Subreport: Logs for com.simiacryptus.ref.lang.ReferenceCountingBase
This is a network apply the following layout:
LayerTests.java:203 executed in 0.07 seconds (0.000 gc):
return Graphviz.fromGraph((Graph) TestUtil.toGraph(((DAGNetwork) layer).addRef())).height(400).width(600)
.render(Format.PNG).toImage();
executing command [/bin/sh, -c, dot -Tsvg /tmp/GraphvizJava/DotEngine1545734464234827155/dotfile.dot -ooutfile.svg]
Returns
Using Seed 5759257157705867264
LayerTests.java:241 executed in 0.00 seconds (0.000 gc):
return new GsonBuilder().setPrettyPrinting().create().toJson(
subLayer.getJson(new HashMap<>(), SerialPrecision.Double)
);
Returns
{
"class": "com.simiacryptus.mindseye.layers.cudnn.ImgTileSubnetLayer",
"id": "007d8fea-c898-4dd4-a221-89ebdbdbe110",
"isFrozen": false,
"name": "ImgTileSubnetLayer",
"inner": {
"class": "com.simiacryptus.mindseye.network.PipelineNetwork",
"id": "e9e14ecb-ae9f-4e8d-979a-d23b8a989a64",
"isFrozen": false,
"name": "PipelineNetwork",
"inputs": [
"733656a9-a066-458c-95a1-97693cc6a993"
],
"nodes": {
"508fb733-61d5-44c2-b11f-9346049fc516": "c36400d7-5567-44aa-b062-a6c07ee1dec5"
},
"layers": {
"c36400d7-5567-44aa-b062-a6c07ee1dec5": {
"class": "com.simiacryptus.mindseye.layers.cudnn.conv.SimpleConvolutionLayer",
"id": "3e1980bb-02a2-476b-9aa5-a0aa21c92a98",
"isFrozen": false,
"name": "SimpleConvolutionLayer",
"filter": [
[
[
-0.804
]
],
[
[
-0.852
]
],
[
[
0.7
]
],
[
[
1.108
]
],
[
[
1.912
]
],
[
[
1.524
]
],
[
[
-1.72
]
],
[
[
-1.688
]
],
[
[
1.764
]
]
],
"strideX": 1,
"strideY": 1,
"paddingX": 0,
"paddingY": 0,
"precision": "Double"
}
},
"links": {
"508fb733-61d5-44c2-b11f-9346049fc516": [
"733656a9-a066-458c-95a1-97693cc6a993"
]
},
"labels": {},
"head": "508fb733-61d5-44c2-b11f-9346049fc516"
},
"height": 16384,
"width": 16384,
"strideX": 16384,
"strideY": 16384,
"precision": "Double",
"parallel": false
}
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: [
[ [ 0.496, -0.608, 1.764 ] ]
]
Inputs Statistics: {meanExponent=-0.09137205448042225, negative=1, min=-0.608, max=1.764, mean=0.5506666666666667, count=3, sum=1.6520000000000001, positive=2, stdDev=0.9691361560115735, zeros=0}
Output: [
[ [ 1.354032, 2.075408, 3.28488 ] ]
]
Outputs Statistics: {meanExponent=0.3217506412105428, negative=0, min=1.354032, max=3.28488, mean=2.2381066666666665, count=3, sum=6.714319999999999, positive=3, stdDev=0.7966164455065909, 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: [
[ [ 0.496, -0.608, 1.764 ] ]
]
Value Statistics: {meanExponent=-0.09137205448042225, negative=1, min=-0.608, max=1.764, mean=0.5506666666666667, count=3, sum=1.6520000000000001, positive=2, stdDev=0.9691361560115735, zeros=0}
Implemented Feedback: [ [ -0.804, 1.108, -1.72 ], [ -0.852, 1.912, -1.688 ], [ 0.7, 1.524, 1.764 ] ]
Implemented Statistics: {meanExponent=0.09991175209538534, negative=4, min=-1.72, max=1.912, mean=0.21600000000000003, count=9, sum=1.9440000000000002, positive=5, stdDev=1.3971335735076378, zeros=0}
Measured Feedback: [ [ -0.8040000000009151, 1.1080000000029955, -1.719999999996169 ], [ -0.8520000000000749, 1.9120000000016901, -1.6879999999996897 ], [ 0.700000000000145, 1.5240000000016352, 1.7639999999996547 ] ]
Measured Statistics: {meanExponent=0.09991175209555361, negative=4, min=-1.719999999996169, max=1.9120000000016901, mean=0.21600000000103017, count=9, sum=1.9440000000092716, positive=5, stdDev=1.3971335735076553, zeros=0}
Feedback Error: [ [ -9.15045816896054E-13, 2.9953817204386723E-12, 3.830935568771565E-12 ], [ -7.494005416219807E-14, 1.6902035326893383E-12, 3.1019631308026874E-13 ], [ 1.4499512701604544E-13, 1.6351364706679306E-12, -3.452793606584237E-13 ] ]
Error Statistics: {meanExponent=-12.163492496318133, negative=3, min=-9.15045816896054E-13, max=3.830935568771565E-12, mean=1.0301759445496828E-12, count=9, sum=9.271583500947145E-12, positive=6, stdDev=1.516826511262619E-12, zeros=0}
Returns
{
"absoluteTol" : {
"count" : 9,
"sum" : 1.1942113964380496E-11,
"min" : 7.494005416219807E-14,
"max" : 3.830935568771565E-12,
"sumOfSquare" : 3.025822627798163E-23,
"standardDeviation" : 1.2654475154526254E-12,
"average" : 1.3269015515978329E-12
},
"relativeTol" : {
"count" : 9,
"sum" : 4.3501678320780025E-12,
"min" : 4.397890502476219E-14,
"max" : 1.3517065525427E-12,
"sumOfSquare" : 3.904976761072112E-24,
"standardDeviation" : 4.4750102672346216E-13,
"average" : 4.833519813420003E-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" : 9,
"sum" : 1.1942113964380496E-11,
"min" : 7.494005416219807E-14,
"max" : 3.830935568771565E-12,
"sumOfSquare" : 3.025822627798163E-23,
"standardDeviation" : 1.2654475154526254E-12,
"average" : 1.3269015515978329E-12
},
"relativeTol" : {
"count" : 9,
"sum" : 4.3501678320780025E-12,
"min" : 4.397890502476219E-14,
"max" : 1.3517065525427E-12,
"sumOfSquare" : 3.904976761072112E-24,
"standardDeviation" : 4.4750102672346216E-13,
"average" : 4.833519813420003E-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: 1.3269e-12 +- 1.2654e-12 [7.4940e-14 - 3.8309e-12] (9#)
relativeTol: 4.8335e-13 +- 4.4750e-13 [4.3979e-14 - 1.3517e-12] (9#)
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:241 executed in 0.00 seconds (0.000 gc):
return new GsonBuilder().setPrettyPrinting().create().toJson(
subLayer.getJson(new HashMap<>(), SerialPrecision.Double)
);
Returns
{
"class": "com.simiacryptus.mindseye.layers.cudnn.conv.SimpleConvolutionLayer",
"id": "212771cf-bedf-4bda-8232-4e772b57022f",
"isFrozen": false,
"name": "SimpleConvolutionLayer",
"filter": [
[
[
-0.384
]
],
[
[
0.524
]
],
[
[
-0.128
]
],
[
[
0.048
]
],
[
[
0.028
]
],
[
[
-1.028
]
],
[
[
-0.608
]
],
[
[
0.08
]
],
[
[
1.208
]
]
],
"strideX": 1,
"strideY": 1,
"paddingX": 0,
"paddingY": 0,
"precision": "Double"
}
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: [
[ [ 0.048, 1.524, 1.208 ] ]
]
Inputs Statistics: {meanExponent=-0.35123562044523937, negative=0, min=0.048, max=1.524, mean=0.9266666666666667, count=3, sum=2.7800000000000002, positive=3, stdDev=0.6345630167463451, zeros=0}
Output: [
[ [ 0.6255200000000001, -1.196848, 1.5519999999999998 ] ]
]
Outputs Statistics: {meanExponent=0.021723971814320776, negative=1, min=-1.196848, max=1.5519999999999998, mean=0.32689066666666666, count=3, sum=0.980672, positive=2, stdDev=1.1419065755193032, zeros=0}
We validate the agreement between the implemented derivative of the inputs apply finite difference estimations:
SingleDerivativeTester.java:117 executed in 0.03 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: [
[ [ 0.048, 1.524, 1.208 ] ]
]
Value Statistics: {meanExponent=-0.35123562044523937, negative=0, min=0.048, max=1.524, mean=0.9266666666666667, count=3, sum=2.7800000000000002, positive=3, stdDev=0.6345630167463451, zeros=0}
Implemented Feedback: [ [ -0.384, 0.048, -0.608 ], [ 0.524, 0.028, 0.08 ], [ -0.128, -1.028, 1.208 ] ]
Implemented Statistics: {meanExponent=-0.6310749594526688, negative=4, min=-1.028, max=1.208, mean=-0.02888888888888889, count=9, sum=-0.26, positive=5, stdDev=0.6079203976480335, zeros=0}
Measured Feedback: [ [ -0.38399999999993994, 0.047999999999159826, -0.6079999999997199 ], [ 0.5239999999995248, 0.027999999998584713, 0.08000000000008001 ], [ -0.12800000000035006, -1.028000000000695, 1.2080000000014302 ] ]
Measured Statistics: {meanExponent=-0.6310749594557561, negative=4, min=-1.028000000000695, max=1.2080000000014302, mean=-0.028888888889102817, count=9, sum=-0.26000000000192536, positive=5, stdDev=0.6079203976483837, zeros=0}
Feedback Error: [ [ 6.006306563222097E-14, -8.40175151672895E-13, 2.80109269112927E-13 ], [ -4.75175454539567E-13, -1.4152880256634859E-12, 8.000544671205034E-14 ], [ -3.5005331966431186E-13, -6.94999613415348E-13, 1.4301893003221267E-12 ] ]
Error Statistics: {meanExponent=-12.397484946442125, negative=5, min=-1.4152880256634859E-12, max=1.4301893003221267E-12, mean=-2.1392494257514252E-13, count=9, sum=-1.9253244831762828E-12, positive=4, stdDev=7.646585392027388E-13, zeros=0}
Returns
{
"absoluteTol" : {
"count" : 9,
"sum" : 5.626058646734933E-12,
"min" : 6.006306563222097E-14,
"max" : 1.4301893003221267E-12,
"sumOfSquare" : 5.674199063683E-24,
"standardDeviation" : 4.895860644733416E-13,
"average" : 6.251176274149925E-13
},
"relativeTol" : {
"count" : 9,
"sum" : 3.758422720623397E-11,
"min" : 7.820711670862717E-14,
"max" : 2.5273000458915258E-11,
"sumOfSquare" : 7.181682415283391E-22,
"standardDeviation" : 7.896662866863706E-12,
"average" : 4.176025245137108E-12
}
}
We validate the agreement between the implemented derivative of the internal weights apply finite difference estimations:
SingleDerivativeTester.java:133 executed in 0.04 seconds (0.000 gc):
return testLearning(
statistics,
component.addRef(),
RefUtil.addRef(inputPrototype),
outputPrototype.addRef());
},
outputPrototype.addRef(),
RefUtil.addRef(inputPrototype),
component.addRef()));
Learning Gradient for weight setByCoord 0
Weights: [ -0.384, 0.524, -0.128, 0.048, 0.028, -1.028, -0.608, 0.08, 1.208 ]
Implemented Gradient: [ [ 0.048, 0.0, 0.0 ], [ 1.524, 0.0, 0.0 ], [ 1.208, 0.0, 0.0 ], [ 0.0, 0.048, 0.0 ], [ 0.0, 1.524, 0.0 ], [ 0.0, 1.208, 0.0 ], [ 0.0, 0.0, 0.048 ], [ 0.0, 0.0, 1.524 ], [ 0.0, 0.0, 1.208 ] ]
Implemented Statistics: {meanExponent=-0.35123562044523937, negative=0, min=0.0, max=1.524, mean=0.3088888888888889, count=27, sum=8.34, positive=9, stdDev=0.5701298963968054, zeros=18}
Measured Gradient: [ [ 0.04800000000027005, 0.0, 0.0 ], [ 1.5239999999994147, 0.0, 0.0 ], [ 1.2079999999992097, 0.0, 0.0 ], [ 0.0, 0.047999999999159826, 0.0 ], [ 0.0, 1.5239999999994147, 0.0 ], [ 0.0, 1.2079999999992097, 0.0 ], [ 0.0, 0.0, 0.04800000000138027 ], [ 0.0, 0.0, 1.5240000000016352 ], [ 0.0, 0.0, 1.2080000000014302 ] ]
Measured Statistics: {meanExponent=-0.35123562044441625, negative=0, min=0.0, max=1.5240000000016352, mean=0.3088888888889305, count=27, sum=8.340000000001124, positive=9, stdDev=0.5701298963968197, zeros=18}
Gradient Error: [ [ 2.700478729522615E-13, 0.0, 0.0 ], [ -5.853095785823825E-13, 0.0, 0.0 ], [ -7.902567489281864E-13, 0.0, 0.0 ], [ 0.0, -8.40175151672895E-13, 0.0 ], [ 0.0, -5.853095785823825E-13, 0.0 ], [ 0.0, -7.902567489281864E-13, 0.0 ], [ 0.0, 0.0, 1.380270897577418E-12 ], [ 0.0, 0.0, 1.6351364706679306E-12 ], [ 0.0, 0.0, 1.4301893003221267E-12 ] ]
Error Statistics: {meanExponent=-12.089441095023746, negative=5, min=-8.40175151672895E-13, max=1.6351364706679306E-12, mean=4.164210128984088E-14, count=27, sum=1.1243367348257038E-12, positive=4, stdDev=5.865923855170016E-13, zeros=18}
Returns
{
"absoluteTol" : {
"count" : 36,
"sum" : 1.3933010994948702E-11,
"min" : 0.0,
"max" : 1.6351364706679306E-12,
"sumOfSquare" : 1.501146573003472E-23,
"standardDeviation" : 5.169085238101305E-13,
"average" : 3.870280831930195E-13
},
"relativeTol" : {
"count" : 18,
"sum" : 6.56935475494223E-11,
"min" : 7.820711670862717E-14,
"max" : 2.5273000458915258E-11,
"sumOfSquare" : 1.0103233425699542E-21,
"standardDeviation" : 6.5428733203309114E-12,
"average" : 3.649641530523461E-12
}
}
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: 3.8703e-13 +- 5.1691e-13 [0.0000e+00 - 1.6351e-12] (36#)
relativeTol: 3.6496e-12 +- 6.5429e-12 [7.8207e-14 - 2.5273e-11] (18#)
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:241 executed in 0.00 seconds (0.000 gc):
return new GsonBuilder().setPrettyPrinting().create().toJson(
subLayer.getJson(new HashMap<>(), SerialPrecision.Double)
);
Returns
{
"class": "com.simiacryptus.mindseye.layers.cudnn.ImgTileSubnetLayer",
"id": "c30028a1-7ed2-4a75-a1c3-5c83992c373c",
"isFrozen": false,
"name": "ImgTileSubnetLayer",
"inner": {
"class": "com.simiacryptus.mindseye.network.PipelineNetwork",
"id": "c6b2fff7-ab64-4051-98bf-4b2c90cd6df8",
"isFrozen": false,
"name": "PipelineNetwork",
"inputs": [
"5e4f9a39-c210-43f8-bb78-78ad520a856c"
],
"nodes": {
"cca21f26-5da6-427d-9fd9-6be82e56a1bf": "7f3419e9-107e-4635-b00b-186d25029aea"
},
"layers": {
"7f3419e9-107e-4635-b00b-186d25029aea": {
"class": "com.simiacryptus.mindseye.layers.cudnn.conv.SimpleConvolutionLayer",
"id": "212771cf-bedf-4bda-8232-4e772b57022f",
"isFrozen": false,
"name": "SimpleConvolutionLayer",
"filter": [
[
[
-0.384
]
],
[
[
0.524
]
],
[
[
-0.128
]
],
[
[
0.048
]
],
[
[
0.028
]
],
[
[
-1.028
]
],
[
[
-0.608
]
],
[
[
0.08
]
],
[
[
1.208
]
]
],
"strideX": 1,
"strideY": 1,
"paddingX": 0,
"paddingY": 0,
"precision": "Double"
}
},
"links": {
"cca21f26-5da6-427d-9fd9-6be82e56a1bf": [
"5e4f9a39-c210-43f8-bb78-78ad520a856c"
]
},
"labels": {},
"head": "cca21f26-5da6-427d-9fd9-6be82e56a1bf"
},
"height": 16384,
"width": 16384,
"strideX": 16384,
"strideY": 16384,
"precision": "Double",
"parallel": false
}
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: [
[ [ -1.72, -1.028, -0.384 ] ]
]
Inputs Statistics: {meanExponent=-0.056049071355221115, negative=3, min=-1.72, max=-0.384, mean=-1.044, count=3, sum=-3.132, positive=0, stdDev=0.5455370442661676, zeros=0}
Output: [
[ [ 0.17095999999999992, 0.283408, 0.49964800000000004 ] ]
]
Outputs Statistics: {meanExponent=-0.5386764107363906, negative=0, min=0.17095999999999992, max=0.49964800000000004, mean=0.3180053333333333, count=3, sum=0.954016, positive=3, stdDev=0.13639814744913356, zeros=0}
We validate the agreement between the implemented derivative of the inputs apply finite difference estimations:
SingleDerivativeTester.java:117 executed in 0.03 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: [
[ [ -1.72, -1.028, -0.384 ] ]
]
Value Statistics: {meanExponent=-0.056049071355221115, negative=3, min=-1.72, max=-0.384, mean=-1.044, count=3, sum=-3.132, positive=0, stdDev=0.5455370442661676, zeros=0}
Implemented Feedback: [ [ -0.384, 0.048, -0.608 ], [ 0.524, 0.028, 0.08 ], [ -0.128, -1.028, 1.208 ] ]
Implemented Statistics: {meanExponent=-0.6310749594526688, negative=4, min=-1.028, max=1.208, mean=-0.02888888888888889, count=9, sum=-0.26, positive=5, stdDev=0.6079203976480335, zeros=0}
Measured Feedback: [ [ -0.38399999999993994, 0.04800000000027005, -0.6079999999997199 ], [ 0.52400000000008, 0.028000000000250047, 0.08000000000008001 ], [ -0.1280000000000725, -1.0279999999995848, 1.2079999999997648 ] ]
Measured Statistics: {meanExponent=-0.631074959451942, negative=4, min=-1.0279999999995848, max=1.2079999999997648, mean=-0.02888888888876358, count=9, sum=-0.25999999999887224, positive=5, stdDev=0.6079203976478884, zeros=0}
Feedback Error: [ [ 6.006306563222097E-14, 2.700478729522615E-13, 2.80109269112927E-13 ], [ 7.993605777301127E-14, 2.5004651127424893E-13, 8.000544671205034E-14 ], [ -7.249756350802272E-14, 4.1522341120980855E-13, -2.3514523661560816E-13 ] ]
Error Statistics: {meanExponent=-12.809866639443877, negative=2, min=-2.3514523661560816E-13, max=4.1522341120980855E-13, mean=1.253098705047664E-13, count=9, sum=1.1277888345428977E-12, positive=7, stdDev=1.891340260354767E-13, zeros=0}
Returns
{
"absoluteTol" : {
"count" : 9,
"sum" : 1.7430744347901594E-12,
"min" : 6.006306563222097E-14,
"max" : 4.1522341120980855E-13,
"sumOfSquare" : 4.632681910527873E-25,
"standardDeviation" : 1.1817047918710191E-13,
"average" : 1.936749371989066E-13
},
"relativeTol" : {
"count" : 9,
"sum" : 8.745462835555176E-12,
"min" : 7.627486428721867E-14,
"max" : 4.465116272734508E-12,
"sumOfSquare" : 2.8295713663968297E-23,
"standardDeviation" : 1.4831493967007508E-12,
"average" : 9.717180928394641E-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" : 9,
"sum" : 1.7430744347901594E-12,
"min" : 6.006306563222097E-14,
"max" : 4.1522341120980855E-13,
"sumOfSquare" : 4.632681910527873E-25,
"standardDeviation" : 1.1817047918710191E-13,
"average" : 1.936749371989066E-13
},
"relativeTol" : {
"count" : 9,
"sum" : 8.745462835555176E-12,
"min" : 7.627486428721867E-14,
"max" : 4.465116272734508E-12,
"sumOfSquare" : 2.8295713663968297E-23,
"standardDeviation" : 1.4831493967007508E-12,
"average" : 9.717180928394641E-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: 1.9367e-13 +- 1.1817e-13 [6.0063e-14 - 4.1522e-13] (9#)
relativeTol: 9.7172e-13 +- 1.4831e-12 [7.6275e-14 - 4.4651e-12] (9#)
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:241 executed in 0.00 seconds (0.000 gc):
return new GsonBuilder().setPrettyPrinting().create().toJson(
subLayer.getJson(new HashMap<>(), SerialPrecision.Double)
);
Returns
{
"class": "com.simiacryptus.mindseye.layers.cudnn.conv.SimpleConvolutionLayer",
"id": "3e1980bb-02a2-476b-9aa5-a0aa21c92a98",
"isFrozen": false,
"name": "SimpleConvolutionLayer",
"filter": [
[
[
-0.804
]
],
[
[
-0.852
]
],
[
[
0.7
]
],
[
[
1.108
]
],
[
[
1.912
]
],
[
[
1.524
]
],
[
[
-1.72
]
],
[
[
-1.688
]
],
[
[
1.764
]
]
],
"strideX": 1,
"strideY": 1,
"paddingX": 0,
"paddingY": 0,
"precision": "Double"
}
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: [
[ [ -0.852, 1.912, -1.688 ] ]
]
Inputs Statistics: {meanExponent=0.1464333083321392, negative=2, min=-1.688, max=1.912, mean=-0.2093333333333333, count=3, sum=-0.6279999999999999, positive=1, stdDev=1.5383465871585058, zeros=0}
Output: [
[ [ -2.125616, 0.1392159999999995, -4.739648 ] ]
]
Outputs Statistics: {meanExponent=0.04897335027754579, negative=2, min=-4.739648, max=0.1392159999999995, mean=-2.242016, count=3, sum=-6.7260480000000005, positive=1, stdDev=1.993487764802182, zeros=0}
We validate the agreement between the implemented derivative of the inputs apply finite difference estimations:
SingleDerivativeTester.java:117 executed in 0.02 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: [
[ [ -0.852, 1.912, -1.688 ] ]
]
Value Statistics: {meanExponent=0.1464333083321392, negative=2, min=-1.688, max=1.912, mean=-0.2093333333333333, count=3, sum=-0.6279999999999999, positive=1, stdDev=1.5383465871585058, zeros=0}
Implemented Feedback: [ [ -0.804, 1.108, -1.72 ], [ -0.852, 1.912, -1.688 ], [ 0.7, 1.524, 1.764 ] ]
Implemented Statistics: {meanExponent=0.09991175209538534, negative=4, min=-1.72, max=1.912, mean=0.21600000000000003, count=9, sum=1.9440000000000002, positive=5, stdDev=1.3971335735076378, zeros=0}
Measured Feedback: [ [ -0.8039999999986946, 1.1080000000029955, -1.72000000000061 ], [ -0.8520000000000749, 1.9120000000016901, -1.6879999999996897 ], [ 0.700000000000145, 1.5239999999999698, 1.7639999999996547 ] ]
Measured Statistics: {meanExponent=0.09991175209549219, negative=4, min=-1.72000000000061, max=1.9120000000016901, mean=0.21600000000059844, count=9, sum=1.9440000000053859, positive=5, stdDev=1.3971335735079857, zeros=0}
Feedback Error: [ [ 1.305400232354259E-12, 2.9953817204386723E-12, -6.09956529729061E-13 ], [ -7.494005416219807E-14, 1.6902035326893383E-12, 3.1019631308026874E-13 ], [ 1.4499512701604544E-13, -3.019806626980426E-14, -3.452793606584237E-13 ] ]
Error Statistics: {meanExponent=-12.427634717284143, negative=4, min=-6.09956529729061E-13, max=2.9953817204386723E-12, mean=5.984225460843441E-13, count=9, sum=5.385802914759097E-12, positive=5, stdDev=1.1017774319612763E-12, zeros=0}
Returns
{
"absoluteTol" : {
"count" : 9,
"sum" : 7.506550936398071E-12,
"min" : 3.019806626980426E-14,
"max" : 2.9953817204386723E-12,
"sumOfSquare" : 1.4148207479171282E-23,
"standardDeviation" : 9.361436549028408E-13,
"average" : 8.340612151553412E-13
},
"relativeTol" : {
"count" : 9,
"sum" : 3.1300398425145637E-12,
"min" : 9.907502057022492E-15,
"max" : 1.3517065525427E-12,
"sumOfSquare" : 2.7437380494735083E-24,
"standardDeviation" : 4.288441690780685E-13,
"average" : 3.4778220472384043E-13
}
}
We validate the agreement between the implemented derivative of the internal weights apply finite difference estimations:
SingleDerivativeTester.java:133 executed in 0.03 seconds (0.000 gc):
return testLearning(
statistics,
component.addRef(),
RefUtil.addRef(inputPrototype),
outputPrototype.addRef());
},
outputPrototype.addRef(),
RefUtil.addRef(inputPrototype),
component.addRef()));
Learning Gradient for weight setByCoord 0
Weights: [ -0.804, -0.852, 0.7, 1.108, 1.912, 1.524, -1.72, -1.688, 1.764 ]
Implemented Gradient: [ [ -0.852, 0.0, 0.0 ], [ 1.912, 0.0, 0.0 ], [ -1.688, 0.0, 0.0 ], [ 0.0, -0.852, 0.0 ], [ 0.0, 1.912, 0.0 ], [ 0.0, -1.688, 0.0 ], [ 0.0, 0.0, -0.852 ], [ 0.0, 0.0, 1.912 ], [ 0.0, 0.0, -1.688 ] ]
Implemented Statistics: {meanExponent=0.1464333083321392, negative=6, min=-1.688, max=1.912, mean=-0.06977777777777779, count=27, sum=-1.8840000000000001, positive=3, stdDev=0.8936300225954534, zeros=18}
Measured Gradient: [ [ -0.8520000000000749, 0.0, 0.0 ], [ 1.9120000000016901, 0.0, 0.0 ], [ -1.6879999999996897, 0.0, 0.0 ], [ 0.0, -0.8520000000000749, 0.0 ], [ 0.0, 1.9120000000016901, 0.0 ], [ 0.0, -1.6879999999996897, 0.0 ], [ 0.0, 0.0, -0.8520000000000749 ], [ 0.0, 0.0, 1.9119999999972492 ], [ 0.0, 0.0, -1.6879999999996897 ] ]
Measured Statistics: {meanExponent=0.14643330833214122, negative=6, min=-1.6879999999996897, max=1.9120000000016901, mean=-0.06977777777772831, count=27, sum=-1.8839999999986645, positive=3, stdDev=0.89363002259545, zeros=18}
Gradient Error: [ [ -7.494005416219807E-14, 0.0, 0.0 ], [ 1.6902035326893383E-12, 0.0, 0.0 ], [ 3.1019631308026874E-13, 0.0, 0.0 ], [ 0.0, -7.494005416219807E-14, 0.0 ], [ 0.0, 1.6902035326893383E-12, 0.0 ], [ 0.0, 3.1019631308026874E-13, 0.0 ], [ 0.0, 0.0, -7.494005416219807E-14 ], [ 0.0, 0.0, -2.750688565811288E-12 ], [ 0.0, 0.0, 3.1019631308026874E-13 ] ]
Error Statistics: {meanExponent=-12.445069851664979, negative=4, min=-2.750688565811288E-12, max=1.6902035326893383E-12, mean=4.9462491715614846E-14, count=27, sum=1.3354872763216008E-12, positive=5, stdDev=7.076125827104438E-13, zeros=18}
Returns
{
"absoluteTol" : {
"count" : 36,
"sum" : 1.4793055669315436E-11,
"min" : 0.0,
"max" : 2.9953817204386723E-12,
"sumOfSquare" : 2.7733584322186556E-23,
"standardDeviation" : 7.755795023579573E-13,
"average" : 4.109182130365399E-13
},
"relativeTol" : {
"count" : 18,
"sum" : 5.140944927301413E-12,
"min" : 9.907502057022492E-15,
"max" : 1.3517065525427E-12,
"sumOfSquare" : 3.68301836526182E-24,
"standardDeviation" : 3.507708259137904E-13,
"average" : 2.856080515167452E-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: 4.1092e-13 +- 7.7558e-13 [0.0000e+00 - 2.9954e-12] (36#)
relativeTol: 2.8561e-13 +- 3.5077e-13 [9.9075e-15 - 1.3517e-12] (18#)
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:241 executed in 0.00 seconds (0.000 gc):
return new GsonBuilder().setPrettyPrinting().create().toJson(
subLayer.getJson(new HashMap<>(), SerialPrecision.Double)
);
Returns
{
"class": "com.simiacryptus.mindseye.layers.cudnn.ImgConcatLayer",
"id": "c5a25024-2f2d-47cb-86a9-2321bb9c7ac3",
"isFrozen": false,
"name": "ImgConcatLayer",
"maxBands": 6,
"precision": "Double",
"parallel": false
}
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: [
[ [ -0.804, 1.108, 0.028 ] ]
],
[
[ [ -0.712, 1.048, -1.616 ] ]
]
Inputs Statistics: {meanExponent=-0.5343487198389728, negative=1, min=-0.804, max=1.108, mean=0.11066666666666669, count=3, sum=0.3320000000000001, positive=2, stdDev=0.7827563832735928, zeros=0},
{meanExponent=0.027094210907710525, negative=2, min=-1.616, max=1.048, mean=-0.4266666666666667, count=3, sum=-1.28, positive=1, stdDev=1.1061299903517468, zeros=0}
Output: [
[ [ -0.804, 1.108, 0.028, -0.712, 1.048, -1.616 ] ]
]
Outputs Statistics: {meanExponent=-0.25362725446563117, negative=3, min=-1.616, max=1.108, mean=-0.158, count=6, sum=-0.948, positive=3, stdDev=0.9951368415114242, zeros=0}
We validate the agreement between the implemented derivative of the inputs apply finite difference estimations:
SingleDerivativeTester.java:117 executed in 0.08 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: [
[ [ -0.804, 1.108, 0.028 ] ]
]
Value Statistics: {meanExponent=-0.5343487198389728, negative=1, min=-0.804, max=1.108, mean=0.11066666666666669, count=3, sum=0.3320000000000001, positive=2, stdDev=0.7827563832735928, zeros=0}
Implemented Feedback: [ [ 1.0, 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, 1.0, 0.0, 0.0, 0.0 ] ]
Implemented Statistics: {meanExponent=0.0, negative=0, min=0.0, max=1.0, mean=0.16666666666666666, count=18, sum=3.0, positive=3, stdDev=0.37267799624996495, zeros=15}
Measured Feedback: [ [ 0.9999999999998899, 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, 0.999999999999994, 0.0, 0.0, 0.0 ] ]
Measured Statistics: {meanExponent=-3.275498961392841E-14, negative=0, min=0.0, max=0.999999999999994, mean=0.16666666666665408, count=18, sum=2.9999999999997735, positive=3, stdDev=0.37267799624993686, zeros=15}
Feedback Error: [ [ -1.1013412404281553E-13, 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, -5.995204332975845E-15, 0.0, 0.0, 0.0 ] ]
Error Statistics: {meanExponent=-13.379450735480562, negative=3, min=-1.1013412404281553E-13, max=0.0, mean=-1.2570191801033718E-14, count=18, sum=-2.262634524186069E-13, positive=0, stdDev=3.45211835430916E-14, zeros=15}
Feedback for input 1
Inputs Values: [
[ [ -0.712, 1.048, -1.616 ] ]
]
Value Statistics: {meanExponent=0.027094210907710525, negative=2, min=-1.616, max=1.048, mean=-0.4266666666666667, count=3, sum=-1.28, positive=1, stdDev=1.1061299903517468, zeros=0}
Implemented Feedback: [ [ 0.0, 0.0, 0.0, 1.0, 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, 1.0 ] ]
Implemented Statistics: {meanExponent=0.0, negative=0, min=0.0, max=1.0, mean=0.16666666666666666, count=18, sum=3.0, positive=3, stdDev=0.37267799624996495, zeros=15}
Measured Feedback: [ [ 0.0, 0.0, 0.0, 0.9999999999998899, 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, 0.9999999999998899 ] ]
Measured Statistics: {meanExponent=-4.7830642341045674E-14, negative=0, min=0.0, max=0.9999999999998899, mean=0.1666666666666483, count=18, sum=2.9999999999996696, positive=3, stdDev=0.3726779962499239, zeros=15}
Feedback Error: [ [ 0.0, 0.0, 0.0, -1.1013412404281553E-13, 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, -1.1013412404281553E-13 ] ]
Error Statistics: {meanExponent=-12.958078098036827, negative=3, min=-1.1013412404281553E-13, max=0.0, mean=-1.8355687340469256E-14, count=18, sum=-3.304023721284466E-13, positive=0, stdDev=4.104456466702158E-14, zeros=15}
Returns
{
"absoluteTol" : {
"count" : 36,
"sum" : 5.566658245470535E-13,
"min" : 0.0,
"max" : 1.1013412404281553E-13,
"sumOfSquare" : 6.068356886838552E-26,
"standardDeviation" : 3.8033566630277717E-14,
"average" : 1.5462939570751485E-14
},
"relativeTol" : {
"count" : 6,
"sum" : 2.7833291227354194E-13,
"min" : 2.9976021664879317E-15,
"max" : 5.50670620214108E-14,
"sumOfSquare" : 1.517089221709805E-26,
"standardDeviation" : 1.9405141964550633E-14,
"average" : 4.638881871225699E-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" : 36,
"sum" : 5.566658245470535E-13,
"min" : 0.0,
"max" : 1.1013412404281553E-13,
"sumOfSquare" : 6.068356886838552E-26,
"standardDeviation" : 3.8033566630277717E-14,
"average" : 1.5462939570751485E-14
},
"relativeTol" : {
"count" : 6,
"sum" : 2.7833291227354194E-13,
"min" : 2.9976021664879317E-15,
"max" : 5.50670620214108E-14,
"sumOfSquare" : 1.517089221709805E-26,
"standardDeviation" : 1.9405141964550633E-14,
"average" : 4.638881871225699E-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: 1.5463e-14 +- 3.8034e-14 [0.0000e+00 - 1.1013e-13] (36#)
relativeTol: 4.6389e-14 +- 1.9405e-14 [2.9976e-15 - 5.5067e-14] (6#)
SingleDerivativeTester.java:156 executed in 0.00 seconds (0.000 gc):
testFrozen(component.addRef(), RefUtil.addRef(inputPrototype));
testUnFrozen(component.addRef(), RefUtil.addRef(inputPrototype));
LayerTests.java:425 executed in 0.06 seconds (0.055 gc):
throwException(exceptions.addRef());
LayerBase: Conv [1/1 x 1/1, 18]+
Error
java.lang.reflect.UndeclaredThrowableException
at com.sun.proxy.$Proxy51.run(Unknown Source)
at com.simiacryptus.notebook.NotebookOutput.lambda$run$6e9b516b$1(NotebookOutput.java:105)
at com.simiacryptus.notebook.MarkdownNotebookOutput.lambda$eval$dc58be99$1(MarkdownNotebookOutput.java:657)
at com.simiacryptus.util.test.SysOutInterceptor.withOutput(SysOutInterceptor.java:102)
at com.simiacryptus.notebook.MarkdownNotebookOutput.eval(MarkdownNotebookOutput.java:649)
at com.simiacryptus.notebook.NotebookOutput.run(NotebookOutput.java:104)
at com.simiacryptus.mindseye.test.unit.LayerTests.run(LayerTests.java:425)
at com.simiacryptus.mindseye.test.LayerTestBase.derivativeTest(LayerTestBase.java:87)
at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)
at sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:62)
at sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)
at java.lang.reflect.Method.invoke(Method.java:498)
at org.junit.platform.commons.util.ReflectionUtils.invokeMethod(ReflectionUtils.java:686)
at org.junit.jupiter.engine.execution.MethodInvocation.proceed(MethodInvocation.java:60)
at org.junit.jupiter.engine.execution.InvocationInterceptorChain$ValidatingInvocation.proceed(InvocationInterceptorChain.java:131)
at org.junit.jupiter.engine.extension.TimeoutInvocation.proceed(TimeoutInvocation.java:46)
at org.junit.jupiter.engine.extension.TimeoutExtension.intercept(TimeoutExtension.java:149)
at org.junit.jupiter.engine.extension.TimeoutExtension.interceptTestableMethod(TimeoutExtension.java:140)
at org.junit.jupiter.engine.extension.TimeoutExtension.interceptTestMethod(TimeoutExtension.java:84)
at org.junit.jupiter.engine.execution.ExecutableInvoker$ReflectiveInterceptorCall.lambda$ofVoidMethod$0(ExecutableInvoker.java:115)
at org.junit.jupiter.engine.execution.ExecutableInvoker.lambda$invoke$0(ExecutableInvoker.java:105)
at org.junit.jupiter.engine.execution.InvocationInterceptorChain$InterceptedInvocation.proceed(InvocationInterceptorChain.java:106)
at org.junit.jupiter.engine.execution.InvocationInterceptorChain.proceed(InvocationInterceptorChain.java:64)
at org.junit.jupiter.engine.execution.InvocationInterceptorChain.chainAndInvoke(InvocationInterceptorChain.java:45)
at org.junit.jupiter.engine.execution.InvocationInterceptorChain.invoke(InvocationInterceptorChain.java:37)
at org.junit.jupiter.engine.execution.ExecutableInvoker.invoke(ExecutableInvoker.java:104)
at org.junit.jupiter.engine.execution.ExecutableInvoker.invoke(ExecutableInvoker.java:98)
at org.junit.jupiter.engine.descriptor.TestMethodTestDescriptor.lambda$invokeTestMethod$6(TestMethodTestDescriptor.java:205)
at org.junit.platform.engine.support.hierarchical.ThrowableCollector.execute(ThrowableCollector.java:73)
at org.junit.jupiter.engine.descriptor.TestMethodTestDescriptor.invokeTestMethod(TestMethodTestDescriptor.java:201)
at org.junit.jupiter.engine.descriptor.TestMethodTestDescriptor.execute(TestMethodTestDescriptor.java:137)
at org.junit.jupiter.engine.descriptor.TestMethodTestDescriptor.execute(TestMethodTestDescriptor.java:71)
at org.junit.platform.engine.support.hierarchical.NodeTestTask.lambda$executeRecursively$5(NodeTestTask.java:135)
at org.junit.platform.engine.support.hierarchical.ThrowableCollector.execute(ThrowableCollector.java:73)
at org.junit.platform.engine.support.hierarchical.NodeTestTask.lambda$executeRecursively$7(NodeTestTask.java:125)
at org.junit.platform.engine.support.hierarchical.Node.around(Node.java:135)
at org.junit.platform.engine.support.hierarchical.NodeTestTask.lambda$executeRecursively$8(NodeTestTask.java:123)
at org.junit.platform.engine.support.hierarchical.ThrowableCollector.execute(ThrowableCollector.java:73)
at org.junit.platform.engine.support.hierarchical.NodeTestTask.executeRecursively(NodeTestTask.java:122)
at org.junit.platform.engine.support.hierarchical.NodeTestTask.execute(NodeTestTask.java:80)
at java.util.ArrayList.forEach(ArrayList.java:1257)
at org.junit.platform.engine.support.hierarchical.SameThreadHierarchicalTestExecutorService.invokeAll(SameThreadHierarchicalTestExecutorService.java:38)
at org.junit.platform.engine.support.hierarchical.NodeTestTask.lambda$executeRecursively$5(NodeTestTask.java:139)
at org.junit.platform.engine.support.hierarchical.ThrowableCollector.execute(ThrowableCollector.java:73)
at org.junit.platform.engine.support.hierarchical.NodeTestTask.lambda$executeRecursively$7(NodeTestTask.java:125)
at org.junit.platform.engine.support.hierarchical.Node.around(Node.java:135)
at org.junit.platform.engine.support.hierarchical.NodeTestTask.lambda$executeRecursively$8(NodeTestTask.java:123)
at org.junit.platform.engine.support.hierarchical.ThrowableCollector.execute(ThrowableCollector.java:73)
at org.junit.platform.engine.support.hierarchical.NodeTestTask.executeRecursively(NodeTestTask.java:122)
at org.junit.platform.engine.support.hierarchical.NodeTestTask.execute(NodeTestTask.java:80)
at java.util.ArrayList.forEach(ArrayList.java:1257)
at org.junit.platform.engine.support.hierarchical.SameThreadHierarchicalTestExecutorService.invokeAll(SameThreadHierarchicalTestExecutorService.java:38)
at org.junit.platform.engine.support.hierarchical.NodeTestTask.lambda$executeRecursively$5(NodeTestTask.java:139)
at org.junit.platform.engine.support.hierarchical.ThrowableCollector.execute(ThrowableCollector.java:73)
at org.junit.platform.engine.support.hierarchical.NodeTestTask.lambda$executeRecursively$7(NodeTestTask.java:125)
at org.junit.platform.engine.support.hierarchical.Node.around(Node.java:135)
at org.junit.platform.engine.support.hierarchical.NodeTestTask.lambda$executeRecursively$8(NodeTestTask.java:123)
at org.junit.platform.engine.support.hierarchical.ThrowableCollector.execute(ThrowableCollector.java:73)
at org.junit.platform.engine.support.hierarchical.NodeTestTask.executeRecursively(NodeTestTask.java:122)
at org.junit.platform.engine.support.hierarchical.NodeTestTask.execute(NodeTestTask.java:80)
at org.junit.platform.engine.support.hierarchical.SameThreadHierarchicalTestExecutorService.submit(SameThreadHierarchicalTestExecutorService.java:32)
at org.junit.platform.engine.support.hierarchical.HierarchicalTestExecutor.execute(HierarchicalTestExecutor.java:57)
at org.junit.platform.engine.support.hierarchical.HierarchicalTestEngine.execute(HierarchicalTestEngine.java:51)
at org.junit.platform.launcher.core.DefaultLauncher.execute(DefaultLauncher.java:248)
at org.junit.platform.launcher.core.DefaultLauncher.lambda$execute$5(DefaultLauncher.java:211)
at org.junit.platform.launcher.core.DefaultLauncher.withInterceptedStreams(DefaultLauncher.java:226)
at org.junit.platform.launcher.core.DefaultLauncher.execute(DefaultLauncher.java:199)
at org.junit.platform.launcher.core.DefaultLauncher.execute(DefaultLauncher.java:141)
at org.junit.platform.runner.JUnitPlatform.run(JUnitPlatform.java:139)
at com.simiacryptus.util.test.MacroTestRunner.runTest(MacroTestRunner.java:94)
at com.simiacryptus.util.test.MacroTestRunner.lambda$null$8bb7732f$1(MacroTestRunner.java:177)
at com.simiacryptus.aws.TendrilControl.lambda$null$0(TendrilControl.java:86)
at java.lang.Thread.run(Thread.java:748)
Caused by: java.lang.reflect.InvocationTargetException
at sun.reflect.GeneratedMethodAccessor29.invoke(Unknown Source)
at sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)
at java.lang.reflect.Method.invoke(Method.java:498)
at com.simiacryptus.ref.lang.RefUtil$RefWrapperHandler.invoke(RefUtil.java:238)
... 73 more
Caused by: com.simiacryptus.mindseye.test.unit.TestError: Error in SingleDerivativeTester{probeSize=1.0E-4, tolerance=0.001, testFeedback=true, testLearning=true, verbose=true, verify=true} apply Conv [1/1 x 1/1, 18]+
at com.simiacryptus.mindseye.test.unit.LayerTests.run(LayerTests.java:377)
at com.simiacryptus.mindseye.test.unit.LayerTests.run(LayerTests.java:418)
... 66 more
Caused by: java.lang.reflect.UndeclaredThrowableException
at com.sun.proxy.$Proxy54.accept(Unknown Source)
at java.util.stream.Streams$RangeIntSpliterator.forEachRe
...skipping 258 bytes...
Layer.java:302)
at com.simiacryptus.mindseye.layers.cudnn.ImgConcatLayer$Accumulator.accept(ImgConcatLayer.java:266)
at com.simiacryptus.mindseye.network.CountingResult$CountingAccumulator.accum(CountingResult.java:115)
at com.simiacryptus.mindseye.network.CountingResult$CountingAccumulator.accept(CountingResult.java:108)
at com.simiacryptus.mindseye.network.CountingResult$CountingAccumulator.accept(CountingResult.java:76)
at com.simiacryptus.mindseye.lang.Result.accumulate(Result.java:136)
at com.simiacryptus.mindseye.test.SimpleEval.checkedFeedback(SimpleEval.java:171)
at com.simiacryptus.mindseye.test.SimpleEval.setResult(SimpleEval.java:87)
at com.simiacryptus.mindseye.test.SimpleEval.eval(SimpleEval.java:121)
at com.simiacryptus.mindseye.test.SimpleEval.run(SimpleEval.java:109)
at com.simiacryptus.mindseye.test.SimpleEval.run(SimpleEval.java:102)
at com.simiacryptus.mindseye.test.unit.SingleDerivativeTester.test(SingleDerivativeTester.java:95)
at com.simiacryptus.mindseye.test.unit.SingleDerivativeTester.test(SingleDerivativeTester.java:44)
at com.simiacryptus.mindseye.test.unit.LayerTests.run(LayerTests.java:369)
... 67 more
Caused by: java.lang.reflect.InvocationTargetException
at sun.reflect.GeneratedMethodAccessor24.invoke(Unknown Source)
at sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)
at java.lang.reflect.Method.invoke(Method.java:498)
at com.simiacryptus.ref.lang.RefUtil$RefWrapperHandler.invoke(RefUtil.java:238)
... 85 more
Caused by: java.lang.reflect.UndeclaredThrowableException
at com.sun.proxy.$Proxy51.run(Unknown Source)
at com.simiacryptus.util.Util.lambda$runAllParallel$4(Util.java:399)
at com.simiacryptus.ref.wrappers.RefStream.lambda$forEach$25(RefStream.java:354)
at java.util.stream.ForEachOps$ForEachOp$OfRef.accept(ForEachOps.java:183)
at java.util.Spliterators$ArraySpliterator.forEachRemaining(Spliterators.java:948)
at java.util.stream.AbstractPipeline.copyInto(AbstractPipeline.java:482)
at java.util.stream.ForEachOps$ForEachTask.compute(ForEachOps.java:290)
at java.util.concurrent.CountedCompleter.exec(CountedCompleter.java:731)
at java.util.concurrent.ForkJoinTask.doExec(ForkJoinTask.java:289)
at java.util.concurrent.ForkJoinTask.doInvoke(ForkJoinTask.java:401)
at java.util.concurrent.ForkJoinTask.invoke(ForkJoinTask.java:734)
at java.util.stream.ForEachOps$ForEachOp.evaluateParallel(ForEachOps.java:159)
at java.util.stream.ForEachOps$ForEachOp$OfRef.evaluateParallel(ForEachOps.java:173)
at java.util.stream.AbstractPipeline.evaluate(AbstractPipeline.java:233)
at java.util.stream.ReferencePipeline.forEach(ReferencePipeline.java:485)
at java.util.stream.ReferencePipeline$Head.forEach(ReferencePipeline.java:650)
at com.simiacryptus.ref.wrappers.StreamWrapper.forEach(StreamWrapper.java:129)
at com.simiacryptus.ref.wrappers.RefStream.forEach(RefStream.java:354)
at com.simiacryptus.util.Util.runAllParallel(Util.java:397)
at com.simiacryptus.mindseye.layers.cudnn.conv.SimpleConvolutionLayer$Accumulator.accept(SimpleConvolutionLayer.java:749)
at com.simiacryptus.mindseye.layers.cudnn.conv.SimpleConvolutionLayer$Accumulator.accept(SimpleConvolutionLayer.java:616)
at com.simiacryptus.mindseye.network.CountingResult$CountingAccumulator.accum(CountingResult.java:115)
at com.simiacryptus.mindseye.network.CountingResult$CountingAccumulator.accept(CountingResult.java:108)
at com.simiacryptus.mindseye.network.CountingResult$CountingAccumulator.accept(CountingResult.java:76)
at com.simiacryptus.mindseye.network.CountingResult$CountingAccumulator.accum(CountingResult.java:115)
at com.simiacryptus.mindseye.network.CountingResult$CountingAccumulator.accept(CountingResult.java:108)
at com.simiacryptus.mindseye.network.CountingResult$CountingAccumulator.accept(CountingResult.java:76)
at com.simiacryptus.mindseye.layers.cudnn.ImgConcatLayer$Accumulator.lambda$accept$2(ImgConcatLayer.java:381)
... 89 more
Caused by: java.lang.reflect.InvocationTargetException
at sun.reflect.GeneratedMethodAccessor29.invoke(Unknown Source)
at sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)
at java.lang.reflect.Method.invoke(Method.java:498)
at com.simiacryptus.ref.lang.RefUtil$RefWrapperHandler.invoke(RefUtil.java:238)
... 117 more
Caused by: java.lang.reflect.UndeclaredThrowableException
at com.sun.proxy.$Proxy56.apply(Unknown Source)
at com.simiacryptus.lang.ResourcePool.apply(ResourcePool.java:91)
at com.simiacryptus.mindseye.lang.cudnn.CudaSystem.run(CudaSystem.java:655)
at com.simiacryptus.mindseye.layers.cudnn.conv.SimpleConvolutionLayer$Accumulator.lambda$accept$3(SimpleConvolutionLayer.java:732)
... 121 more
Caused by: java.lang.reflect.InvocationTargetException
at sun.reflect.GeneratedMethodAccessor25.invoke(Unknown Source)
at sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)
at java.lang.reflect.Method.invoke(Method.java:498)
at com.simiacryptus.ref.lang.RefUtil$RefWrapperHandler.invoke(RefUtil.java:238)
... 125 more
Caused by: java.lang.reflect.UndeclaredThrowableException
at com.sun.proxy.$Proxy56.apply(Unknown Source)
at com.simiacryptus.mindseye.lang.cudnn.CudnnHandle.lambda$call$3(CudnnHandle.java:131)
at java.util.concurrent.FutureTask.run(FutureTask.java:266)
at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1149)
at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:624)
... 1 more
Caused by: java.lang.reflect.InvocationTargetException
at sun.reflect.GeneratedMethodAccessor25.invoke(Unknown Source)
at sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)
at java.lang.reflect.Method.invoke(Method.java:498)
at com.simiacryptus.ref.lang.RefUtil$RefWrapperHandler.invoke(RefUtil.java:238)
... 6 more
Caused by: java.lang.IllegalStateException: Object com.simiacryptus.mindseye.lang.cudnn.CudaTensor (0 refs; 1 adds, 0 frees) apply current stack
at com.simiacryptus.ref.lang.ReferenceCountingBase.getStackTrace(ReferenceCountingBase.java:85)
at com.simiacryptus.ref.lang.ReferenceCountingBase.referenceReport(ReferenceCountingBase.java:325)
at com.simiacryptus.ref.lang.ReferenceCountingBase.addRef(ReferenceCountingBase.java:170)
at com.simiacryptus.mindseye.lang.cudnn.CudaTensor.addRef(CudaTensor.java:266)
at com.simiacryptus.mindseye.layers.cudnn.conv.SimpleConvolutionLayer.fwd(SimpleConvolutionLayer.java:245)
at com.simiacryptus.mindseye.layers.cudnn.conv.SimpleConvolutionLayer.bck2(SimpleConvolutionLayer.java:349)
at com.simiacryptus.mindseye.layers.cudnn.conv.SimpleConvolutionLayer.access$100(SimpleConvolutionLayer.java:44)
at com.simiacryptus.mindseye.layers.cudnn.conv.SimpleConvolutionLayer$Accumulator.lambda$null$2(SimpleConvolutionLayer.java:738)
at sun.reflect.GeneratedMethodAccessor25.invoke(Unknown Source)
at sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)
at java.lang.reflect.Method.invoke(Method.java:498)
at com.simiacryptus.ref.lang.RefUtil$RefWrapperHandler.invoke(RefUtil.java:238)
at com.sun.proxy.$Proxy56.apply(Unknown Source)
at com.simiacryptus.mindseye.lang.cudnn.CudnnHandle.lambda$call$3(CudnnHandle.java:131)
at java.util.concurrent.FutureTask.run(FutureTask.java:266)
at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1149)
at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:624)
at java.lang.Thread.run(Thread.java:748)
at com.simiacryptus.ref.lang.ReferenceCountingBase.addRef(ReferenceCountingBase.java:170)
at com.simiacryptus.mindseye.lang.cudnn.CudaTensor.addRef(CudaTensor.java:266)
at com.simiacryptus.mindseye.layers.cudnn.conv.SimpleConvolutionLayer.fwd(SimpleConvolutionLayer.java:245)
at com.simiacryptus.mindseye.layers.cudnn.conv.SimpleConvolutionLayer.bck2(SimpleConvolutionLayer.java:349)
at com.simiacryptus.mindseye.layers.cudnn.conv.SimpleConvolutionLayer.access$100(SimpleConvolutionLayer.java:44)
at com.simiacryptus.mindseye.layers.cudnn.conv.SimpleConvolutionLayer$Accumulator.lambda$null$2(SimpleConvolutionLayer.java:738)
... 10 more
{
"result": "InvocationTargetException / InvocationTargetException / InvocationTargetException / InvocationTargetException / InvocationTargetException / IllegalStateException",
"performance": {
"execution_time": "1.112",
"gc_time": "0.457"
},
"created_on": 1586746033287,
"file_name": "derivativeTest",
"report": {
"simpleName": "BandExpand",
"canonicalName": "com.simiacryptus.mindseye.layers.cudnn.conv.ConvolutionLayerTest.BandExpand",
"link": "https://github.com/SimiaCryptus/mindseye-cudnn/tree/59d5b3318556370acb2d83ee6ec123ce0fc6974f/src/test/java/com/simiacryptus/mindseye/layers/cudnn/conv/ConvolutionLayerTest.java",
"javaDoc": ""
},
"archive": "s3://code.simiacrypt.us/tests/com/simiacryptus/mindseye/layers/cudnn/conv/ConvolutionLayer/BandExpand/derivativeTest/202004134713",
"id": "de4ecb7d-d6b6-4118-aac2-df85cd0747d4",
"report_type": "Components",
"display_name": "Derivative Validation",
"target": {
"simpleName": "ConvolutionLayer",
"canonicalName": "com.simiacryptus.mindseye.layers.cudnn.conv.ConvolutionLayer",
"link": "https://github.com/SimiaCryptus/mindseye-cudnn/tree/59d5b3318556370acb2d83ee6ec123ce0fc6974f/src/main/java/com/simiacryptus/mindseye/layers/cudnn/conv/ConvolutionLayer.java",
"javaDoc": ""
}
}