Subreport: Logs for com.simiacryptus.ref.lang.ReferenceCountingBase
Using Seed 4352616360432730112
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 ], [ -0.608 ], [ 1.208 ], [ -0.852 ] ],
[ [ 0.7 ], [ 1.764 ], [ -1.72 ], [ 1.912 ] ],
[ [ -0.128 ], [ 0.048 ], [ -1.028 ], [ -1.688 ] ],
[ [ 0.496 ], [ 1.524 ], [ -0.384 ], [ -0.804 ] ]
]
Inputs Statistics: {meanExponent=-0.20600101677773192, negative=8, min=-1.72, max=1.912, mean=0.0325, count=16, sum=0.52, positive=8, stdDev=1.1193595266937248, zeros=0}
Output: [
[ [ 0.0064 ], [ 0.369664 ], [ 1.459264 ], [ 0.725904 ] ],
[ [ 0.48999999999999994 ], [ 3.1116960000000002 ], [ 2.9583999999999997 ], [ 3.655744 ] ],
[ [ 0.016384 ], [ 0.002304 ], [ 1.056784 ], [ 2.849344 ] ],
[ [ 0.24601599999999998 ], [ 2.322576 ], [ 0.147456 ], [ 0.6464160000000001 ] ]
]
Outputs Statistics: {meanExponent=-0.4120020335554638, negative=0, min=0.002304, max=3.655744, mean=1.2540219999999997, count=16, sum=20.064351999999996, positive=16, stdDev=1.2450300162020191, zeros=0}
We validate the agreement between the implemented derivative of the inputs apply finite difference estimations:
SingleDerivativeTester.java:117 executed in 0.27 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 ], [ -0.608 ], [ 1.208 ], [ -0.852 ] ],
[ [ 0.7 ], [ 1.764 ], [ -1.72 ], [ 1.912 ] ],
[ [ -0.128 ], [ 0.048 ], [ -1.028 ], [ -1.688 ] ],
[ [ 0.496 ], [ 1.524 ], [ -0.384 ], [ -0.804 ] ]
]
Value Statistics: {meanExponent=-0.20600101677773192, negative=8, min=-1.72, max=1.912, mean=0.0325, count=16, sum=0.52, positive=8, stdDev=1.1193595266937248, zeros=0}
Implemented Feedback: [ [ 0.16, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ... ], [ 0.0, 1.4, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ... ], [ 0.0, 0.0, -0.256, 0.0, 0.0, 0.0, 0.0, 0.0, ... ], [ 0.0, 0.0, 0.0, 0.992, 0.0, 0.0, 0.0, 0.0, ... ], [ 0.0, 0.0, 0.0, 0.0, -1.216, 0.0, 0.0, 0.0, ... ], [ 0.0, 0.0, 0.0, 0.0, 0.0, 3.528, 0.0, 0.0, ... ], [ 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.096, 0.0, ... ], [ 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 3.048, ... ], ... ]
Implemented Statistics: {meanExponent=0.09502897888624931, negative=8, min=-3.44, max=3.824, mean=0.0040625, count=256, sum=1.04, positive=8, stdDev=0.5599008805974053, zeros=240}
Measured Feedback: [ [ 0.1601000000000033, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ... ], [ 0.0, 1.4000999999996822, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ... ], [ 0.0, 0.0, -0.2558999999999548, 0.0, 0.0, 0.0, 0.0, 0.0, ... ], [ 0.0, 0.0, 0.0, 0.9920999999998847, 0.0, 0.0, 0.0, 0.0, ... ], [ 0.0, 0.0, 0.0, 0.0, -1.2159000000000475, 0.0, 0.0, 0.0, ... ], [ 0.0, 0.0, 0.0, 0.0, 0.0, 3.5280999999987017, 0.0, 0.0, ... ], [ 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.09610000000000174, 0.0, ... ], [ 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 3.0480999999982217, ... ], ... ]
Measured Statistics: {meanExponent=0.09505979972472121, negative=8, min=-3.4398999999973867, max=3.8240999999983316, mean=0.004068749999991832, count=256, sum=1.041599999997909, positive=8, stdDev=0.5599015613464833, zeros=240}
Feedback Error: [ [ 1.000000000032919E-4, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ... ], [ 0.0, 9.999999968224316E-5, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ... ], [ 0.0, 0.0, 1.0000000004523057E-4, 0.0, 0.0, 0.0, 0.0, 0.0, ... ], [ 0.0, 0.0, 0.0, 9.999999988474784E-5, 0.0, 0.0, 0.0, 0.0, ... ], [ 0.0, 0.0, 0.0, 0.0, 9.999999995247144E-5, 0.0, 0.0, 0.0, ... ], [ 0.0, 0.0, 0.0, 0.0, 0.0, 9.999999870169418E-5, 0.0, 0.0, ... ], [ 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0000000000173759E-4, 0.0, ... ], [ 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 9.999999822163375E-5, ... ], ... ]
Error Statistics: {meanExponent=-4.000000000567765, negative=0, min=0.0, max=1.0000000261323194E-4, mean=6.249999991829198E-6, count=256, sum=0.0015999999979082746, positive=16, stdDev=2.420614588215098E-5, zeros=240}
Returns
{
"absoluteTol" : {
"count" : 256,
"sum" : 0.0015999999979082746,
"min" : 0.0,
"max" : 1.0000000261323194E-4,
"sumOfSquare" : 1.5999999958165493E-7,
"standardDeviation" : 2.4206145882150976E-5,
"average" : 6.249999991829198E-6
},
"relativeTol" : {
"count" : 16,
"sum" : 0.001399108069324463,
"min" : 1.3075142627814711E-5,
"max" : 5.20562207192799E-4,
"sumOfSquare" : 4.204069731887917E-7,
"standardDeviation" : 1.3648786835643583E-4,
"average" : 8.744425433277894E-5
}
}
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" : 256,
"sum" : 0.0015999999979082746,
"min" : 0.0,
"max" : 1.0000000261323194E-4,
"sumOfSquare" : 1.5999999958165493E-7,
"standardDeviation" : 2.4206145882150976E-5,
"average" : 6.249999991829198E-6
},
"relativeTol" : {
"count" : 16,
"sum" : 0.001399108069324463,
"min" : 1.3075142627814711E-5,
"max" : 5.20562207192799E-4,
"sumOfSquare" : 4.204069731887917E-7,
"standardDeviation" : 1.3648786835643583E-4,
"average" : 8.744425433277894E-5
}
}
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: 6.2500e-06 +- 2.4206e-05 [0.0000e+00 - 1.0000e-04] (256#)
relativeTol: 8.7444e-05 +- 1.3649e-04 [1.3075e-05 - 5.2056e-04] (16#)
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=6.2500e-06 +- 2.4206e-05 [0.0000e+00 - 1.0000e-04] (256#), relativeTol=8.7444e-05 +- 1.3649e-04 [1.3075e-05 - 5.2056e-04] (16#)} | OK |
{
"result": "OK",
"performance": {
"execution_time": "0.476",
"gc_time": "0.150"
},
"created_on": 1586748509521,
"file_name": "derivativeTest",
"report": {
"simpleName": "Double",
"canonicalName": "com.simiacryptus.mindseye.layers.cudnn.SquareActivationLayerTest.Double",
"link": "https://github.com/SimiaCryptus/mindseye-cudnn/tree/59d5b3318556370acb2d83ee6ec123ce0fc6974f/src/test/java/com/simiacryptus/mindseye/layers/cudnn/SquareActivationLayerTest.java",
"javaDoc": ""
},
"archive": "s3://code.simiacrypt.us/tests/com/simiacryptus/mindseye/layers/cudnn/SquareActivationLayer/Double/derivativeTest/202004132829",
"id": "af7557c2-371c-4c55-a1a2-756e339c25ae",
"report_type": "Components",
"display_name": "Derivative Validation",
"target": {
"simpleName": "SquareActivationLayer",
"canonicalName": "com.simiacryptus.mindseye.layers.cudnn.SquareActivationLayer",
"link": "https://github.com/SimiaCryptus/mindseye-cudnn/tree/59d5b3318556370acb2d83ee6ec123ce0fc6974f/src/main/java/com/simiacryptus/mindseye/layers/cudnn/SquareActivationLayer.java",
"javaDoc": ""
}
}