Subreport: Logs for com.simiacryptus.ref.lang.ReferenceCountingBase
Using Seed 6548869208208993280
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 ] ]
],
[
[ [ -0.608 ], [ 0.048 ] ],
[ [ 1.764 ], [ 1.524 ] ]
],
[
[ [ 1.208 ], [ -1.028 ] ],
[ [ -1.72 ], [ -0.384 ] ]
],
[
[ [ -0.852 ], [ -1.688 ] ],
[ [ 1.912 ], [ -0.804 ] ]
],
[
[ [ 1.108 ], [ -0.712 ] ],
[ [ 0.028 ], [ 1.048 ] ]
]
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},
{meanExponent=-0.27634290888807383, negative=1, min=-0.608, max=1.764, mean=0.682, count=4, sum=2.728, positive=3, stdDev=0.993194844932252, zeros=0},
{meanExponent=-0.021520069945137582, negative=3, min=-1.72, max=1.208, mean=-0.481, count=4, sum=-1.924, positive=1, stdDev=1.083565872478457, zeros=0},
{meanExponent=0.08613899343621721, negative=3, min=-1.688, max=1.912, mean=-0.358, count=4, sum=-1.432, positive=1, stdDev=1.35690382857445, zeros=0},
{meanExponent=-0.40886523299520144, negative=1, min=-0.712, max=1.108, mean=0.36800000000000005, count=4, sum=1.4720000000000002, positive=3, stdDev=0.7569676347110225, zeros=0}
Output: [
[ [ 0.9359999999999999 ], [ -3.508 ] ],
[ [ 2.684 ], [ 1.88 ] ]
]
Outputs Statistics: {meanExponent=0.3048189485481856, negative=1, min=-3.508, max=2.684, mean=0.498, count=4, sum=1.992, positive=3, stdDev=2.3941804443274526, zeros=0}
We validate the agreement between the implemented derivative of the inputs apply finite difference estimations:
SingleDerivativeTester.java:117 executed in 0.24 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: [ [ 1.0, 0.0, 0.0, 0.0 ], [ 0.0, 1.0, 0.0, 0.0 ], [ 0.0, 0.0, 1.0, 0.0 ], [ 0.0, 0.0, 0.0, 1.0 ] ]
Implemented Statistics: {meanExponent=0.0, negative=0, min=0.0, max=1.0, mean=0.25, count=16, sum=4.0, positive=4, stdDev=0.4330127018922193, zeros=12}
Measured Feedback: [ [ 0.9999999999998899, 0.0, 0.0, 0.0 ], [ 0.0, 0.9999999999976694, 0.0, 0.0 ], [ 0.0, 0.0, 1.0000000000021103, 0.0 ], [ 0.0, 0.0, 0.0, 1.0000000000021103 ] ]
Measured Statistics: {meanExponent=1.932512242964997E-13, negative=0, min=0.0, max=1.0000000000021103, mean=0.25000000000011124, count=16, sum=4.00000000000178, positive=4, stdDev=0.433012701892412, zeros=12}
Feedback Error: [ [ -1.1013412404281553E-13, 0.0, 0.0, 0.0 ], [ 0.0, -2.3305801732931286E-12, 0.0, 0.0 ], [ 0.0, 0.0, 2.1103119252074976E-12, 0.0 ], [ 0.0, 0.0, 0.0, 2.1103119252074976E-12 ] ]
Error Statistics: {meanExponent=-11.985480186078018, negative=2, min=-2.3305801732931286E-12, max=2.1103119252074976E-12, mean=1.1124434706744069E-13, count=16, sum=1.779909553079051E-12, positive=2, stdDev=9.404972566646684E-13, zeros=12}
Feedback for input 1
Inputs Values: [
[ [ -0.608 ], [ 0.048 ] ],
[ [ 1.764 ], [ 1.524 ] ]
]
Value Statistics: {meanExponent=-0.27634290888807383, negative=1, min=-0.608, max=1.764, mean=0.682, count=4, sum=2.728, positive=3, stdDev=0.993194844932252, zeros=0}
Implemented Feedback: [ [ 1.0, 0.0, 0.0, 0.0 ], [ 0.0, 1.0, 0.0, 0.0 ], [ 0.0, 0.0, 1.0, 0.0 ], [ 0.0, 0.0, 0.0, 1.0 ] ]
Implemented Statistics: {meanExponent=0.0, negative=0, min=0.0, max=1.0, mean=0.25, count=16, sum=4.0, positive=4, stdDev=0.4330127018922193, zeros=12}
Measured Feedback: [ [ 0.9999999999998899, 0.0, 0.0, 0.0 ], [ 0.0, 0.9999999999976694, 0.0, 0.0 ], [ 0.0, 0.0, 1.0000000000021103, 0.0 ], [ 0.0, 0.0, 0.0, 1.0000000000021103 ] ]
Measured Statistics: {meanExponent=1.932512242964997E-13, negative=0, min=0.0, max=1.0000000000021103, mean=0.25000000000011124, count=16, sum=4.00000000000178, positive=4, stdDev=0.433012701892412, zeros=12}
Feedback Error: [ [ -1.1013412404281553E-13, 0.0, 0.0, 0.0 ], [ 0.0, -2.3305801732931286E-12, 0.0, 0.0 ], [ 0.0, 0.0, 2.1103119252074976E-12, 0.0 ], [ 0.0, 0.0, 0.0, 2.1103119252074976E-12 ] ]
Error Statistics: {meanExponent=-11.985480186078018, negative=2, min=-2.3305801732931286E-12, max=2.1103119252074976E-12, mean=1.1124434706744069E-13, count=16, sum=1.779909553079051E-12, positive=2, stdDev=9.404972566646684E-13, zeros=12}
Feedback for input 2
Inputs Values: [
[ [ 1.208 ], [ -1.028 ] ],
[ [ -1.72 ], [ -0.384 ] ]
]
Value Statistics: {meanExponent=-0.021520069945137582, negative=3, min=-1.72, max=1.208, mean=-0.481, count=4, sum=-1.924, positive=1, stdDev=1.083565872478457, zeros=0}
Implemented Feedback: [ [ 1.0, 0.0, 0.0, 0.0 ], [ 0.0, 1.0, 0.0, 0.0 ], [ 0.0, 0.0, 1.0, 0.0 ], [ 0.0, 0.0, 0.0, 1.0 ] ]
Implemented Statistics: {meanExponent=0.0, negative=0, min=0.0, max=1.0, mean=0.25, count=16, sum=4.0, positive=4, stdDev=0.4330127018922193, zeros=12}
Measured Feedback: [ [ 1.0000000000021103, 0.0, 0.0, 0.0 ], [ 0.0, 0.9999999999976694, 0.0, 0.0 ], [ 0.0, 0.0, 0.9999999999976694, 0.0 ], [ 0.0, 0.0, 0.0, 1.0000000000021103 ] ]
Measured Statistics: {meanExponent=-4.783064234211626E-14, negative=0, min=0.0, max=1.0000000000021103, mean=0.24999999999997247, count=16, sum=3.9999999999995595, positive=4, stdDev=0.4330127018921716, zeros=12}
Feedback Error: [ [ 2.1103119252074976E-12, 0.0, 0.0, 0.0 ], [ 0.0, -2.3305801732931286E-12, 0.0, 0.0 ], [ 0.0, 0.0, -2.3305801732931286E-12, 0.0 ], [ 0.0, 0.0, 0.0, 2.1103119252074976E-12 ] ]
Error Statistics: {meanExponent=-11.65409464969267, negative=2, min=-2.3305801732931286E-12, max=2.1103119252074976E-12, mean=-2.7533531010703882E-14, count=16, sum=-4.405364961712621E-13, positive=2, stdDev=1.1112467999493107E-12, zeros=12}
Feedback for input 3
Inputs Values: [
[ [ -0.852 ], [ -1.688 ] ],
[ [ 1.912 ], [ -0.804 ] ]
]
Value Statistics: {meanExponent=0.08613899343621721, negative=3, min=-1.688, max=1.912, mean=-0.358, count=4, sum=-1.432, positive=1, stdDev=1.35690382857445, zeros=0}
Implemented Feedback: [ [ 1.0, 0.0, 0.0, 0.0 ], [ 0.0, 1.0, 0.0, 0.0 ], [ 0.0, 0.0, 1.0, 0.0 ], [ 0.0, 0.0, 0.0, 1.0 ] ]
Implemented Statistics: {meanExponent=0.0, negative=0, min=0.0, max=1.0, mean=0.25, count=16, sum=4.0, positive=4, stdDev=0.4330127018922193, zeros=12}
Measured Feedback: [ [ 1.0000000000021103, 0.0, 0.0, 0.0 ], [ 0.0, 0.9999999999976694, 0.0, 0.0 ], [ 0.0, 0.0, 1.0000000000021103, 0.0 ], [ 0.0, 0.0, 0.0, 1.0000000000021103 ] ]
Measured Statistics: {meanExponent=4.3433309093458037E-13, negative=0, min=0.0, max=1.0000000000021103, mean=0.25000000000025, count=16, sum=4.000000000004, positive=4, stdDev=0.4330127018926524, zeros=12}
Feedback Error: [ [ 2.1103119252074976E-12, 0.0, 0.0, 0.0 ], [ 0.0, -2.3305801732931286E-12, 0.0, 0.0 ], [ 0.0, 0.0, 2.1103119252074976E-12, 0.0 ], [ 0.0, 0.0, 0.0, 2.1103119252074976E-12 ] ]
Error Statistics: {meanExponent=-11.664873998291286, negative=1, min=-2.3305801732931286E-12, max=2.1103119252074976E-12, mean=2.5002222514558525E-13, count=16, sum=4.000355602329364E-12, positive=3, stdDev=1.0545044867169235E-12, zeros=12}
Feedback for input 4
Inputs Values: [
[ [ 1.108 ], [ -0.712 ] ],
[ [ 0.028 ], [ 1.048 ] ]
]
Value Statistics: {meanExponent=-0.40886523299520144, negative=1, min=-0.712, max=1.108, mean=0.36800000000000005, count=4, sum=1.4720000000000002, positive=3, stdDev=0.7569676347110225, zeros=0}
Implemented Feedback: [ [ 1.0, 0.0, 0.0, 0.0 ], [ 0.0, 1.0, 0.0, 0.0 ], [ 0.0, 0.0, 1.0, 0.0 ], [ 0.0, 0.0, 0.0, 1.0 ] ]
Implemented Statistics: {meanExponent=0.0, negative=0, min=0.0, max=1.0, mean=0.25, count=16, sum=4.0, positive=4, stdDev=0.4330127018922193, zeros=12}
Measured Feedback: [ [ 1.0000000000021103, 0.0, 0.0, 0.0 ], [ 0.0, 0.9999999999976694, 0.0, 0.0 ], [ 0.0, 0.0, 1.0000000000021103, 0.0 ], [ 0.0, 0.0, 0.0, 1.0000000000021103 ] ]
Measured Statistics: {meanExponent=4.3433309093458037E-13, negative=0, min=0.0, max=1.0000000000021103, mean=0.25000000000025, count=16, sum=4.000000000004, positive=4, stdDev=0.4330127018926524, zeros=12}
Feedback Error: [ [ 2.1103119252074976E-12, 0.0, 0.0, 0.0 ], [ 0.0, -2.3305801732931286E-12, 0.0, 0.0 ], [ 0.0, 0.0, 2.1103119252074976E-12, 0.0 ], [ 0.0, 0.0, 0.0, 2.1103119252074976E-12 ] ]
Error Statistics: {meanExponent=-11.664873998291286, negative=1, min=-2.3305801732931286E-12, max=2.1103119252074976E-12, mean=2.5002222514558525E-13, count=16, sum=4.000355602329364E-12, positive=3, stdDev=1.0545044867169235E-12, zeros=12}
Returns
{
"absoluteTol" : {
"count" : 80,
"sum" : 3.9527492390334373E-11,
"min" : 0.0,
"max" : 2.3305801732931286E-12,
"sumOfSquare" : 8.605487977551523E-23,
"standardDeviation" : 9.118977231038986E-13,
"average" : 4.940936548791796E-13
},
"relativeTol" : {
"count" : 20,
"sum" : 1.9763746195161978E-11,
"min" : 5.50670620214108E-14,
"max" : 1.1652900866479222E-12,
"sumOfSquare" : 2.1513719943869603E-23,
"standardDeviation" : 3.1491560460944313E-13,
"average" : 9.88187309758099E-13
}
}
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" : 80,
"sum" : 3.9527492390334373E-11,
"min" : 0.0,
"max" : 2.3305801732931286E-12,
"sumOfSquare" : 8.605487977551523E-23,
"standardDeviation" : 9.118977231038986E-13,
"average" : 4.940936548791796E-13
},
"relativeTol" : {
"count" : 20,
"sum" : 1.9763746195161978E-11,
"min" : 5.50670620214108E-14,
"max" : 1.1652900866479222E-12,
"sumOfSquare" : 2.1513719943869603E-23,
"standardDeviation" : 3.1491560460944313E-13,
"average" : 9.88187309758099E-13
}
}
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: 4.9409e-13 +- 9.1190e-13 [0.0000e+00 - 2.3306e-12] (80#)
relativeTol: 9.8819e-13 +- 3.1492e-13 [5.5067e-14 - 1.1653e-12] (20#)
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=4.9409e-13 +- 9.1190e-13 [0.0000e+00 - 2.3306e-12] (80#), relativeTol=9.8819e-13 +- 3.1492e-13 [5.5067e-14 - 1.1653e-12] (20#)} | OK |
{
"result": "OK",
"performance": {
"execution_time": "0.480",
"gc_time": "0.179"
},
"created_on": 1586741364131,
"file_name": "derivativeTest",
"report": {
"simpleName": "Double_List",
"canonicalName": "com.simiacryptus.mindseye.layers.cudnn.SumInputsLayerTest.Double_List",
"link": "https://github.com/SimiaCryptus/mindseye-cudnn/tree/59d5b3318556370acb2d83ee6ec123ce0fc6974f/src/test/java/com/simiacryptus/mindseye/layers/cudnn/SumInputsLayerTest.java",
"javaDoc": ""
},
"archive": "s3://code.simiacrypt.us/tests/com/simiacryptus/mindseye/layers/cudnn/SumInputsLayer/Double_List/derivativeTest/202004132924",
"id": "7719e7b1-f077-4027-be2b-cc170cd24ab1",
"report_type": "Components",
"display_name": "Derivative Validation",
"target": {
"simpleName": "SumInputsLayer",
"canonicalName": "com.simiacryptus.mindseye.layers.cudnn.SumInputsLayer",
"link": "https://github.com/SimiaCryptus/mindseye-cudnn/tree/59d5b3318556370acb2d83ee6ec123ce0fc6974f/src/main/java/com/simiacryptus/mindseye/layers/cudnn/SumInputsLayer.java",
"javaDoc": ""
}
}