Subreport: Logs for com.simiacryptus.ref.lang.ReferenceCountingBase
Using Seed 5629146939315975168
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 ], [ -0.128, 0.048, -1.028, -1.688 ] ],
[ [ 0.7, 1.764, -1.72, 1.912 ], [ 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: [
[ [ 0.08 ], [ -0.608 ], [ -0.128 ], [ 0.048 ] ],
[ [ 1.208 ], [ -0.852 ], [ -1.028 ], [ -1.688 ] ],
[ [ 0.7 ], [ 1.764 ], [ 0.496 ], [ 1.524 ] ],
[ [ -1.72 ], [ 1.912 ], [ -0.384 ], [ -0.804 ] ]
]
Outputs Statistics: {meanExponent=-0.20600101677773192, negative=8, min=-1.72, max=1.912, mean=0.0325, count=16, sum=0.52, positive=8, stdDev=1.119359526693725, zeros=0}
We validate the agreement between the implemented derivative of the inputs apply finite difference estimations:
SingleDerivativeTester.java:117 executed in 0.04 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 ], [ -0.128, 0.048, -1.028, -1.688 ] ],
[ [ 0.7, 1.764, -1.72, 1.912 ], [ 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: [ [ 1.0, 0.0, 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, 0.0, 0.0, 0.0, 0.0, 0.0, ... ], [ 0.0, 0.0, 0.0, 0.0, 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, 0.0, 1.0, 0.0, ... ], [ 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ... ], [ 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ... ], ... ]
Implemented Statistics: {meanExponent=0.0, negative=0, min=0.0, max=1.0, mean=0.0625, count=256, sum=16.0, positive=16, stdDev=0.24206145913796356, zeros=240}
Measured Feedback: [ [ 1.0000000000000286, 0.0, 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, 0.0, 0.0, 0.0, 0.0, 0.0, ... ], [ 0.0, 0.0, 0.0, 0.0, 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, 0.0, 0.9999999999998899, 0.0, ... ], [ 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ... ], [ 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ... ], ... ]
Measured Statistics: {meanExponent=-4.02968340085978E-14, negative=0, min=0.0, max=1.0000000000000286, mean=0.0624999999999942, count=256, sum=15.999999999998515, positive=16, stdDev=0.24206145913794108, zeros=240}
Feedback Error: [ [ 2.864375403532904E-14, 0.0, 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, 0.0, 0.0, 0.0, 0.0, 0.0, ... ], [ 0.0, 0.0, 0.0, 0.0, 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, 0.0, -1.1013412404281553E-13, 0.0, ... ], [ 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ... ], [ 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ... ], ... ]
Error Statistics: {meanExponent=-13.031189593810693, negative=14, min=-1.1013412404281553E-13, max=2.864375403532904E-14, mean=-5.799180580190466E-15, count=256, sum=-1.4845902285287593E-12, positive=2, stdDev=2.5221276698790836E-14, zeros=240}
Returns
{
"absoluteTol" : {
"count" : 256,
"sum" : 1.5991652446700755E-12,
"min" : 0.0,
"max" : 1.1013412404281553E-13,
"sumOfSquare" : 1.7145428319196874E-25,
"standardDeviation" : 2.5114170155982587E-14,
"average" : 6.246739236992482E-15
},
"relativeTol" : {
"count" : 16,
"sum" : 7.995826223350798E-13,
"min" : 1.4321877017664317E-14,
"max" : 5.50670620214108E-14,
"sumOfSquare" : 4.286357079799686E-26,
"standardDeviation" : 1.347520333040395E-14,
"average" : 4.997391389594249E-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" : 256,
"sum" : 1.5991652446700755E-12,
"min" : 0.0,
"max" : 1.1013412404281553E-13,
"sumOfSquare" : 1.7145428319196874E-25,
"standardDeviation" : 2.5114170155982587E-14,
"average" : 6.246739236992482E-15
},
"relativeTol" : {
"count" : 16,
"sum" : 7.995826223350798E-13,
"min" : 1.4321877017664317E-14,
"max" : 5.50670620214108E-14,
"sumOfSquare" : 4.286357079799686E-26,
"standardDeviation" : 1.347520333040395E-14,
"average" : 4.997391389594249E-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: 6.2467e-15 +- 2.5114e-14 [0.0000e+00 - 1.1013e-13] (256#)
relativeTol: 4.9974e-14 +- 1.3475e-14 [1.4322e-14 - 5.5067e-14] (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=6.2467e-15 +- 2.5114e-14 [0.0000e+00 - 1.1013e-13] (256#), relativeTol=4.9974e-14 +- 1.3475e-14 [1.4322e-14 - 5.5067e-14] (16#)} | OK |
{
"result": "OK",
"performance": {
"execution_time": "0.166",
"gc_time": "0.086"
},
"created_on": 1586735014676,
"file_name": "derivativeTest",
"report": {
"simpleName": "Expand",
"canonicalName": "com.simiacryptus.mindseye.layers.java.ImgReshapeLayerTest.Expand",
"link": "https://github.com/SimiaCryptus/mindseye-java/tree/93db34cedee48c0202777a2b25deddf1dfaf5731/src/test/java/com/simiacryptus/mindseye/layers/java/ImgReshapeLayerTest.java",
"javaDoc": ""
},
"archive": "s3://code.simiacrypt.us/tests/com/simiacryptus/mindseye/layers/java/ImgReshapeLayer/Expand/derivativeTest/202004124334",
"id": "44ed4b87-f43a-4825-8594-035c877f31ff",
"report_type": "Components",
"display_name": "Derivative Validation",
"target": {
"simpleName": "ImgReshapeLayer",
"canonicalName": "com.simiacryptus.mindseye.layers.java.ImgReshapeLayer",
"link": "https://github.com/SimiaCryptus/mindseye-java/tree/93db34cedee48c0202777a2b25deddf1dfaf5731/src/main/java/com/simiacryptus/mindseye/layers/java/ImgReshapeLayer.java",
"javaDoc": ""
}
}