DigitRecognitionCNN.cs

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using SciSharp.Models;
using SciSharp.Models.ImageClassification;
using System.Diagnostics;
using System.IO;
using System.Linq;
using Tensorflow;
using Tensorflow.NumPy;
using static Tensorflow.Binding;

namespace TensorFlowNET.Examples;

/// <summary>
/// Convolutional Neural Network classifier for Hand Written Digits
/// CNN architecture with two convolutional layers, followed by two fully-connected layers at the end.
/// Use Stochastic Gradient Descent (SGD) optimizer. 
/// https://www.easy-tensorflow.com/tf-tutorials/convolutional-neural-nets-cnns/cnn1
/// </summary>
public class DigitRecognitionCNN : SciSharpExample, IExample
{
    Datasets<MnistDataSet> mnist;

    float accuracy_test = 0f;

    NDArray x_train, y_train;
    NDArray x_valid, y_valid;
    NDArray x_test, y_test;

    public ExampleConfig InitConfig()
        => Config = new ExampleConfig
        {
            Name = "MNIST CNN (Graph)",
            Enabled = true
        };

    public bool Run()
    {
        PrepareData();
        Train();
        Test();
        Predict();

        return accuracy_test > 0.95;
    }

    public override void Train()
    {
        // using wizard to train model
        var wizard = new ModelWizard();
        var task = wizard.AddImageClassificationTask<CNN>(new TaskOptions
        {
            InputShape = (28, 28, 1),
            NumberOfClass = 10,
        });
        task.SetModelArgs(new ConvArgs
        {
            NumberOfNeurons = 128
        });
        task.Train(new TrainingOptions
        {
            Epochs = 5,
            TrainingData = new FeatureAndLabel(x_train, y_train),
            ValidationData = new FeatureAndLabel(x_valid, y_valid)
        });
    }

    public override void Test()
    {
        var wizard = new ModelWizard();
        var task = wizard.AddImageClassificationTask<CNN>(new TaskOptions
        {
            ModelPath = @"image_classification_cnn_v1\saved_model.pb"
        });
        var result = task.Test(new TestingOptions
        {
            TestingData = new FeatureAndLabel(x_test, y_test)
        });
        accuracy_test = result.Accuracy;
    }

    public override void Predict()
    {
        // predict image
        var wizard = new ModelWizard();
        var task = wizard.AddImageClassificationTask<CNN>(new TaskOptions
        {
            LabelPath = @"image_classification_cnn_v1\labels.txt",
            ModelPath = @"image_classification_cnn_v1\saved_model.pb"
        });

        var input = x_test["0:1"];
        var result = task.Predict(input);
        long output = np.argmax(y_test[0]);
        Debug.Assert(result.Label == output.ToString());

        input = x_test["1:2"];
        result = task.Predict(input);
        output = np.argmax(y_test[1]);
        Debug.Assert(result.Label == output.ToString());
    }

    public override void PrepareData()
    {
        Directory.CreateDirectory("image_classification_cnn_v1");
        var loader = new MnistModelLoader();
        mnist = loader.LoadAsync(".resources/mnist", oneHot: true, showProgressInConsole: true).Result;
        (x_train, y_train) = Reformat(mnist.Train.Data, mnist.Train.Labels);
        (x_valid, y_valid) = Reformat(mnist.Validation.Data, mnist.Validation.Labels);
        (x_test, y_test) = Reformat(mnist.Test.Data, mnist.Test.Labels);

        print("Size of:");
        print($"- Training-set:\t\t{len(mnist.Train.Data)}");
        print($"- Validation-set:\t{len(mnist.Validation.Data)}");

        // generate labels
        var labels = range(0, 10).Select(x => x.ToString());
        File.WriteAllLines(@"image_classification_cnn_v1\labels.txt", labels);
    }

    /// <summary>
    /// Reformats the data to the format acceptable for convolutional layers
    /// </summary>
    /// <param name="x"></param>
    /// <param name="y"></param>
    /// <returns></returns>
    private (NDArray, NDArray) Reformat(NDArray x, NDArray y)
    {
        var (unique_y, _) = np.unique(np.argmax(y, 1));
        var (img_size, num_ch, num_class) = ((int)np.sqrt((float)x.shape[1]).astype(np.int32), 1, len(unique_y));
        var dataset = x.reshape((x.shape[0], img_size, img_size, num_ch)).astype(np.float32);
        //y[0] = np.arange(num_class) == y[0];
        //var labels = (np.arange(num_class) == y.reshape(y.shape[0], 1, y.shape[1])).astype(np.float32);
        return (dataset, y);
    }
}