// ==== UI methods
private void button1_Click(object sender, EventArgs e) // Load data
{
this.pixelFile = textBox1.Text;
this.labelFile = textBox2.Text;
this.trainImages = MnistDataLoader.LoadData(pixelFile, labelFile);
listBox1.Items.Add("MNIST images loaded into memory");
}
public MnistData()
{
MnistDataLoader mnistDataLoader = new MnistDataLoader();
//訓練用データ
Real[][] x = new Real[mnistDataLoader.TrainData.Length][];
Real[] xLabel = new Real[mnistDataLoader.TrainData.Length];
for (int i = 0; i < mnistDataLoader.TrainData.Length; i++)
{
x[i] = new Real[1 * 28 * 28];
for (int j = 0; j < mnistDataLoader.TrainData[i].Length; j++)
{
x[i][j] = mnistDataLoader.TrainData[i][j] / 255.0;
}
xLabel[i] = mnistDataLoader.TrainLabel[i];
}
this.Train = new LabeledDataSet(x, xLabel, new[] { 1, 28, 28 });
//評価用データ
Real[][] y = new Real[mnistDataLoader.TeachData.Length][];
Real[] yLabel = new Real[mnistDataLoader.TeachData.Length];
for (int i = 0; i < mnistDataLoader.TeachData.Length; i++)
{
y[i] = new Real[1 * 28 * 28];
for (int j = 0; j < mnistDataLoader.TeachData[i].Length; j++)
{
y[i][j] = mnistDataLoader.TeachData[i][j] / 255.0;
}
yLabel[i] = mnistDataLoader.TeachLabel[i];
}
this.Eval = new LabeledDataSet(y, yLabel, new[] { 1, 28, 28 });
}
public void LoadData(Action <int> progressUpdater)
{
if (File.Exists(jsonFilePath))
{
var deserialized = JsonConvert.DeserializeObject <NeuralNetwork>(File.ReadAllText(jsonFilePath));
network = deserialized;
progressUpdater.Invoke(100);
return;
}
network = new NeuralNetwork(inputLength, 100, 10, 0.3);
var database = MnistDataLoader.LoadData(pixelFile, labelFile);
var toDoubles = database.Select(i => i.pixels.SelectMany(s => s)
.Select(
byt => DigitImage.ConvertGrayScaleByteToDouble(byt))).ToArray();
//Test2(toDoubles, database);
var targets = database.Select(i => Convert.ToInt32(i.label)).ToArray();
var percent = 0;
for (var index = 0; index < toDoubles.Length; index++)
{
var doubleArray = toDoubles[index];
var correctAnswer = targets[index];
var target = Enumerable.Range(0, 10).Select(x => 0.01).ToArray();
target[correctAnswer] = 0.99;
percent = NotifyDataLoadingProgress(index, toDoubles.Length, percent, progressUpdater);
network.Train(doubleArray.ToArray(), target);
}
var networkJson = JsonConvert.SerializeObject(network);
File.WriteAllText(jsonFilePath, networkJson);
}
