示例#1
0
    void Start()
    {
        observation_inputs = new List <float[, , ]>();
        decision_outputs   = new List <float[]>();
        decision_weights   = new List <float>();
        evalSolver         = Noedify.CreateSolver();

        // PlayerPrefs.DeleteAll();
        rdb = gameObject.GetComponent <Rigidbody>();
    }
 void Start()
 {
     trainingSolver = Noedify.CreateSolver();
     trainingSet    = new List <Hoppy_Brain.PlayerObservationDecision>();
     BuildNetwork();
     no_players = 1;
     ChangeNoPlayers(false);
     lastObstacleSpawn_t = -obstacleSpawnTime;
     topScore            = 0;
 }
示例#3
0
 // Start is called before the first frame update
 void Start()
 {
     if (simController == null)
     {
         simController = GameObject.Find("SimController").GetComponent <Hoppy_SimController>();
     }
     observation_inputs     = new List <float[, , ]>();
     decision_outputs       = new List <float[]>();
     decision_weights       = new List <float>();
     lastMovement_t         = -decisionPeriod;
     evalSolver             = Noedify.CreateSolver();
     modifiedDecisionPeriod = decisionPeriod;
 }
示例#4
0
    public void TrainModel()
    {
        List <float[, , ]> trainingData = new List <float[, , ]>();
        List <float[]>     outputData   = new List <float[]>();

        List <Texture2D[]> MNIST_images = new List <Texture2D[]>();

        MNIST_images.Add(MNIST_images0);
        MNIST_images.Add(MNIST_images1);
        MNIST_images.Add(MNIST_images2);
        MNIST_images.Add(MNIST_images3);
        MNIST_images.Add(MNIST_images4);
        MNIST_images.Add(MNIST_images5);
        MNIST_images.Add(MNIST_images6);
        MNIST_images.Add(MNIST_images7);
        MNIST_images.Add(MNIST_images8);
        MNIST_images.Add(MNIST_images9);
        Noedify_Utils.ImportImageData(ref trainingData, ref outputData, MNIST_images, true);
        debugger.net = net;

        Noedify_Solver.SolverMethod solverMethod = Noedify_Solver.SolverMethod.MainThread;
        if (solverMethodToggle != null)
        {
            if (solverMethodToggle.isOn)
            {
                solverMethod = Noedify_Solver.SolverMethod.Background;
            }
        }

        if (solver == null)
        {
            solver = Noedify.CreateSolver();
        }
        solver.debug = new Noedify_Solver.DebugReport();
        sw.Start();
        //solver.costThreshold = 0.01f; // Add a cost threshold to prematurely end training when a suitably low error is achieved
        //solver.suppressMessages = true; // suppress training messages from appearing in editor the console
        solver.TrainNetwork(net, trainingData, outputData, no_epochs, batch_size, trainingRate, costFunction, solverMethod, null, 8);
        float[] cost = solver.cost_report;

        StartCoroutine(PlotCostWhenComplete(solver, cost));
    }
 void Start()
 {
     trainingSolver = Noedify.CreateSolver();
     trainingSet    = new List <Hoppy_Brain.PlayerObservationDecision>();
     BuildNetwork();
 }
示例#6
0
    Noedify.Net BuildAndImportModel()
    {
        modelImportComplete = false;
        int no_labels = 10;

        bool importTensorflowModel = false;

        net = new Noedify.Net();

        // Attempt to load network saved as a binary file
        // This is much faster than importing form a parameters file
        bool status = net.LoadModel("Noedify-Model_Digit_Drawing_Test");

        if (status == false)
        {
            print("Binary file not found. Importing Tensorflow parameters.");
            importTensorflowModel = true;
        }
        // If the binary file doesn't exist yet, import the parameters from the Tensorflow file
        // This is slower, so we will save the network as a binary file after importing it
        if (importTensorflowModel)
        {
            /* Input layer */
            Noedify.Layer inputLayer = new Noedify.Layer(
                Noedify.LayerType.Input2D, // layer type
                new int[2] {
                28, 28
            },                // input size
                1,            // # of channels
                "input layer" // layer name
                );
            net.AddLayer(inputLayer);

            // Hidden layer 0
            Noedify.Layer hiddenLayer0 = new Noedify.Layer(
                Noedify.LayerType.FullyConnected,   // layer type
                600,                                // layer size
                Noedify.ActivationFunction.Sigmoid, // activation function
                "fully connected 1"                 // layer name
                );
            net.AddLayer(hiddenLayer0);

            // Hidden layer 2
            Noedify.Layer hiddenLayer1 = new Noedify.Layer(
                Noedify.LayerType.FullyConnected,   // layer type
                300,                                // layer size
                Noedify.ActivationFunction.Sigmoid, // activation function
                "fully connected 2"                 // layer name
                );
            net.AddLayer(hiddenLayer1);

            // Hidden layer 2
            Noedify.Layer hiddenLayer2 = new Noedify.Layer(
                Noedify.LayerType.FullyConnected,   // layer type
                140,                                // layer size
                Noedify.ActivationFunction.Sigmoid, // activation function
                "fully connected 3"                 // layer name
                );
            net.AddLayer(hiddenLayer2);

            /* Output layer */
            Noedify.Layer outputLayer = new Noedify.Layer(
                Noedify.LayerType.Output,           // layer type
                no_labels,                          // layer size
                Noedify.ActivationFunction.Sigmoid, // activation function
                "output layer"                      // layer name
                );
            net.AddLayer(outputLayer);

            net.BuildNetwork();

            status = NSAI_Manager.ImportNetworkParameters(net, "FC_mnist_600x300x140_parameters");
            if (status)
            {
                print("Successfully loaded model.");
            }
            else
            {
                print("Tensorflow model load failed. Have you moved the \"...Assets/Noedify/Resources\" folder to: \"...Assets/Resources\" ?");
                print("All model parameter files must be stored in: \"...Assets/Resources/Noedify/ModelParameterFiles\"");
                return(null);
            }
            net.SaveModel("Noedify-Model_Digit_Drawing_Test");
            print("Saved binary model file \"Noedify-Model_Digit_Drawing_Test\"");
        }
        solver = Noedify.CreateSolver();
        solver.suppressMessages = true;
        modelImportComplete     = true;
        return(net);
    }