, max=0.0, mean=-2.884803507186007E-13, count=25, sum=-7.212008767965017E-12, positive=0, stdDev=7.546800133627568E-13, zeros=20}
Returns
{
"absoluteTol" : {
"count" : 25,
"sum" : 7.212008767965017E-12,
"min" : 0.0,
"max" : 2.3305801732931286E-12,
"sumOfSquare" : 1.6319070882998445E-23,
"standardDeviation" : 7.546800133627568E-13,
"average" : 2.884803507186007E-13
},
"relativeTol" : {
"count" : 5,
"sum" : 3.6060043839865882E-12,
"min" : 5.50670620214108E-14,
"max" : 1.1652900866479222E-12,
"sumOfSquare" : 4.079767720759106E-24,
"standardDeviation" : 5.438959822048711E-13,
"average" : 7.212008767973177E-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: [ -0.804, 1.108, 0.028, -0.712, 1.048 ]
Implemented Gradient: [ [ 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 Gradient: [ [ 0.9999999999976694, 0.0, 0.0, 0.0, 0.0 ], [ 0.0, 0.9999999999976694, 0.0, 0.0, 0.0 ], [ 0.0, 0.0, 0.9999999999998899, 0.0, 0.0 ], [ 0.0, 0.0, 0.0, 0.9999999999998899, 0.0 ], [ 0.0, 0.0, 0.0, 0.0, 0.9999999999976694 ] ]
Measured Statistics: {meanExponent=-6.26427122273724E-13, negative=0, min=0.0, max=0.9999999999998899, mean=0.19999999999971152, count=25, sum=4.999999999992788, positive=5, stdDev=0.39999999999942304, zeros=20}
Gradient Error: [ [ -2.3305801732931286E-12, 0.0, 0.0, 0.0, 0.0 ], [ 0.0, -2.3305801732931286E-12, 0.0, 0.0, 0.0 ], [ 0.0, 0.0, -1.1013412404281553E-13, 0.0, 0.0 ], [ 0.0, 0.0, 0.0, -1.1013412404281553E-13, 0.0 ], [ 0.0, 0.0, 0.0, 0.0, -2.3305801732931286E-12 ] ]
Error Statistics: {meanExponent=-12.162752810711991, negative=5, min=-2.3305801732931286E-12, max=0.0, mean=-2.884803507186007E-13, count=25, sum=-7.212008767965017E-12, positive=0, stdDev=7.546800133627568E-13, zeros=20}
Returns
{
"absoluteTol" : {
"count" : 50,
"sum" : 1.4424017535930034E-11,
"min" : 0.0,
"max" : 2.3305801732931286E-12,
"sumOfSquare" : 3.263814176599689E-23,
"standardDeviation" : 7.546800133627568E-13,
"average" : 2.884803507186007E-13
},
"relativeTol" : {
"count" : 10,
"sum" : 7.2120087679731764E-12,
"min" : 5.50670620214108E-14,
"max" : 1.1652900866479222E-12,
"sumOfSquare" : 8.159535441518212E-24,
"standardDeviation" : 5.438959822048711E-13,
"average" : 7.212008767973177E-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: 2.8848e-13 +- 7.5468e-13 [0.0000e+00 - 2.3306e-12] (50#)
relativeTol: 7.2120e-13 +- 5.4390e-13 [5.5067e-14 - 1.1653e-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=2.8848e-13 +- 7.5468e-13 [0.0000e+00 - 2.3306e-12] (50#), relativeTol=7.2120e-13 +- 5.4390e-13 [5.5067e-14 - 1.1653e-12] (10#)} | OK |
{
"result": "OK",
"performance": {
"execution_time": "0.169",
"gc_time": "0.097"
},
"created_on": 1586735328627,
"file_name": "derivativeTest",
"report": {
"simpleName": "Basic",
"canonicalName": "com.simiacryptus.mindseye.layers.java.BiasLayerTest.Basic",
"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/Basic/derivativeTest/202004124848",
"id": "6348c50f-a01b-408d-9e17-01a85886c89d",
"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": ""
}
}