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Stabilize the LR test #4446

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Nov 11, 2019
Merged

Stabilize the LR test #4446

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217c1af
Stabilize the LR test
Nov 7, 2019
b8d7b23
removed rand to use existing seed.
Nov 8, 2019
3a30c55
adjust for nix
Nov 8, 2019
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4 changes: 2 additions & 2 deletions src/Microsoft.ML.Vision/ImageClassificationTrainer.cs
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Original file line number Diff line number Diff line change
Expand Up @@ -937,7 +937,7 @@ private void TrainAndEvaluateClassificationLayer(string trainBottleneckFilePath,
metrics.Train.LearningRate = learningRate;
// Update train state.
trainstate.CurrentEpoch = epoch;
using (var cursor = trainingSet.GetRowCursor(trainingSet.Schema.ToArray(), new Random()))
using (var cursor = trainingSet.GetRowCursor(trainingSet.Schema.ToArray(), Host.Rand))
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@codemzs codemzs Nov 8, 2019 •
edited by bpstark
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trainingSet.GetRowCursor(trainingSet.Schema.ToArray(), Host.Rand)) [](start = 36, length = 66)

You don't need to pass anything, look at the code for ShuffleRowTransformer:

        if (_forceShuffle || _forceShuffleSource)
            _forceShuffleSeed = options.ForceShuffleSeed ?? Host.Rand.NextSigned(); #Resolved

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While you are correct that if we do not pass anything then it should still be stable, RowShuffling uses host to create a seed to then create a new random, this seems unnecessary, as we would be creating a new object when we can just use an existing random which is more efficient.

In reply to: 344297118 [](ancestors = 344297118)

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@codemzs codemzs Nov 8, 2019 •
edited by bpstark
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I discussed offline with @bpstark and we agreed that we should not be passing in Host.Rand object (as I suggested earlier) because it is not thread safe. As I explained, the shuffling transformer code will create a seed from Host.Rand object (that it has access internally) to create an local/internal Rand object and this will ensure stability and also remove concerns around thread safety. #Resolved

{
var labelGetter = cursor.GetGetter<long>(trainingSet.Schema[0]);
var featuresGetter = cursor.GetGetter<VBuffer<float>>(featureColumn);
Expand Down Expand Up @@ -1069,7 +1069,7 @@ private void TrainAndEvaluateClassificationLayer(string trainBottleneckFilePath,
metrics.Train.BatchProcessedCount = 0;
metrics.Train.Accuracy = 0;
metrics.Train.CrossEntropy = 0;
using (var cursor = validationSet.GetRowCursor(validationSet.Schema.ToArray(), new Random()))
using (var cursor = validationSet.GetRowCursor(validationSet.Schema.ToArray(), Host.Rand))
{
var labelGetter = cursor.GetGetter<long>(validationSet.Schema[0]);
var featuresGetter = cursor.GetGetter<VBuffer<float>>(featureColumn);
Expand Down
2 changes: 1 addition & 1 deletion test/Microsoft.ML.AutoML.Tests/AutoFitTests.cs
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Expand Up @@ -52,7 +52,7 @@ public void AutoFitMultiTest()
[TensorFlowFact]
public void AutoFitImageClassificationTrainTest()
{
var context = new MLContext();
var context = new MLContext(seed: 1);
var datasetPath = DatasetUtil.GetFlowersDataset();
var columnInference = context.Auto().InferColumns(datasetPath, "Label");
var textLoader = context.Data.CreateTextLoader(columnInference.TextLoaderOptions);
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33 changes: 15 additions & 18 deletions test/Microsoft.ML.Tests/ScenariosWithDirectInstantiation/TensorflowTests.cs
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Expand Up @@ -1274,8 +1274,8 @@ public void TensorFlowImageClassificationDefault()
if (!(RuntimeInformation.IsOSPlatform(OSPlatform.Windows) ||
(RuntimeInformation.IsOSPlatform(OSPlatform.OSX))))
{
Assert.InRange(metrics.MicroAccuracy, 0.3, 1);
Assert.InRange(metrics.MacroAccuracy, 0.3, 1);
Assert.InRange(metrics.MicroAccuracy, 0.2, 1);
Assert.InRange(metrics.MacroAccuracy, 0.2, 1);
}
else
{
Expand Down Expand Up @@ -1370,8 +1370,8 @@ public void TensorFlowImageClassification(ImageClassificationTrainer.Architectur
if (!(RuntimeInformation.IsOSPlatform(OSPlatform.Windows) ||
(RuntimeInformation.IsOSPlatform(OSPlatform.OSX))))
{
Assert.InRange(metrics.MicroAccuracy, 0.3, 1);
Assert.InRange(metrics.MacroAccuracy, 0.3, 1);
Assert.InRange(metrics.MicroAccuracy, 0.2, 1);
Assert.InRange(metrics.MacroAccuracy, 0.2, 1);
}
else
{
Expand Down Expand Up @@ -1429,16 +1429,16 @@ public void TensorFlowImageClassification(ImageClassificationTrainer.Architectur
[TensorFlowFact]
public void TensorFlowImageClassificationWithExponentialLRScheduling()
{
TensorFlowImageClassificationWithLRScheduling(new ExponentialLRDecay());
TensorFlowImageClassificationWithLRScheduling(new ExponentialLRDecay(), 50);
}

[Fact(Skip ="Very unstable tests, causing many build failures.")]
[TensorFlowFact]
public void TensorFlowImageClassificationWithPolynomialLRScheduling()
{
TensorFlowImageClassificationWithLRScheduling(new PolynomialLRDecay());
TensorFlowImageClassificationWithLRScheduling(new PolynomialLRDecay(), 50);
}

internal void TensorFlowImageClassificationWithLRScheduling(LearningRateScheduler learningRateScheduler)
internal void TensorFlowImageClassificationWithLRScheduling(LearningRateScheduler learningRateScheduler, int epoch)
{
string assetsRelativePath = @"assets";
string assetsPath = GetAbsolutePath(assetsRelativePath);
Expand Down Expand Up @@ -1484,17 +1484,14 @@ internal void TensorFlowImageClassificationWithLRScheduling(LearningRateSchedule
// ResnetV2101 you can try a different architecture/
// pre-trained model.
Arch = ImageClassificationTrainer.Architecture.ResnetV2101,
Epoch = 50,
Epoch = epoch,
BatchSize = 10,
LearningRate = 0.01f,
MetricsCallback = (metric) => Console.WriteLine(metric),
ValidationSet = validationSet,
ReuseValidationSetBottleneckCachedValues = false,
ReuseTrainSetBottleneckCachedValues = false,
EarlyStoppingCriteria = null,
// Using Exponential Decay for learning rate scheduling
// You can also try other types of Learning rate scheduling methods
// available in LearningRateScheduler.cs
LearningRateScheduler = learningRateScheduler,
WorkspacePath = GetTemporaryDirectory()
};
Expand Down Expand Up @@ -1526,8 +1523,8 @@ internal void TensorFlowImageClassificationWithLRScheduling(LearningRateSchedule
if (!(RuntimeInformation.IsOSPlatform(OSPlatform.Windows) ||
(RuntimeInformation.IsOSPlatform(OSPlatform.OSX))))
{
Assert.InRange(metrics.MicroAccuracy, 0.3, 1);
Assert.InRange(metrics.MacroAccuracy, 0.3, 1);
Assert.InRange(metrics.MicroAccuracy, 0.2, 1);
Assert.InRange(metrics.MacroAccuracy, 0.2, 1);
}
else
{
Expand Down Expand Up @@ -1669,8 +1666,8 @@ public void TensorFlowImageClassificationEarlyStoppingIncreasing()
if (!(RuntimeInformation.IsOSPlatform(OSPlatform.Windows) ||
(RuntimeInformation.IsOSPlatform(OSPlatform.OSX))))
{
Assert.InRange(metrics.MicroAccuracy, 0.3, 1);
Assert.InRange(metrics.MacroAccuracy, 0.3, 1);
Assert.InRange(metrics.MicroAccuracy, 0.2, 1);
Assert.InRange(metrics.MacroAccuracy, 0.2, 1);
}
else
{
Expand Down Expand Up @@ -1763,8 +1760,8 @@ public void TensorFlowImageClassificationEarlyStoppingDecreasing()
if (!(RuntimeInformation.IsOSPlatform(OSPlatform.Windows) ||
(RuntimeInformation.IsOSPlatform(OSPlatform.OSX))))
{
Assert.InRange(metrics.MicroAccuracy, 0.3, 1);
Assert.InRange(metrics.MacroAccuracy, 0.3, 1);
Assert.InRange(metrics.MicroAccuracy, 0.2, 1);
Assert.InRange(metrics.MacroAccuracy, 0.2, 1);
}
else
{
Expand Down