Subreport: Logs for com.simiacryptus.ref.lang.ReferenceCountingBase
Using Seed 6824188445141982208
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 ], [ 1.208 ], [ 1.108 ], [ 1.032 ], [ 1.612 ], [ 1.552 ], [ -0.804 ], [ -1.832 ] ],
[ [ 0.7 ], [ -1.72 ], [ 0.028 ], [ 0.3 ], [ 1.64 ], [ 1.876 ], [ 0.148 ], [ 1.368 ] ],
[ [ -0.128 ], [ -1.028 ], [ -0.712 ], [ 0.636 ], [ 0.392 ], [ -0.408 ], [ -0.032 ], [ -1.54 ] ],
[ [ 0.496 ], [ -0.384 ], [ 1.048 ], [ -0.176 ], [ 0.092 ], [ -0.384 ], [ -0.892 ], [ -0.876 ] ],
[ [ -0.608 ], [ -0.852 ], [ -1.616 ], [ 1.556 ], [ -0.556 ], [ -1.572 ], [ 1.62 ], [ 1.652 ] ],
[ [ 1.764 ], [ 1.912 ], [ 1.356 ], [ 0.788 ], [ -1.476 ], [ -1.516 ], [ -1.856 ], [ -1.424 ] ],
[ [ 0.048 ], [ -1.688 ], [ 1.512 ], [ -0.768 ], [ 1.704 ], [ -0.636 ], [ 0.996 ], [ -0.464 ] ],
[ [ 1.524 ], [ -0.804 ], [ 1.556 ], [ -0.068 ], [ -1.228 ], [ -1.492 ], [ 0.048 ], [ -0.012 ] ]
]
Inputs Statistics: {meanExponent=-0.1923523558471011, negative=32, min=-1.856, max=1.912, mean=0.059375000000000025, count=64, sum=3.8000000000000016, positive=32, stdDev=1.1518728703181615, zeros=0}
Output: [
[ [ 3.799999237060547 ] ]
]
Outputs Statistics: {meanExponent=0.5797835094219608, negative=0, min=3.799999237060547, max=3.799999237060547, mean=3.799999237060547, count=1, sum=3.799999237060547, positive=1, stdDev=0.0, zeros=0}
We validate the agreement between the implemented derivative of the inputs apply finite difference estimations:
SingleDerivativeTester.java:117 executed in 0.21 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 ], [ 1.208 ], [ 1.108 ], [ 1.032 ], [ 1.612 ], [ 1.552 ], [ -0.804 ], [ -1.832 ] ],
[ [ 0.7 ], [ -1.72 ], [ 0.028 ], [ 0.3 ], [ 1.64 ], [ 1.876 ], [ 0.148 ], [ 1.368 ] ],
[ [ -0.128 ], [ -1.028 ], [ -0.712 ], [ 0.636 ], [ 0.392 ], [ -0.408 ], [ -0.032 ], [ -1.54 ] ],
[ [ 0.496 ], [ -0.384 ], [ 1.048 ], [ -0.176 ], [ 0.092 ], [ -0.384 ], [ -0.892 ], [ -0.876 ] ],
[ [ -0.608 ], [ -0.852 ], [ -1.616 ], [ 1.556 ], [ -0.556 ], [ -1.572 ], [ 1.62 ], [ 1.652 ] ],
[ [ 1.764 ], [ 1.912 ], [ 1.356 ], [ 0.788 ], [ -1.476 ], [ -1.516 ], [ -1.856 ], [ -1.424 ] ],
[ [ 0.048 ], [ -1.688 ], [ 1.512 ], [ -0.768 ], [ 1.704 ], [ -0.636 ], [ 0.996 ], [ -0.464 ] ],
[ [ 1.524 ], [ -0.804 ], [ 1.556 ], [ -0.068 ], [ -1.228 ], [ -1.492 ], [ 0.048 ], [ -0.012 ] ]
]
Value Statistics: {meanExponent=-0.1923523558471011, negative=32, min=-1.856, max=1.912, mean=0.059375000000000025, count=64, sum=3.8000000000000016, positive=32, stdDev=1.1518728703181615, zeros=0}
Implemented Feedback: [ [ 1.0 ], [ 1.0 ], [ 1.0 ], [ 1.0 ], [ 1.0 ], [ 1.0 ], [ 1.0 ], [ 1.0 ], ... ]
Implemented Statistics: {meanExponent=0.0, negative=0, min=1.0, max=1.0, mean=1.0, count=64, sum=64.0, positive=64, stdDev=0.0, zeros=0}
Measured Feedback: [ [ 0.9999275207519531 ], [ 1.0004043579101562 ], [ 0.9999275207519531 ], [ 0.9999275207519531 ], [ 1.0001659393310547 ], [ 0.9999275207519531 ], [ 0.9999275207519531 ], [ 0.9999275207519531 ], ... ]
Measured Statistics: {meanExponent=1.3817536105557528E-5, negative=0, min=0.9999275207519531, max=1.0004043579101562, mean=1.00003182888031, count=64, sum=64.00203704833984, positive=64, stdDev=1.5699347485382466E-4, zeros=0}
Feedback Error: [ [ -7.2479248046875E-5 ], [ 4.0435791015625E-4 ], [ -7.2479248046875E-5 ], [ -7.2479248046875E-5 ], [ 1.659393310546875E-4 ], [ -7.2479248046875E-5 ], [ -7.2479248046875E-5 ], [ -7.2479248046875E-5 ], ... ]
Error Statistics: {meanExponent=-3.979863132219137, negative=42, min=-7.2479248046875E-5, max=4.0435791015625E-4, mean=3.1828880310058594E-5, count=64, sum=0.00203704833984375, positive=22, stdDev=1.5699347485382466E-4, zeros=0}
Returns
{
"absoluteTol" : {
"count" : 64,
"sum" : 0.00812530517578125,
"min" : 7.2479248046875E-5,
"max" : 4.0435791015625E-4,
"sumOfSquare" : 1.6422418411821127E-6,
"standardDeviation" : 9.768173871876216E-5,
"average" : 1.2695789337158203E-4
},
"relativeTol" : {
"count" : 64,
"sum" : 0.004062352406212477,
"min" : 3.624093738138246E-5,
"max" : 2.0213808701091164E-4,
"sumOfSquare" : 4.1044704160651777E-7,
"standardDeviation" : 4.8828821471424955E-5,
"average" : 6.347425634706995E-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" : 64,
"sum" : 0.00812530517578125,
"min" : 7.2479248046875E-5,
"max" : 4.0435791015625E-4,
"sumOfSquare" : 1.6422418411821127E-6,
"standardDeviation" : 9.768173871876216E-5,
"average" : 1.2695789337158203E-4
},
"relativeTol" : {
"count" : 64,
"sum" : 0.004062352406212477,
"min" : 3.624093738138246E-5,
"max" : 2.0213808701091164E-4,
"sumOfSquare" : 4.1044704160651777E-7,
"standardDeviation" : 4.8828821471424955E-5,
"average" : 6.347425634706995E-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: 1.2696e-04 +- 9.7682e-05 [7.2479e-05 - 4.0436e-04] (64#)
relativeTol: 6.3474e-05 +- 4.8829e-05 [3.6241e-05 - 2.0214e-04] (64#)
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=1.2696e-04 +- 9.7682e-05 [7.2479e-05 - 4.0436e-04] (64#), relativeTol=6.3474e-05 +- 4.8829e-05 [3.6241e-05 - 2.0214e-04] (64#)} | OK |
{
"result": "OK",
"performance": {
"execution_time": "0.426",
"gc_time": "0.153"
},
"created_on": 1586735136990,
"file_name": "derivativeTest",
"report": {
"simpleName": "Float",
"canonicalName": "com.simiacryptus.mindseye.layers.cudnn.SumReducerLayerTest.Float",
"link": "https://github.com/SimiaCryptus/mindseye-cudnn/tree/59d5b3318556370acb2d83ee6ec123ce0fc6974f/src/test/java/com/simiacryptus/mindseye/layers/cudnn/SumReducerLayerTest.java",
"javaDoc": ""
},
"archive": "s3://code.simiacrypt.us/tests/com/simiacryptus/mindseye/layers/cudnn/SumReducerLayer/Float/derivativeTest/202004124536",
"id": "cec7c64c-6c67-4b73-894d-3818524f4fbe",
"report_type": "Components",
"display_name": "Derivative Validation",
"target": {
"simpleName": "SumReducerLayer",
"canonicalName": "com.simiacryptus.mindseye.layers.cudnn.SumReducerLayer",
"link": "https://github.com/SimiaCryptus/mindseye-cudnn/tree/59d5b3318556370acb2d83ee6ec123ce0fc6974f/src/main/java/com/simiacryptus/mindseye/layers/cudnn/SumReducerLayer.java",
"javaDoc": ""
}
}