Subreport: Logs for com.simiacryptus.ref.lang.ReferenceCountingBase
Using Seed 7837106967572882432
Most layers, including this one, should behave the same no matter how the items are split between batches. We verify this:
BatchingTester.java:201 executed in 0.17 seconds (0.000 gc):
return test(reference == null ? null : reference.addRef(), RefUtil.addRef(inputPrototype));
Output
Derivatives
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Scalar Statistics: {meanExponent=NaN, negative=0, min=0.0, max=0.0, mean=0.0, count=64, sum=0.0, positive=0, stdDev=0.0, zeros=64}
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Scalar Statistics: {meanExponent=NaN, negative=0, min=0.0, max=0.0, mean=0.0, count=64, sum=0.0, positive=0, stdDev=0.0, zeros=64}
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Scalar Statistics: {meanExponent=NaN, negative=0, min=0.0, max=0.0, mean=0.0, count=64, sum=0.0, positive=0, stdDev=0.0, zeros=64}
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Scalar Statistics: {meanExponent=NaN, negative=0, min=0.0, max=0.0, mean=0.0, count=64, sum=0.0, positive=0, stdDev=0.0, zeros=64}
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Scalar Statistics: {meanExponent=NaN, negative=0, min=0.0, max=0.0, mean=0.0, count=64, sum=0.0, positive=0, stdDev=0.0, zeros=64}
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Scalar Statistics: {meanExponent=NaN, negative=0, min=0.0, max=0.0, mean=0.0, count=64, sum=0.0, positive=0, stdDev=0.0, zeros=64}
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Scalar Statistics: {meanExponent=NaN, negative=0, min=0.0, max=0.0, mean=0.0, count=64, sum=0.0, positive=0, stdDev=0.0, zeros=64}
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Scalar Statistics: {meanExponent=NaN, negative=0, min=0.0, max=0.0, mean=0.0, count=64, sum=0.0, positive=0, stdDev=0.0, zeros=64}
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Scalar Statistics: {meanExponent=NaN, negative=0, min=0.0, max=0.0, mean=0.0, count=64, sum=0.0, positive=0, stdDev=0.0, zeros=64}
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Scalar Statistics: {meanExponent=NaN, negative=0, min=0.0, max=0.0, mean=0.0, count=64, sum=0.0, positive=0, stdDev=0.0, zeros=64}
Returns
{
"absoluteTol" : {
"count" : 645,
"sum" : 0.0,
"min" : 0.0,
"max" : 0.0,
"sumOfSquare" : 0.0,
"standardDeviation" : 0.0,
"average" : 0.0
},
"relativeTol" : {
"count" : 643,
"sum" : 0.0,
"min" : 0.0,
"max" : 0.0,
"sumOfSquare" : 0.0,
"standardDeviation" : 0.0,
"average" : 0.0
}
}
LayerTests.java:425 executed in 0.00 seconds (0.000 gc):
throwException(exceptions.addRef());
details | result |
---|---|
ToleranceStatistics{absoluteTol=0.0000e+00 +- 0.0000e+00 [0.0000e+00 - 0.0000e+00] (645#), relativeTol=0.0000e+00 +- 0.0000e+00 [0.0000e+00 - 0.0000e+00] (643#)} | OK |
{
"result": "OK",
"performance": {
"execution_time": "0.350",
"gc_time": "0.157"
},
"created_on": 1586735514382,
"file_name": "batchingTest",
"report": {
"simpleName": "Basic",
"canonicalName": "com.simiacryptus.mindseye.layers.java.MeanSqLossLayerTest.Basic",
"link": "https://github.com/SimiaCryptus/mindseye-java/tree/93db34cedee48c0202777a2b25deddf1dfaf5731/src/test/java/com/simiacryptus/mindseye/layers/java/MeanSqLossLayerTest.java",
"javaDoc": ""
},
"archive": "s3://code.simiacrypt.us/tests/com/simiacryptus/mindseye/layers/java/MeanSqLossLayer/Basic/batchingTest/202004125154",
"id": "d0f60c17-a87c-45e7-929b-c5ec22a6248d",
"report_type": "Components",
"display_name": "Data Batching Invariance",
"target": {
"simpleName": "MeanSqLossLayer",
"canonicalName": "com.simiacryptus.mindseye.layers.java.MeanSqLossLayer",
"link": "https://github.com/SimiaCryptus/mindseye-java/tree/93db34cedee48c0202777a2b25deddf1dfaf5731/src/main/java/com/simiacryptus/mindseye/layers/java/MeanSqLossLayer.java",
"javaDoc": ""
}
}