public NeuralNetwork(int inputUnits, int hiddenUnits, int outpuUnits, string folderName)
{
//学習値のデータがあるならそこから読み込む
if (System.IO.File.Exists(@"Assets/Resources/AIData/" + folderName + "/LearningDataCSVw1.csv"))
{
w1 = NeuralReadCSV.readCSVData("AIData/" + folderName + "/LearningDataCSVw1");
b1 = NeuralReadCSV.readCSVData("AIData/" + folderName + "/LearningDataCSVb1");
w2 = NeuralReadCSV.readCSVData("AIData/" + folderName + "/LearningDataCSVw2");
b2 = NeuralReadCSV.readCSVData("AIData/" + folderName + "/LearningDataCSVb2");
}
else
{
w1 = NumY.RandomArray(inputUnits, hiddenUnits, 1.0f, -1.0f);
b1 = NumY.RandomArray(1, hiddenUnits, 1.0f, -1.0f);
w2 = NumY.RandomArray(hiddenUnits, outpuUnits, 1.0f, -1.0f);
b2 = NumY.RandomArray(1, outpuUnits, 1.0f, -1.0f);
}
}
// Use this for initialization
void Start()
{
NumYArray xData = new NumYArray(NeuralReadCSV.readCSVData(inputDataName));
NumYArray yData = new NumYArray(NeuralReadCSV.readCSVData(outputDataName));
int epochs = 2000;
float learningRate = 0.1f;
//ニューラルネットワークの生成
NeuralNetwork nn = new NeuralNetwork(xData.Size[1], 8, yData.Size[1], "Aoi");
//時間計測
float startTime = Time.realtimeSinceStartup;
bool a = false;
nn.InputData(xData, yData);
a = nn.Train(epochs, learningRate, a);
//データを基にトレーニング
while (a)
{
a = nn.Train(epochs, learningRate, a);
}
float endTime = Time.realtimeSinceStartup;
//トレーニング時間の表示
Debug.Log(endTime - startTime);
//仮データによる結果の予測
float[][] testList = { new float[] { 0.1f, 0.5f, 0.5f, 0.5f, 0.0f } };
NumYArray testData = new NumYArray(testList);
NumYArray result = new NumYArray(nn.Predict(testData));
//予測結果の表示
DisplayPredict(result);
//学習した重みの保存
nn.SaveLearningData("Aoi/");
}
/// <summary>
/// 初期化
/// </summary>
void Start()
{
charName = gameObject.name.Replace("IntentionObj", "");
string filePath = @"Assets/Resources/AIData/" + charName + "/LearningDataCSVw1.csv";
//csvファイルからデータの読み込み
NumYArray xData = new NumYArray(NeuralReadCSV.readCSVData("AIData/" + inputDataName));
NumYArray yData = new NumYArray(NeuralReadCSV.readCSVData("AIData/" + outputDataName));
//ニューラルネットワークの生成
nn = new NeuralNetwork(xData.Size[1], 8, yData.Size[1], charName);
//学習値のデータがないなら初期学習をする
if (!System.IO.File.Exists(filePath) && !isTrain)
{
nn.InputData(xData, yData);
//データを基にトレーニング
nn.Train(this.epochs, this.learningRate, false);
nn.SaveLearningData(charName + "/");
}
}