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Full Model Retrain Sample #4127

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Full Model Retrain Sample #4127

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Full Model Retrain sample
sierralee51 Aug 18, 2019
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removed extra lines
sierralee51 Aug 18, 2019
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fixed some hardcoded code
sierralee51 Aug 19, 2019
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sierralee51 Aug 19, 2019
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sierralee51 Aug 19, 2019
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Simplified the way the ml directory path is obtained
sierralee51 Aug 22, 2019
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236 changes: 236 additions & 0 deletions docs/samples/Microsoft.ML.Samples/Dynamic/ImageClassification/MNISTFullModelRetrain.cs
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using System;
using System.IO;
using System.Linq;
using Microsoft.ML;
using Microsoft.ML.Data;

namespace Samples.Dynamic
{
public static class MNISTFullModelRetrain
{
/// <summary>
/// Example full model retrain using the MNIST model in a ML.NET pipeline.
/// </summary>

private static string sourceDir = Directory.GetCurrentDirectory();

// Represents the path to the machinelearning directory
private static string mlDir = @"..\..\..\..\";

public static void Example()
{
var mlContext = new MLContext(seed: 1);

// Download training data into current directory and load into IDataView
var trainData = DataDownload("Train-Tiny-28x28.txt", mlContext);

// Download testing data into current directory and load into IDataView
var testData = DataDownload("MNIST.Test.tiny.txt", mlContext);

// Download the MNIST model and its variables into current directory
ModelDownload();

// Full model retrain pipeline
var pipe = mlContext.Transforms.CopyColumns("Features", "Placeholder")
.Append(mlContext.Model.RetrainDnnModel(
inputColumnNames: new[] { "Features" },
outputColumnNames: new[] { "Prediction" },
labelColumnName: "TfLabel",
dnnLabel: "Label",
optimizationOperation: "MomentumOp",
lossOperation: "Loss",
modelPath: "mnist_conv_model",
metricOperation: "Accuracy",
epoch: 10,
learningRateOperation: "learning_rate",
learningRate: 0.01f,
batchSize: 20))
.Append(mlContext.Transforms.Concatenate("Features",
"Prediction"))
.AppendCacheCheckpoint(mlContext)
.Append(mlContext.MulticlassClassification.Trainers.LightGbm(
new Microsoft.ML.Trainers.LightGbm
.LightGbmMulticlassTrainer.Options()
{
LabelColumnName = "Label",
FeatureColumnName = "Features",
Seed = 1,
NumberOfThreads = 1,
NumberOfIterations = 1
}));

var trainedModel = pipe.Fit(trainData);
var predicted = trainedModel.Transform(testData);
var metrics = mlContext.MulticlassClassification.Evaluate(predicted);

// Print out metrics
Console.WriteLine();
Console.WriteLine($"Micro-accuracy: {metrics.MicroAccuracy}, " +
$"macro-accuracy = {metrics.MacroAccuracy}");

// Get one sample for the fully retrained model to predict on
var sample = GetOneMNISTExample();

// Create a prediction engine to predict on one sample
var predictionEngine = mlContext.Model.CreatePredictionEngine<
MNISTData, MNISTPrediction>(trainedModel);

var prediction = predictionEngine.Predict(sample);

// Print predicted labels
Console.WriteLine("Predicted Labels: ");
foreach (var pLabel in prediction.PredictedLabels)
{
Console.Write(pLabel + " ");
}

// Clean up folder by deleting extra files made during retrain
CleanUp("mnist_conv_model");
}

// Copies data from another location into current directory
// and loads it into IDataView using a TextLoader
private static IDataView DataDownload(string fileName, MLContext mlContext)
{
string dataPath = Path.Combine(mlDir, "test", "data", fileName);
if (!File.Exists(fileName))
{
System.IO.File.Copy(dataPath, Path.Combine(sourceDir, fileName));
}

return mlContext.Data.CreateTextLoader(
new[]
{
new TextLoader.Column("Label", DataKind.UInt32,
new[] { new TextLoader.Range(0) }, new KeyCount(10)),
new TextLoader.Column("TfLabel", DataKind.Int64, 0),
new TextLoader.Column("Placeholder", DataKind.Single,
new[] { new TextLoader.Range(1, 784) })
},
allowSparse: true
).Load(fileName);
}

// Copies MNIST model folder from another location into current directory
private static void ModelDownload()
{
if (!Directory.Exists(Path.Combine(sourceDir, "mnist_conv_model")))
{
// The original path to the MNIST model
var oldModel = Path.Combine(new[] { mlDir, "packages",
"microsoft.ml.tensorflow.testmodels", "0.0.11-test",
"contentfiles", "any", "any", "mnist_conv_model" });

// Create a new folder in the current directory for the MNIST model
string newModel = Directory.CreateDirectory(Path.Combine(sourceDir,
"mnist_conv_model")).FullName;

// Copy the model into the new mnist_conv_model folder
System.IO.File.Copy(Path.Combine(oldModel, "saved_model.pb"),
Path.Combine(newModel, "saved_model.pb"));

// The original folder that the model variables are in.
// Because the folder already exists, the "CreateDirectory" method
// call creates a DirectoryInfo object for the existing folder
// rather than making a new directory.
var oldVariables = Directory.CreateDirectory(Path.Combine(oldModel,
"variables"));

// Create a new folder in the new mnist_conv_model directory to
// store the model variables
var newVariables = Directory.CreateDirectory(Path.Combine(newModel,
"variables"));

// Get the files in the original variables folder
var variableNames = oldVariables.GetFiles();

foreach (var vName in variableNames)
{
// Copy each file from the original variables folder into the
// new variables folder
System.IO.File.Copy(vName.FullName, Path.Combine(
newVariables.FullName, vName.Name));
}

}
}
public class MNISTData
{
public long Label;

[VectorType(784)]
public float[] Placeholder;
}

public class MNISTPrediction
{
[ColumnName("Score")]
public float[] PredictedLabels;
}

// Returns one sample
private static MNISTData GetOneMNISTExample()
{
return new MNISTData()
{
Placeholder = new float[] { 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, 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,
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, 0, 0, 0, 0, 0, 0, 0, 0, 3, 18, 18, 18, 126,
136, 175, 26, 166, 255, 247, 127, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 30, 36, 94, 154, 170, 253, 253, 253, 253, 253, 225, 172,
253, 242, 195, 64, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 49, 238,
253, 253, 253, 253, 253, 253, 253, 253, 251, 93, 82, 82, 56,
39, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 18, 219, 253, 253, 253,
253, 253, 198, 182, 247, 241, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 80, 156, 107, 253, 253, 205, 11, 0, 43,
154, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
14, 1, 154, 253, 90, 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, 139, 253, 190, 2, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 11,
190, 253, 70, 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, 35, 241, 225, 160, 108, 1, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 81,
240, 253, 253, 119, 25, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 45, 186, 253, 253, 150, 27, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
16, 93, 252, 253, 187, 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, 249, 253, 249, 64, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 46, 130,
183, 253, 253, 207, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 39, 148, 229, 253, 253, 253, 250, 182, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 24, 114, 221,
253, 253, 253, 253, 201, 78, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 23, 66, 213, 253, 253, 253, 253, 198, 81, 2,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 18, 171, 219,
253, 253, 253, 253, 195, 80, 9, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 55, 172, 226, 253, 253, 253, 253, 244, 133,
11, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 136,
253, 253, 253, 212, 135, 132, 16, 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, 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, 0, 0, 0, 0 }
};
}

// Deletes extra variable folders produced during retrain
private static void CleanUp(string model_location)
{
var directories = Directory.GetDirectories(model_location,
"variables-*");
if (directories != null && directories.Length > 0)
{
var varDir = Path.Combine(model_location, "variables");
if (Directory.Exists(varDir))
Directory.Delete(varDir, true);
Directory.Move(directories[0], varDir);
}
}
}
}