Category Archives: C#

Using CNTK and C# to train Mario to drive Kart


Introduction

In this blog post I am going to explain one of possible way how to implement Deep Learning ML to play video game. For this purpose I used the following:

  1.  N64 Nintendo emulator which can be found here,
  2. Mario Kart 64 ROM, which can be found on internet as well,
  3. CNTK – Microsoft Cognitive Toolkit
  4. .NET Framework and C#

The idea behind this machine learning project is to capture images together with action, while you play Mario Kart game. Then captured images are transformed into features of training data set, and action keys into label hot vectors respectively.  Since we need to capture images, the emulator should be positioned at fixed location and size during playing the game, as well as during testing algorithm to play game. The flowing image shows N64 emulator graphics configuration settings.

2018-02-15_16-34-03

Also the N64 emulator is positioned to Top-Left corned of screen, so it is easier to capture the images.

Data collection for training data set

During image captures game is played as you would play normally. Also no special agent, not platform is required.

In .NET and C# it is implemented image capture from the specific position of screen, as well as it is recorded which keys are pressed during game play. In order to record keys press, the code found here is modified and used.

The flowing image shows the position of N64 emulator with playing Mario Kart game (1), the windows which is capture and transform the image (2), and the application which collect images, and key press action and generated training data set into file(3).

2018-02-15_16-31-42

The data is generated on the following way:

  • each image is captured, resized to 100×74 pixels and gray scaled prior to be transformed and persisted to data set training file.
  • before image is persisted the hotkey of action key press is recorded and connected to image.

So the training data is persisted into CNTK format which consist of:

  1. |label – which represent 5 component hot vector, indicate: Forward, Break, Forward-Left, Forward-Right and None (1 0 0 0 0)
  2. |features consist of 100×74 numbers which represent pixels of the images.

The following data sample shows how training data set are persisted in the txt file:

|label 1 0 0 0 0 |features 202 202 202 202 202 202 204 189 234 209 199...
|label 0 1 0 0 0 |features 201 201 201 201 201 201 201 201 203 18...
|label 0 0 1 0 0 |features 199 199 199 199 199 199 199 199 199 19...
|label 0 0 0 1 1 |features 199 199 199 199 199 199 199 199 199 19...

Since my training data is more than 300 000 MB of size, I provided just few BM sized file, but you can generate file as big as you wish with just playing the game, and running the flowing code from Program.cs file:

await GenerateData.Start();

Training Model to play the game

Once we generate the data, we can move to the next step: training RCNN model to play the game. For training model the CNTK is used. Also since we play a game and previous sequence will determined the next sequence in the game, LSTM RNN is used. More information about CNTK and LSTM can be found in previous posts. In my case I have collected nearly 15000 images during several round of playing the same level and route. Also for more accurate model much more images should be collected, nearly 100 000. The model is trained in one hour, with 500000 iterations. The source code about whole project can be found on GitHub page. (http://github.com/bhrnjica/LSTMBotGame )

By running the following code, the training process is started with provided training data:

CNTKDeepNN.Train(DeviceDescriptor.GPUDevice(0));

Playing the game with CNTK model

Once we trained the model, we move to the next step: playing a game. The emulator should be positioned on the same position and with the same size in order to play the game.ONce the model is trained and created in th training folder, the playing game can be achive by running:

var dev = DeviceDescriptor.CPUDevice;
MarioKartPlay.LoadModel(“../../../../training/mario_kart_modelv1”, dev);
MarioKartPlay.PlayGame(dev);

How it looks like on my case, you can see on this youtube video:

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CNTK 106 Tutorial – Time Series prediction with LSTM using C#


In this post will show how to implement CNTK 106 Tutorial in C#. This tutorial lecture is written in Python and there is no related example in C#. For this reason I decided to translate this very good tutorial into C#. The tutorial can be found at: CNTK 106: Part A – Time series prediction with LSTM (Basics)  and uses sin wave function in order to predict time series data. For this problem the Long Short Term Memory, LSTM, Recurrent Neural Network is used.

Goal

The goal of this tutorial is prediction the simulated data of a continuous function ( sin wave). From N previous values of the y=sin(t) function where y is the observed amplitude signal at time t, prediction of  M values of y is going to predict for the corresponding future time points.

The excitement of this tutorial is using the LSTM recurrent neural network which is nicely suited for this kind of problems. As you probably know LSTM is special recurrent neural network which has ability to learn from its experience during the training. More information about this fantastic version of recurrent neural network can be found here.

The blog post is divided into several sub-sections:

  1. Simulated data part
  2. LSTM Network
  3. Model training and evaluation

Since the simulated data set is huge, the original tutorial has two running mode which is described by the variable isFast. In case of fast mode, the variable is set to True, and this mode will be used in this tutorial. Later, the reader may change the value to False in order to see much better training model, but the training time will be much longer. The Demo for this this blog post exposes variables of the batch size and iteration number to the user, so the user may defined those numbers as he/she want.

Data generation

In order to generate simulated sin wave data, we are going to implement several helper methods. Let N and M  be a ordered set of past values and future (desired predicted values) of the sine wave, respectively. The two methods are implemented:

  1. generateWaveDataset()

The generateWaveDataset takes the periodic function,set of independent values (which is corresponded the time for this case) and generate the wave function, by providing the time steps and time shift. The method is related to the generate_data() python methods from the original tutorial.

static Dictionary<string, (float[][] train, float[][] valid, float[][] test)> loadWaveDataset(Func<double, double> fun, float[] x0, int timeSteps, int timeShift)
{
    ////fill data
    float[] xsin = new float[x0.Length];//all data
    for (int l = 0; l < x0.Length; l++)
        xsin[l] = (float)fun(x0[l]);


    //split data on training and testing part
    var a = new float[xsin.Length - timeShift];
    var b = new float[xsin.Length - timeShift];

    for (int l = 0; l < xsin.Length; l++)
    {
        //
        if (l < xsin.Length - timeShift) a[l] = xsin[l]; // if (l >= timeShift)
            b[l - timeShift] = xsin[l];
    }

    //make arrays of data
    var a1 = new List<float[]>();
    var b1 = new List<float[]>();
    for (int i = 0; i < a.Length - timeSteps + 1; i++)
    {
        //features
        var row = new float[timeSteps];
        for (int j = 0; j < timeSteps; j++)
            row[j] = a[i + j];
        //create features row
        a1.Add(row);
        //label row
        b1.Add(new float[] { b[i + timeSteps - 1] });
    }

    //split data into train, validation and test data set
    var xxx = splitData(a1.ToArray(), 0.1f, 0.1f);
    var yyy = splitData(b1.ToArray(), 0.1f, 0.1f);


    var retVal = new Dictionary<string, (float[][] train, float[][] valid, float[][] test)>();
    retVal.Add("features", xxx);
    retVal.Add("label", yyy);
    return retVal;
}

Once the data is generated, three datasets should be created: train, validate and test dataset, which are generated by splitting the dataset generated by the above method. The following splitData method splits the original sin wave dataset into three datasets,

static (float[][] train, float[][] valid, float[][] test) splitData(float[][] data, float valSize = 0.1f, float testSize = 0.1f)
{
    //calculate
    var posTest = (int)(data.Length * (1 - testSize));
    var posVal = (int)(posTest * (1 - valSize));

    return (data.Skip(0).Take(posVal).ToArray(), data.Skip(posVal).Take(posTest - posVal).ToArray(), data.Skip(posTest).ToArray());
}

In order to visualize the data, the Windows Forms project is created. Moreover, the ZedGraph .NET class library is used in order to visualize the data. The following picture shows the generated data.

Network modeling

As mentioned on the beginning of the blog post, we are going to create LSTM recurrent neural network, with 1 LSTM cell for each input. We have N inputs and each input is a value in our continuous function. The N outputs from the LSTM are the input into a dense layer that produces a single output. Between LSTM and dense layer we insert a dropout layer that randomly drops 20% of the values coming from the LSTM to prevent overfitting the model to the training dataset. We want use use the dropout layer during training but when using the model to make predictions we don’t want to drop values.

The description above can be illustrated on the following picture:

The implementation of the LSTM can be sumarize in one method, but the real implementation can be viewed in the demo sample which is attached with this blog post.
The following methods implements LSTM network depicted on the image above. The arguments for the method are already defined.

public static Function CreateModel(Variable input, int outDim, int LSTMDim, int cellDim, DeviceDescriptor device, string outputName)
{

    Func<Variable, Function> pastValueRecurrenceHook = (x) => CNTKLib.PastValue(x);

    //creating LSTM cell for each input variable
    Function LSTMFunction = LSTMPComponentWithSelfStabilization<float>(
        input,
        new int[] { LSTMDim },
        new int[] { cellDim },
        pastValueRecurrenceHook,
        pastValueRecurrenceHook,
        device).Item1;

    //after the LSTM sequence is created return the last cell in order to continue generating the network
    Function lastCell = CNTKLib.SequenceLast(LSTMFunction);

    //implement drop out for 10%
    var dropOut = CNTKLib.Dropout(lastCell,0.2, 1);

    //create last dense layer before output
    var outputLayer =  FullyConnectedLinearLayer(dropOut, outDim, device, outputName);

    return outputLayer;
}

Training the network

In order to train the model, the nextBatch() method is implemented that produces batches to feed the training function. Note that because CNTK supports variable sequence length, we must feed the batches as list of sequences. This is a convenience function to generate small batches of data often referred to as minibatch.

private static IEnumerable<(float[] X, float[] Y)> nextBatch(float[][] X, float[][] Y, int mMSize)
{

    float[] asBatch(float[][] data, int start, int count)
    {
        var lst = new List<float>();
        for (int i = start; i < start + count; i++) { if (i >= data.Length)
                break;

            lst.AddRange(data[i]);
        }
        return lst.ToArray();
    }

    for (int i = 0; i <= X.Length - 1; i += mMSize) { var size = X.Length - i; if (size > 0 && size > mMSize)
            size = mMSize;

        var x = asBatch(X, i, size);
        var y = asBatch(Y, i, size);

        yield return (x, y);
    }
}

Note: Since the this tutorial is implemented as WinForms C# project which can visualize training and testing datasets, as well as it  can show the best found model during the training process, there are lot of other implemented methods which are not mentioned here, but can be found in the demo source code attached in this blog post.

Key Insight

When working with LSTM the user should pay attention on the following:

Since LSTM must work with axes with unknown dimensions, the variables should be defined on different way as we could saw in the previous blog posts. So the input and the output variable are initialized with the following code listing:

// build the model
var feature = Variable.InputVariable(new int[] { inDim }, DataType.Float, featuresName, null, false /*isSparse*/);
var label = Variable.InputVariable(new int[] { ouDim }, DataType.Float, labelsName, new List<CNTK.Axis>() { CNTK.Axis.DefaultBatchAxis() }, false);

As specified in the original tutorial: “Specifying the dynamic axes enables the recurrence engine handle the time sequence data in the expected order. Please take time to understand how to work with both static and dynamic axes in CNTK as described here, the dynamic axes is key point in LSTM.
Now the implementation is continue with the defining learning rate, momentum, the learner and the trainer.

 
var lstmModel = LSTMHelper.CreateModel(feature, ouDim, hiDim, cellDim, device, "timeSeriesOutput");

Function trainingLoss = CNTKLib.SquaredError(lstmModel, label, "squarederrorLoss");
Function prediction = CNTKLib.SquaredError(lstmModel, label, "squarederrorEval");


// prepare for training
TrainingParameterScheduleDouble learningRatePerSample = new TrainingParameterScheduleDouble(0.0005, 1);
TrainingParameterScheduleDouble momentumTimeConstant = CNTKLib.MomentumAsTimeConstantSchedule(256);

IList<Learner> parameterLearners = new List<Learner>() {
    Learner.MomentumSGDLearner(lstmModel.Parameters(), learningRatePerSample, momentumTimeConstant, /*unitGainMomentum = */true)  };

//create trainer
var trainer = Trainer.CreateTrainer(lstmModel, trainingLoss, prediction, parameterLearners);

Now the code is ready, and the 10 epochs should return acceptable result:

 
// train the model
for (int i = 1; i <= iteration; i++)
{
    //get the next minibatch amount of data
    foreach (var miniBatchData in nextBatch(featureSet.train, labelSet.train, batchSize))
    {
        var xValues = Value.CreateBatch<float>(new NDShape(1, inDim), miniBatchData.X, device);
        var yValues = Value.CreateBatch<float>(new NDShape(1, ouDim), miniBatchData.Y, device);

        //Combine variables and data in to Dictionary for the training
        var batchData = new Dictionary<Variable, Value>();
        batchData.Add(feature, xValues);
        batchData.Add(label, yValues);

        //train minibarch data
        trainer.TrainMinibatch(batchData, device);
    }

    if (this.InvokeRequired)
    {
        // Execute the same method, but this time on the GUI thread
        this.Invoke(
            new Action(() =>
            {
                //output training process
                progressReport(trainer, lstmModel.Clone(), i, device);
            }
            ));
    }
    else
    {
        //output training process
        progressReport(trainer, lstmModel.Clone(), i, device);

    }             
}

Model Evaluation

Model evaluation is implemented during the training process. In this way we can see the learning process and how the model is getting better and better.

Fore each minibatch the progress method is called which updates the charts for the training and testing data set.

void progressReport(Trainer trainer, Function model, int iteration, DeviceDescriptor device)
{
    textBox3.Text = iteration.ToString();
    textBox4.Text = trainer.PreviousMinibatchLossAverage().ToString();
    progressBar1.Value = iteration;

    reportOnGraphs(trainer, model, iteration, device);
}

private void reportOnGraphs(Trainer trainer, Function model, int i, DeviceDescriptor device)
{
    currentModelEvaluation(trainer, model, i, device);
    currentModelTest(trainer, model, i, device);
}

The following picture shows the training process, where the model evaluation is shown simultaneously, for the training and testing data set.
Also the simulation of the Loss value during the training is simulated as well.

As can be see the blog post extends the original Tutorial with some handy tricks during the training process. Also this demo is good strarting point for development bether tool for LSTM Time Series training. The full source code of this blog post, which shows much more implementation than presented in the blog post can be found here.

Advance Technology Days 13: Predavanje o C# 7.* kompajleru


Predavanje o novoj verziji C# 7 kompajlera prošla je vrlo uspješno, a mnogobrojna publika pokazala je da se ipak i ovakve teme mogu uraditi zanimljive i interesantne.

Sve prezentirano na predavanju moguće je preuzeti sa donjeg linka. Na linku je uključena prezentacijska datoteka i demo primjeri u C#:

Trikovi u C# 7.1 u službi objektnog orijentiranog i funkcionalnog programera

 

Vidimo se na ATD14

 

 

 

Deploy CNTK model to Excel using C#


In the last blog post, we saw how to save model state (checkpoint) in order to load it and train again. Also we have seen how to save model for the evaluation and testing. In fact we have seen how to prepare the model to be production ready.

Once we finish with the modelling process, we enter in to production phase, to install the model on place where we can use it for solving real world problems. This blog post is going to describe the process how deployed CNTK model can be exported to Excel like as AddIn and be used like ordinary Excel formula.

Preparing and deploying CNTK model

From the previous posts we saw how to train and save the model. This will be our starting point for this post.

Assume we trained and saved the model for evaluation from the previous blog post with file name as “IrisModel.model”. The model calculates Iris flower based on 4 input parameters, as we saw earlier.

  1. The first step is to create  .NET Class Library and install the following Nugget packages
    1. CNTK CPU Only ver. 2.3
    2. Excel Dna Addin
    3. Include saved IrisModel.model file in the project as Content and should be copied in Debug folder of the application.

As we can see, for this export we need Excel Dna Addin, fantastic library for making anything as Excel Addin. It can be install as Nuget package, and more information can be found at http://excel-dna.net/.

The following picture shows above 3 actions.

Once we prepare everything, we can start with the implementation of the Excel Addin.

  1. Change the Class.cs name into IrisModel.cs, and implement two methods:
    1. public static string IrisEval(object arg) and
    2. private static float EvaluateModel(float[] vector).

The first method is direct Excel function which will be called in the excel, and the second method is similar from the previous blog post for model evaluation. The following code snippet shows the implementation for the methods:

[ExcelFunction(Description = "IrisEval - Prediction for the Iris flower based on 4 input values.")]
public static string IrisEval(object arg)
{
    try
    {
        //First convert object in to array
        object[,] obj = (object[,])arg;

        //create list to convert values
        List<float> calculatedOutput = new List<float>();
        //
        foreach (var s in obj)
        {
            var ss = float.Parse(s.ToString(), CultureInfo.InvariantCulture);
            calculatedOutput.Add(ss);
        }
        if (calculatedOutput.Count != 4)
            throw new Exception("Incorrect number of input variables. It must be 4!");
        return EvaluateModel(calculatedOutput.ToArray());
    }
    catch (Exception ex)
    {
        return ex.Message;
    }

}
private static string EvaluateModel(float[] vector)
{
    //load the model from disk
    var ffnn_model = Function.Load(@"IrisModel.model", DeviceDescriptor.CPUDevice);

    //extract features and label from the model
    Variable feature = ffnn_model.Arguments[0];
    Variable label = ffnn_model.Output;

    Value xValues = Value.CreateBatch<float>(new int[] { feature.Shape[0] }, vector, DeviceDescriptor.CPUDevice);
    //Value yValues = - we don't need it, because we are going to calculate it

    //map the variables and values
    var inputDataMap = new Dictionary<Variable, Value>();
    inputDataMap.Add(feature, xValues);
    var outputDataMap = new Dictionary<Variable, Value>();
    outputDataMap.Add(label, null);

    //evaluate the model
    ffnn_model.Evaluate(inputDataMap, outputDataMap, DeviceDescriptor.CPUDevice);
    //extract the result  as one hot vector
    var outputData = outputDataMap[label].GetDenseData<float>(label);
    var actualLabels = outputData.Select(l => l.IndexOf(l.Max())).ToList();
    var flower = actualLabels.FirstOrDefault();
    var strFlower = flower == 0 ? "setosa" : flower == 1 ? "versicolor" : "virginica";
    return strFlower;
}<span style="display: inline-block; width: 0px; overflow: hidden; line-height: 0;" data-mce-type="bookmark" class="mce_SELRES_start"></span>

That is all we need for model evaluation in Excel.

Notice that the project must be build with the x64 architecture, and also installed Excel must be in x64 version.  This demo will not work in Excel 32 bits.

Rebuild the project and open Excel file with Iris Data set. You can use the file included in the demo project,  specially prepared for this blog post.

  • Got to Excel – > Options -> Addins,
    • Install the ExportCNTKToExcel-AddIn64-packed addin file.

  • Start typing the Excel  formula :
    • IrisEval(A1:D1) and press Enter. And the magic happen.

 

Complete source code for this blog post can be found here.