1. Test Modules
  2. Training Characteristics
    1. Input Learning
    2. Results
  3. Results

Target Description: The type Sum meta layer.

Report Description: The type Basic.

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

Test Modules

Using Seed 5486392945449544704

Training Characteristics

Input Learning

In this apply, we use a network to learn this target input, given it's pre-evaluated output:

TrainingTester.java:445 executed in 0.00 seconds (0.000 gc):

    return RefArrays.stream(RefUtil.addRef(input_target)).flatMap(RefArrays::stream).map(x -> {
      try {
        return x.prettyPrint();
      } finally {
        x.freeRef();
      }
    }).reduce((a, b) -> a + "\n" + b).orElse("");

Returns

    [ 0.048, -0.068, -0.628, -1.484, 0.788, -0.504, 0.636, 0.148, ... ]
    [ 0.028, 1.512, 0.972, 1.156, -1.256, 1.552, 0.048, 0.092, ... ]
    [ 1.324, -0.504, -1.476, 0.016, -1.856, 1.288, -0.012, -1.492, ... ]
    [ -0.464, -1.484, 0.048, 1.912, -0.892, 1.552, -0.804, 1.916, ... ]
    [ -1.34, -2.0, -1.228, 0.3, 1.512, 0.048, -1.256, 0.184, ... ]

Results

TrainingTester.java:350 executed in 0.03 seconds (0.000 gc):

    return grid(inputLearning, modelLearning, completeLearning);

Returns

TrainingTester.java:353 executed in 0.00 seconds (0.000 gc):

    return new ComponentResult(null == inputLearning ? null : inputLearning.value,
        null == modelLearning ? null : modelLearning.value, null == completeLearning ? null : completeLearning.value);

Returns

    {"input":null, "model":null, "complete":null}

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

    throwException(exceptions.addRef());

Results

detailsresult
{"input":null, "model":null, "complete":null}OK
  {
    "result": "OK",
    "performance": {
      "execution_time": "0.195",
      "gc_time": "0.120"
    },
    "created_on": 1587006784576,
    "file_name": "trainingTest",
    "report": {
      "simpleName": "Basic",
      "canonicalName": "com.simiacryptus.mindseye.layers.java.SumMetaLayerTest.Basic",
      "link": "https://github.com/SimiaCryptus/mindseye-java/tree/c9a1867488dc7e77a975f095285b5882c0486db6/src/test/java/com/simiacryptus/mindseye/layers/java/SumMetaLayerTest.java",
      "javaDoc": "The type Basic."
    },
    "training_analysis": {},
    "archive": "s3://code.simiacrypt.us/tests/com/simiacryptus/mindseye/layers/java/SumMetaLayer/Basic/trainingTest/202004161304",
    "id": "a11d733e-74e9-469b-9ee0-f07d4dfb7e26",
    "report_type": "Components",
    "display_name": "Comparative Training",
    "target": {
      "simpleName": "SumMetaLayer",
      "canonicalName": "com.simiacryptus.mindseye.layers.java.SumMetaLayer",
      "link": "https://github.com/SimiaCryptus/mindseye-java/tree/c9a1867488dc7e77a975f095285b5882c0486db6/src/main/java/com/simiacryptus/mindseye/layers/java/SumMetaLayer.java",
      "javaDoc": "The type Sum meta layer."
    }
  }