Subreport: Logs for com.simiacryptus.ref.lang.ReferenceCountingBase
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: [
[ [ 0.5199893401555818 ], [ 0.46804361920235915 ] ],
[ [ 0.6681877721681662 ], [ 0.621518856927611 ] ]
]
Outputs Statistics: {meanExponent=-0.24884159923755497, negative=0, min=0.46804361920235915, max=0.6681877721681662, mean=0.5694348971134295, count=4, sum=2.277739588453718, positive=4, stdDev=0.07935666881367515, zeros=0}
We validate the agreement between the implemented derivative of the inputs apply finite difference estimations:
SingleDerivativeTester.java:117 executed in 0.06 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: [ [ 0.24960042628014445, 0.0, 0.0, 0.0 ], [ 0.0, 0.22171287329310904, 0.0, 0.0 ], [ 0.0, 0.0, 0.24897878972631618, 0.0 ], [ 0.0, 0.0, 0.0, 0.2352331674110068 ] ]
Implemented Statistics: {meanExponent=-0.6223257147813432, negative=0, min=0.0, max=0.24960042628014445, mean=0.05972032854441103, count=16, sum=0.9555252567105765, positive=4, stdDev=0.10359710707341013, zeros=12}
Measured Feedback: [ [ 0.24959992713680101, 0.0, 0.0, 0.0 ], [ 0.0, 0.22170914423025323, 0.0, 0.0 ], [ 0.0, 0.0, 0.24897958516845176, 0.0 ], [ 0.0, 0.0, 0.0, 0.23523030872385498 ] ]
Measured Statistics: {meanExponent=-0.6223287306389902, negative=0, min=0.0, max=0.24959992713680101, mean=0.05971993532871006, count=16, sum=0.955518965259361, positive=4, stdDev=0.10359647358390664, zeros=12}
Feedback Error: [ [ -4.991433434353709E-7, 0.0, 0.0, 0.0 ], [ 0.0, -3.72906285581176E-6, 0.0, 0.0 ], [ 0.0, 0.0, 7.954421355760299E-7, 0.0 ], [ 0.0, 0.0, 0.0, -2.8586871518299084E-6 ] ]
Error Statistics: {meanExponent=-5.843349947589542, negative=3, min=-3.72906285581176E-6, max=7.954421355760299E-7, mean=-3.932157009688131E-7, count=16, sum=-6.291451215501009E-6, positive=1, stdDev=1.1315359274887063E-6, zeros=12}
Returns
{
"absoluteTol" : {
"count" : 16,
"sum" : 7.88233548665307E-6,
"min" : 0.0,
"max" : 3.72906285581176E-6,
"sumOfSquare" : 2.2959874282977947E-11,
"standardDeviation" : 1.0919212851586005E-6,
"average" : 4.926459679158168E-7
},
"relativeTol" : {
"count" : 4,
"sum" : 1.708335148676086E-5,
"min" : 9.998857973934025E-7,
"max" : 8.409737695569637E-6,
"sumOfSquare" : 1.1119684670620836E-10,
"standardDeviation" : 3.0917884064548608E-6,
"average" : 4.270837871690215E-6
}
}
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" : 7.88233548665307E-6,
"min" : 0.0,
"max" : 3.72906285581176E-6,
"sumOfSquare" : 2.2959874282977947E-11,
"standardDeviation" : 1.0919212851586005E-6,
"average" : 4.926459679158168E-7
},
"relativeTol" : {
"count" : 4,
"sum" : 1.708335148676086E-5,
"min" : 9.998857973934025E-7,
"max" : 8.409737695569637E-6,
"sumOfSquare" : 1.1119684670620836E-10,
"standardDeviation" : 3.0917884064548608E-6,
"average" : 4.270837871690215E-6
}
}
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: 4.9265e-07 +- 1.0919e-06 [0.0000e+00 - 3.7291e-06] (16#)
relativeTol: 4.2708e-06 +- 3.0918e-06 [9.9989e-07 - 8.4097e-06] (4#)
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=4.9265e-07 +- 1.0919e-06 [0.0000e+00 - 3.7291e-06] (16#), relativeTol=4.2708e-06 +- 3.0918e-06 [9.9989e-07 - 8.4097e-06] (4#)} | OK |
{
"result": "OK",
"performance": {
"execution_time": "0.238",
"gc_time": "0.127"
},
"created_on": 1586740493441,
"file_name": "derivativeTest",
"report": {
"simpleName": "Sigmoid_Double",
"canonicalName": "com.simiacryptus.mindseye.layers.cudnn.ActivationLayerTest.Sigmoid_Double",
"link": "https://github.com/SimiaCryptus/mindseye-cudnn/tree/59d5b3318556370acb2d83ee6ec123ce0fc6974f/src/test/java/com/simiacryptus/mindseye/layers/cudnn/ActivationLayerTest.java",
"javaDoc": ""
},
"archive": "s3://code.simiacrypt.us/tests/com/simiacryptus/mindseye/layers/cudnn/ActivationLayer/Sigmoid_Double/derivativeTest/202004131453",
"id": "08d4516c-c031-474b-8ffe-ec346d06337c",
"report_type": "Components",
"display_name": "Derivative Validation",
"target": {
"simpleName": "ActivationLayer",
"canonicalName": "com.simiacryptus.mindseye.layers.cudnn.ActivationLayer",
"link": "https://github.com/SimiaCryptus/mindseye-cudnn/tree/59d5b3318556370acb2d83ee6ec123ce0fc6974f/src/main/java/com/simiacryptus/mindseye/layers/cudnn/ActivationLayer.java",
"javaDoc": ""
}
}