Subreport: Logs for com.simiacryptus.ref.lang.ReferenceCountingBase
Using Seed 3728447707376481280
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, -0.128 ], [ 0.7, 0.496 ] ]
Inputs Statistics: {meanExponent=-0.6122800817139336, negative=1, min=-0.128, max=0.7, mean=0.287, count=4, sum=1.148, positive=3, stdDev=0.3276141022605712, zeros=0}
Output: [ [ 0.08, -0.128 ], [ 0.7, 0.496 ] ]
Outputs Statistics: {meanExponent=-0.6122800817139336, negative=1, min=-0.128, max=0.7, mean=0.287, count=4, sum=1.148, positive=3, stdDev=0.3276141022605712, 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, -0.128 ], [ 0.7, 0.496 ] ]
Value Statistics: {meanExponent=-0.6122800817139336, negative=1, min=-0.128, max=0.7, mean=0.287, count=4, sum=1.148, positive=3, stdDev=0.3276141022605712, zeros=0}
Implemented Feedback: [ [ 1.0, 0.0, 0.0, 0.0 ], [ 0.0, 1.0, 0.0, 0.0 ], [ 0.0, 0.0, 1.0, 0.0 ], [ 0.0, 0.0, 0.0, 1.0 ] ]
Implemented Statistics: {meanExponent=0.0, negative=0, min=0.0, max=1.0, mean=0.25, count=16, sum=4.0, positive=4, stdDev=0.4330127018922193, zeros=12}
Measured Feedback: [ [ 1.0000000000000286, 0.0, 0.0, 0.0 ], [ 0.0, 0.9999999999998899, 0.0, 0.0 ], [ 0.0, 0.0, 0.9999999999998899, 0.0 ], [ 0.0, 0.0, 0.0, 0.9999999999998899 ] ]
Measured Statistics: {meanExponent=-3.276302567614995E-14, negative=0, min=0.0, max=1.0000000000000286, mean=0.24999999999998113, count=16, sum=3.999999999999698, positive=4, stdDev=0.43301270189218666, zeros=12}
Feedback Error: [ [ 2.864375403532904E-14, 0.0, 0.0, 0.0 ], [ 0.0, -1.1013412404281553E-13, 0.0, 0.0 ], [ 0.0, 0.0, -1.1013412404281553E-13, 0.0 ], [ 0.0, 0.0, 0.0, -1.1013412404281553E-13 ] ]
Error Statistics: {meanExponent=-13.104301089584563, negative=3, min=-1.1013412404281553E-13, max=2.864375403532904E-14, mean=-1.8859913630819847E-14, count=16, sum=-3.0175861809311755E-13, positive=1, stdDev=4.4383202767685317E-14, zeros=12}
Returns
{
"absoluteTol" : {
"count" : 16,
"sum" : 3.590461261637756E-13,
"min" : 0.0,
"max" : 1.1013412404281553E-13,
"sumOfSquare" : 3.720904048127126E-26,
"standardDeviation" : 4.26848245403851E-14,
"average" : 2.2440382885235977E-14
},
"relativeTol" : {
"count" : 4,
"sum" : 1.7952306308189672E-13,
"min" : 1.4321877017664317E-14,
"max" : 5.50670620214108E-14,
"sumOfSquare" : 9.302260120318813E-27,
"standardDeviation" : 1.7643182647570603E-14,
"average" : 4.488076577047418E-14
}
}
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" : 16,
"sum" : 3.590461261637756E-13,
"min" : 0.0,
"max" : 1.1013412404281553E-13,
"sumOfSquare" : 3.720904048127126E-26,
"standardDeviation" : 4.26848245403851E-14,
"average" : 2.2440382885235977E-14
},
"relativeTol" : {
"count" : 4,
"sum" : 1.7952306308189672E-13,
"min" : 1.4321877017664317E-14,
"max" : 5.50670620214108E-14,
"sumOfSquare" : 9.302260120318813E-27,
"standardDeviation" : 1.7643182647570603E-14,
"average" : 4.488076577047418E-14
}
}
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: 2.2440e-14 +- 4.2685e-14 [0.0000e+00 - 1.1013e-13] (16#)
relativeTol: 4.4881e-14 +- 1.7643e-14 [1.4322e-14 - 5.5067e-14] (4#)
SingleDerivativeTester.java:156 executed in 0.00 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=2.2440e-14 +- 4.2685e-14 [0.0000e+00 - 1.1013e-13] (16#), relativeTol=4.4881e-14 +- 1.7643e-14 [1.4322e-14 - 5.5067e-14] (4#)} | OK |
{
"result": "OK",
"performance": {
"execution_time": "0.163",
"gc_time": "0.110"
},
"created_on": 1586739186868,
"file_name": "derivativeTest",
"report": {
"simpleName": "Basic",
"canonicalName": "com.simiacryptus.mindseye.layers.java.AssertDimensionsLayerTest.Basic",
"link": "https://github.com/SimiaCryptus/mindseye-java/tree/93db34cedee48c0202777a2b25deddf1dfaf5731/src/test/java/com/simiacryptus/mindseye/layers/java/AssertDimensionsLayerTest.java",
"javaDoc": ""
},
"archive": "s3://code.simiacrypt.us/tests/com/simiacryptus/mindseye/layers/java/AssertDimensionsLayer/Basic/derivativeTest/202004135306",
"id": "2ea99273-698f-469b-a028-132ff9382c7c",
"report_type": "Components",
"display_name": "Derivative Validation",
"target": {
"simpleName": "AssertDimensionsLayer",
"canonicalName": "com.simiacryptus.mindseye.layers.java.AssertDimensionsLayer",
"link": "https://github.com/SimiaCryptus/mindseye-java/tree/93db34cedee48c0202777a2b25deddf1dfaf5731/src/main/java/com/simiacryptus/mindseye/layers/java/AssertDimensionsLayer.java",
"javaDoc": ""
}
}