Subreport: Logs for com.simiacryptus.ref.lang.ReferenceCountingBase
Using Seed 2553622520803406848
SingleDerivativeTester.java:101 executed in 0.01 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 ], [ 0.048 ], [ -0.852 ], [ -0.712 ], [ 1.032 ], [ -0.768 ] ],
[ [ 0.7 ], [ 1.524 ], [ 1.912 ], [ 1.048 ], [ 0.3 ], [ -0.068 ] ],
[ [ -0.128 ], [ 1.208 ], [ -1.688 ], [ -1.616 ], [ 0.636 ], [ 1.612 ] ],
[ [ 0.496 ], [ -1.72 ], [ -0.804 ], [ 1.356 ], [ -0.176 ], [ 1.64 ] ],
[ [ -0.608 ], [ -1.028 ], [ 1.108 ], [ 1.512 ], [ 1.556 ], [ 0.392 ] ],
[ [ 1.764 ], [ -0.384 ], [ 0.028 ], [ 1.556 ], [ 0.788 ], [ 0.092 ] ]
]
Inputs Statistics: {meanExponent=-0.2192709808999687, negative=13, min=-1.72, max=1.912, mean=0.3287777777777778, count=36, sum=11.836, positive=23, stdDev=1.0387145129948714, zeros=0}
Output: [
[ [ 0.08 ], [ 0.048 ], [ -0.852 ], [ -0.712 ], [ 1.032 ], [ -0.768 ] ],
[ [ 0.7 ], [ 1.524 ], [ 1.912 ], [ 1.048 ], [ 0.3 ], [ -0.068 ] ],
[ [ -0.128 ], [ 1.208 ], [ -1.688 ], [ -1.616 ], [ 0.636 ], [ 1.612 ] ],
[ [ 0.496 ], [ -1.72 ], [ -0.804 ], [ 1.356 ], [ -0.176 ], [ 1.64 ] ],
[ [ -0.608 ], [ -1.028 ], [ 1.108 ], [ 1.512 ], [ 1.556 ], [ 0.392 ] ],
[ [ 1.764 ], [ -0.384 ], [ 0.028 ], [ 1.556 ], [ 0.788 ], [ 0.092 ] ]
]
Outputs Statistics: {meanExponent=-0.2192709808999687, negative=13, min=-1.72, max=1.912, mean=0.3287777777777778, count=36, sum=11.836, positive=23, stdDev=1.0387145129948714, zeros=0}
We validate the agreement between the implemented derivative of the inputs apply finite difference estimations:
SingleDerivativeTester.java:117 executed in 0.19 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 ], [ 0.048 ], [ -0.852 ], [ -0.712 ], [ 1.032 ], [ -0.768 ] ],
[ [ 0.7 ], [ 1.524 ], [ 1.912 ], [ 1.048 ], [ 0.3 ], [ -0.068 ] ],
[ [ -0.128 ], [ 1.208 ], [ -1.688 ], [ -1.616 ], [ 0.636 ], [ 1.612 ] ],
[ [ 0.496 ], [ -1.72 ], [ -0.804 ], [ 1.356 ], [ -0.176 ], [ 1.64 ] ],
[ [ -0.608 ], [ -1.028 ], [ 1.108 ], [ 1.512 ], [ 1.556 ], [ 0.392 ] ],
[ [ 1.764 ], [ -0.384 ], [ 0.028 ], [ 1.556 ], [ 0.788 ], [ 0.092 ] ]
]
Value Statistics: {meanExponent=-0.2192709808999687, negative=13, min=-1.72, max=1.912, mean=0.3287777777777778, count=36, sum=11.836, positive=23, stdDev=1.0387145129948714, zeros=0}
Implemented Feedback: [ [ 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ... ], [ 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ... ], [ 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, ... ], [ 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, ... ], [ 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, ... ], [ 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, ... ], [ 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, ... ], [ 0.0, 0.0, 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=0.027777777777777776, count=1296, sum=36.0, positive=36, stdDev=0.16433554953054488, zeros=1260}
Measured Feedback: [ [ 1.0000000000000286, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ... ], [ 0.0, 0.9999999999998899, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ... ], [ 0.0, 0.0, 0.9999999999998899, 0.0, 0.0, 0.0, 0.0, 0.0, ... ], [ 0.0, 0.0, 0.0, 0.9999999999998899, 0.0, 0.0, 0.0, 0.0, ... ], [ 0.0, 0.0, 0.0, 0.0, 0.9999999999998899, 0.0, 0.0, 0.0, ... ], [ 0.0, 0.0, 0.0, 0.0, 0.0, 0.9999999999998899, 0.0, 0.0, ... ], [ 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0000000000000286, 0.0, ... ], [ 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.9999999999998899, ... ], ... ]
Measured Statistics: {meanExponent=-3.987761942938778E-14, negative=0, min=0.0, max=1.0000000000000286, mean=0.027777777777775223, count=1296, sum=35.99999999999669, positive=36, stdDev=0.1643355495305298, zeros=1260}
Feedback Error: [ [ 2.864375403532904E-14, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ... ], [ 0.0, -1.1013412404281553E-13, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ... ], [ 0.0, 0.0, -1.1013412404281553E-13, 0.0, 0.0, 0.0, 0.0, 0.0, ... ], [ 0.0, 0.0, 0.0, -1.1013412404281553E-13, 0.0, 0.0, 0.0, 0.0, ... ], [ 0.0, 0.0, 0.0, 0.0, -1.1013412404281553E-13, 0.0, 0.0, 0.0, ... ], [ 0.0, 0.0, 0.0, 0.0, 0.0, -1.1013412404281553E-13, 0.0, 0.0, ... ], [ 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 2.864375403532904E-14, 0.0, ... ], [ 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, -1.1013412404281553E-13, ... ], ... ]
Error Statistics: {meanExponent=-13.058180480733906, negative=32, min=-1.1013412404281553E-13, max=2.864375403532904E-14, mean=-2.550600334505356E-15, count=1296, sum=-3.305578033518941E-12, positive=4, stdDev=1.691714276507881E-14, zeros=1260}
Returns
{
"absoluteTol" : {
"count" : 1296,
"sum" : 3.5347280658015734E-12,
"min" : 0.0,
"max" : 1.1013412404281553E-13,
"sumOfSquare" : 3.7933308469496645E-25,
"standardDeviation" : 1.688953807200673E-14,
"average" : 2.7274136310197325E-15
},
"relativeTol" : {
"count" : 36,
"sum" : 1.76736403290088E-12,
"min" : 2.9976021664879317E-15,
"max" : 5.50670620214108E-14,
"sumOfSquare" : 9.483327117375196E-26,
"standardDeviation" : 1.496967452719532E-14,
"average" : 4.9093445358357775E-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" : 1296,
"sum" : 3.5347280658015734E-12,
"min" : 0.0,
"max" : 1.1013412404281553E-13,
"sumOfSquare" : 3.7933308469496645E-25,
"standardDeviation" : 1.688953807200673E-14,
"average" : 2.7274136310197325E-15
},
"relativeTol" : {
"count" : 36,
"sum" : 1.76736403290088E-12,
"min" : 2.9976021664879317E-15,
"max" : 5.50670620214108E-14,
"sumOfSquare" : 9.483327117375196E-26,
"standardDeviation" : 1.496967452719532E-14,
"average" : 4.9093445358357775E-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: 2.7274e-15 +- 1.6890e-14 [0.0000e+00 - 1.1013e-13] (1296#)
relativeTol: 4.9093e-14 +- 1.4970e-14 [2.9976e-15 - 5.5067e-14] (36#)
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=2.7274e-15 +- 1.6890e-14 [0.0000e+00 - 1.1013e-13] (1296#), relativeTol=4.9093e-14 +- 1.4970e-14 [2.9976e-15 - 5.5067e-14] (36#)} | OK |
{
"result": "OK",
"performance": {
"execution_time": "0.379",
"gc_time": "0.142"
},
"created_on": 1586739116077,
"file_name": "derivativeTest",
"report": {
"simpleName": "Basic",
"canonicalName": "com.simiacryptus.mindseye.layers.java.ImgTileSubnetLayerTest.Basic",
"link": "https://github.com/SimiaCryptus/mindseye-java/tree/93db34cedee48c0202777a2b25deddf1dfaf5731/src/test/java/com/simiacryptus/mindseye/layers/java/ImgTileSubnetLayerTest.java",
"javaDoc": ""
},
"archive": "s3://code.simiacrypt.us/tests/com/simiacryptus/mindseye/layers/java/ImgTileSubnetLayer/Basic/derivativeTest/202004135156",
"id": "8f1321de-92e7-4a53-813a-3328f5867b23",
"report_type": "Components",
"display_name": "Derivative Validation",
"target": {
"simpleName": "ImgTileSubnetLayer",
"canonicalName": "com.simiacryptus.mindseye.layers.java.ImgTileSubnetLayer",
"link": "https://github.com/SimiaCryptus/mindseye-java/tree/93db34cedee48c0202777a2b25deddf1dfaf5731/src/main/java/com/simiacryptus/mindseye/layers/java/ImgTileSubnetLayer.java",
"javaDoc": ""
}
}