Subreport: Logs for com.simiacryptus.ref.lang.ReferenceCountingBase
This is a network apply the following layout:
LayerTests.java:203 executed in 0.10 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/DotEngine4885040116684728239/dotfile.dot -ooutfile.svg]
Returns
Using Seed 6118410167014930432
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 3.98 seconds (0.186 gc):
return test(reference == null ? null : reference.addRef(), RefUtil.addRef(inputPrototype));
BACKPROP_AGG_SIZE = 3
THREADS = 64
SINGLE_THREADED = false
Initialized CoreSettings = {
"backpropAggregationSize" : 3,
"jvmThreads" : 64,
"singleThreaded" : false
}
Output
Derivatives
Error: [
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...
]
Scalar Statistics: {meanExponent=NaN, negative=0, min=0.0, max=0.0, mean=0.0, count=2520000, sum=0.0, positive=0, stdDev=0.0, zeros=2520000}
Error: [
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...
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Scalar Statistics: {meanExponent=NaN, negative=0, min=0.0, max=0.0, mean=0.0, count=2520000, sum=0.0, positive=0, stdDev=0.0, zeros=2520000}
Returns
{
"absoluteTol" : {
"count" : 8640000,
"sum" : 0.0,
"min" : 0.0,
"max" : 0.0,
"sumOfSquare" : 0.0,
"standardDeviation" : 0.0,
"average" : 0.0
},
"relativeTol" : {
"count" : 8640000,
"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] (8640000#), relativeTol=0.0000e+00 +- 0.0000e+00 [0.0000e+00 - 0.0000e+00] (8640000#)} | OK |
{
"result": "OK",
"performance": {
"execution_time": "4.487",
"gc_time": "0.380"
},
"created_on": 1586747718444,
"file_name": "batchingTest",
"report": {
"simpleName": "IrregularTest_Float",
"canonicalName": "com.simiacryptus.mindseye.layers.cudnn.conv.ConvolutionLayerTest.IrregularTest_Float",
"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/IrregularTest_Float/batchingTest/202004131518",
"id": "1023e370-8715-4866-b632-8a6cd0847ae9",
"report_type": "Components",
"display_name": "Data Batching Invariance",
"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": ""
}
}