1. Test Modules
  2. Batch Execution
  3. Results

Target Description: The type Gaussian activation layer.

Report Description: The type Basic.

Subreport: Logs for com.simiacryptus.ref.lang.ReferenceCountingBase

Test Modules

Using Seed 3213673459361551360

Batch Execution

Most layers, including this one, should behave the same no matter how the items are split between batches. We verify this:

BatchingTester.java:232 executed in 0.29 seconds (0.000 gc):

    return test(reference == null ? null : reference.addRef(), RefUtil.addRef(inputPrototype));
Logging
Output
Derivatives
Error: [
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...
]
Scalar Statistics: {meanExponent=NaN, negative=0, min=0.0, max=0.0, mean=0.0, count=10000, sum=0.0, positive=0, stdDev=0.0, zeros=10000}
Error: [
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...
]
Scalar Statistics: {meanExponent=NaN, negative=0, min=0.0, max=0.0, mean=0.0, count=10000, sum=0.0, positive=0, stdDev=0.0, zeros=10000}
Error: [
[ [ 0.0 ], [ 0.0 ], [ 0.0 ], [ 0.0 ], [ 0.0 ], [ 0.0 ], [ 0.0 ], [ 0.0 ], ... ],
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...
]
Scalar Statistics: {meanExponent=NaN, negative=0, min=0.0, max=0.0, mean=0.0, count=10000, sum=0.0, positive=0, stdDev=0.0, zeros=10000}
Error: [
[ [ 0.0 ], [ 0.0 ], [ 0.0 ], [ 0.0 ], [ 0.0 ], [ 0.0 ], [ 0.0 ], [ 0.0 ], ... ],
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...
]
Scalar Statistics: {meanExponent=NaN, negative=0, min=0.0, max=0.0, mean=0.0, count=10000, sum=0.0, positive=0, stdDev=0.0, zeros=10000}
Error: [
[ [ 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 ], ... ],
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[ [ 0.0 ], [ 0.0 ], [ 0.0 ], [ 0.0 ], [ 0.0 ], [ 0.0 ], [ 0.0 ], [ 0.0 ], ... ],
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...
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Scalar Statistics: {meanExponent=NaN, negative=0, min=0.0, max=0.0, mean=0.0, count=10000, sum=0.0, positive=0, stdDev=0.0, zeros=10000}

Returns

    {
      "absoluteTol" : {
        "count" : 100000,
        "sum" : 0.0,
        "min" : 0.0,
        "max" : 0.0,
        "sumOfSquare" : 0.0,
        "standardDeviation" : 0.0,
        "average" : 0.0
      },
      "relativeTol" : {
        "count" : 99945,
        "sum" : 0.0,
        "min" : 0.0,
        "max" : 0.0,
        "sumOfSquare" : 0.0,
        "standardDeviation" : 0.0,
        "average" : 0.0
      }
    }

LayerTests.java:605 executed in 0.00 seconds (0.000 gc):

    throwException(exceptions.addRef());

Results

detailsresult
ToleranceStatistics{absoluteTol=0.0000e+00 +- 0.0000e+00 [0.0000e+00 - 0.0000e+00] (100000#), relativeTol=0.0000e+00 +- 0.0000e+00 [0.0000e+00 - 0.0000e+00] (99945#)}OK
  {
    "result": "OK",
    "performance": {
      "execution_time": "0.482",
      "gc_time": "0.165"
    },
    "created_on": 1587005188081,
    "file_name": "batchingTest",
    "report": {
      "simpleName": "Basic",
      "canonicalName": "com.simiacryptus.mindseye.layers.java.GaussianActivationLayerTest.Basic",
      "link": "https://github.com/SimiaCryptus/mindseye-java/tree/c9a1867488dc7e77a975f095285b5882c0486db6/src/test/java/com/simiacryptus/mindseye/layers/java/GaussianActivationLayerTest.java",
      "javaDoc": "The type Basic."
    },
    "archive": "s3://code.simiacrypt.us/tests/com/simiacryptus/mindseye/layers/java/GaussianActivationLayer/Basic/batchingTest/202004164628",
    "id": "cd33cbf2-eb33-4fef-9743-e9ffba26d11a",
    "report_type": "Components",
    "display_name": "Data Batching Invariance",
    "target": {
      "simpleName": "GaussianActivationLayer",
      "canonicalName": "com.simiacryptus.mindseye.layers.java.GaussianActivationLayer",
      "link": "https://github.com/SimiaCryptus/mindseye-java/tree/c9a1867488dc7e77a975f095285b5882c0486db6/src/main/java/com/simiacryptus/mindseye/layers/java/GaussianActivationLayer.java",
      "javaDoc": "The type Gaussian activation layer."
    }
  }