Subreport: Logs for com.simiacryptus.ref.lang.ReferenceCountingBase
Using Seed 1740156076053100544
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 ]
Inputs Statistics: {meanExponent=-0.5330433495165998, negative=2, min=-0.608, max=0.7, mean=0.10799999999999998, count=5, sum=0.5399999999999999, positive=3, stdDev=0.4626324675160618, zeros=0}
Output: [ 0.0, 1.3999999999999997, 0.0, 0.0, 0.0 ]
Outputs Statistics: {meanExponent=0.14612803567823793, negative=0, min=0.0, max=1.3999999999999997, mean=0.2799999999999999, count=5, sum=1.3999999999999997, positive=1, stdDev=0.5599999999999998, zeros=4}
We validate the agreement between the implemented derivative of the inputs apply finite difference estimations:
SingleDerivativeTester.java:117 executed in 0.10 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, 0.496, -0.608 ]
Value Statistics: {meanExponent=-0.5330433495165998, negative=2, min=-0.608, max=0.7, mean=0.10799999999999998, count=5, sum=0.5399999999999999, positive=3, stdDev=0.4626324675160618, zeros=0}
Implemented Feedback: [ [ 0.0, 0.0, 0.0, 0.0, 0.0 ], [ 0.0, 2.0, 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 ] ]
Implemented Statistics: {meanExponent=0.3010299956639812, negative=0, min=0.0, max=2.0, mean=0.08, count=25, sum=2.0, positive=1, stdDev=0.3919183588453085, zeros=24}
Measured Feedback: [ [ 0.0, 0.0, 0.0, 0.0, 0.0 ], [ 0.0, 2.000000000002, 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 ] ]
Measured Statistics: {meanExponent=0.3010299956644155, negative=0, min=0.0, max=2.000000000002, mean=0.08000000000008001, count=25, sum=2.000000000002, positive=1, stdDev=0.3919183588457004, zeros=24}
Feedback Error: [ [ 0.0, 0.0, 0.0, 0.0, 0.0 ], [ 0.0, 2.000177801164682E-12, 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 ] ]
Error Statistics: {meanExponent=-11.69893139701975, negative=0, min=0.0, max=2.000177801164682E-12, mean=8.000711204658728E-14, count=25, sum=2.000177801164682E-12, positive=1, stdDev=3.9195320061563997E-13, zeros=24}
Returns
{
"absoluteTol" : {
"count" : 25,
"sum" : 2.000177801164682E-12,
"min" : 0.0,
"max" : 2.000177801164682E-12,
"sumOfSquare" : 4.000711236271982E-24,
"standardDeviation" : 3.9195320061563997E-13,
"average" : 8.000711204658728E-14
},
"relativeTol" : {
"count" : 1,
"sum" : 5.000444502909205E-13,
"min" : 5.000444502909205E-13,
"max" : 5.000444502909205E-13,
"sumOfSquare" : 2.5004445226674887E-25,
"standardDeviation" : 0.0,
"average" : 5.000444502909205E-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" : 25,
"sum" : 2.000177801164682E-12,
"min" : 0.0,
"max" : 2.000177801164682E-12,
"sumOfSquare" : 4.000711236271982E-24,
"standardDeviation" : 3.9195320061563997E-13,
"average" : 8.000711204658728E-14
},
"relativeTol" : {
"count" : 1,
"sum" : 5.000444502909205E-13,
"min" : 5.000444502909205E-13,
"max" : 5.000444502909205E-13,
"sumOfSquare" : 2.5004445226674887E-25,
"standardDeviation" : 0.0,
"average" : 5.000444502909205E-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: 8.0007e-14 +- 3.9195e-13 [0.0000e+00 - 2.0002e-12] (25#)
relativeTol: 5.0004e-13 +- 0.0000e+00 [5.0004e-13 - 5.0004e-13] (1#)
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());
class | details | result |
---|---|---|
com.simiacryptus.mindseye.test.unit.SingleDerivativeTester | ToleranceStatistics{absoluteTol=8.0007e-14 +- 3.9195e-13 [0.0000e+00 - 2.0002e-12] (25#), relativeTol=5.0004e-13 +- 0.0000e+00 [5.0004e-13 - 5.0004e-13] (1#)} | OK |
{
"result": "OK",
"performance": {
"execution_time": "0.273",
"gc_time": "0.105"
},
"created_on": 1586739404945,
"file_name": "derivativeTest",
"report": {
"simpleName": "Basic",
"canonicalName": "com.simiacryptus.mindseye.layers.java.BinaryNoiseLayerTest.Basic",
"link": "https://github.com/SimiaCryptus/mindseye-java/tree/93db34cedee48c0202777a2b25deddf1dfaf5731/src/test/java/com/simiacryptus/mindseye/layers/java/BinaryNoiseLayerTest.java",
"javaDoc": ""
},
"archive": "s3://code.simiacrypt.us/tests/com/simiacryptus/mindseye/layers/java/StochasticSamplingSubnetLayer/Basic/derivativeTest/202004135644",
"id": "df3bf71b-a014-4ccd-bbae-d9af526a760a",
"report_type": "Components",
"display_name": "Derivative Validation",
"target": {
"simpleName": "StochasticSamplingSubnetLayer",
"canonicalName": "com.simiacryptus.mindseye.layers.java.StochasticSamplingSubnetLayer",
"link": "https://github.com/SimiaCryptus/mindseye-java/tree/93db34cedee48c0202777a2b25deddf1dfaf5731/src/main/java/com/simiacryptus/mindseye/layers/java/StochasticSamplingSubnetLayer.java",
"javaDoc": ""
}
}