/// <summary>
/// Create a new digit neural network
/// </summary>
/// <param name="sender">sender object</param>
/// <param name="e">event</param>
private void newDigitToolStripMenuItem_Click(object sender, EventArgs e)
{
int[] layers = new int[3] {
784, 100, 10
};
DigitNN.init(out digitNN, layers);
pictureBoxAI.Image = Properties.Resources.ai;
digit = true;
}
/// <summary>
/// Determine the drawn image and determine the output
/// </summary>
/// <param name="sender">sender object</param>
/// <param name="e">event</param>
private void aiDetermineButton_Click(object sender, EventArgs e)
{
if (digitNN != null)
{
double[] input = DigitNN.transformBitmapdata(B);
byte result = digitNN.determine(input);
if (aiTextBox.Lines.Length == 6)
{
NeuralNetwork.CustomTextBox temp = new NeuralNetwork.CustomTextBox();
for (int i = 1; i < aiTextBox.Lines.Length; i++)
{
temp.Text += aiTextBox.Lines[i] + "\n";
}
temp.Text += "I think it is a " + result + " :)";
aiTextBox.Text = temp.Text;
}
else
{
aiTextBox.Text += "\nI think it is a " + result + " :)";
}
}
if (letterNN != null)
{
double[] input = LetterNN.transformBitmapdata(B);
char result = letterNN.determine(input);
if (aiTextBox.Lines.Length == 6)
{
NeuralNetwork.CustomTextBox temp = new NeuralNetwork.CustomTextBox();
for (int i = 1; i < aiTextBox.Lines.Length; i++)
{
temp.Text += aiTextBox.Lines[i] + "\n";
}
if (result != 0)
{
temp.Text += "I think it is a " + result + " :)";
}
else
{
temp.Text += "I'm not sure :(";
}
aiTextBox.Text = temp.Text;
}
else
{
if (result != 0)
{
aiTextBox.Text += "\nI think it is a " + result + " :)";
}
else
{
aiTextBox.Text += "\nI'm not sure :(";
}
}
}
}
/// <summary>
/// Load digit ai
/// </summary>
/// <param name="sender"></param>
/// <param name="e"></param>
private void loadToolStripMenuItem_Click(object sender, EventArgs e)
{
double[][][] loadedWeights;
double[][] loadedBiases;
int[] header;
int layerLength = 0;
int wbOffset = 0;
string file = "NULL";
DialogResult result = openFileDialog.ShowDialog();
if (result == DialogResult.OK) // Test result.
{
file = openFileDialog.FileName;
byte[] fileByte = File.ReadAllBytes(file);
layerLength = fileByte[0];
header = new int[layerLength];
wbOffset += INT_SIZE;
for (int i = 0; i < layerLength; i++)
{
header[i] = BitConverter.ToInt32(fileByte, INT_SIZE * i + INT_SIZE);
wbOffset += INT_SIZE;
}
//header length: 4, 784 100 100 10
loadedWeights = new double[layerLength - 1][][];
for (int i = 0; i < layerLength - 1; i++)
{
loadedWeights[i] = new double[header[i + 1]][];
for (int j = 0; j < header[i + 1]; j++)
{
loadedWeights[i][j] = new double[header[i]];
Buffer.BlockCopy(fileByte, wbOffset, loadedWeights[i][j], 0, loadedWeights[i][j].Length * DOUBLE_SIZE);
wbOffset += loadedWeights[i][j].Length * DOUBLE_SIZE;
}
}
loadedBiases = new double[layerLength - 1][];
for (int i = 0; i < layerLength - 1; i++)
{
loadedBiases[i] = new double[header[i + 1]];
Buffer.BlockCopy(fileByte, wbOffset, loadedBiases[i], 0, loadedBiases[i].Length * DOUBLE_SIZE);
wbOffset += loadedBiases[i].Length * DOUBLE_SIZE;
}
digitNN = new NeuralNetwork <byte>(header);
DigitNN.init(out digitNN, header);
digitNN.testWB(ref loadedWeights, ref loadedBiases);
pictureBoxAI.Image = Properties.Resources.ai;
digit = true;
}
}
/// <summary>
/// Thread for digit training
/// </summary>
private void trainDigitThread()
{
double[][] transformedData = DigitNN.transformBitmapdata(data);
int epochs = 30;
int batchSize = 10;
double[][] trainData = null;
double[][] validset = null;
byte[] trainLabels = null;
byte[] validsetLabels = null;
if (labelsDigit.Length == 10000)
{
DigitNN.splitIntoTrainAndValidset(transformedData, out trainData, out validset, labelsDigit.Length - labelsDigit.Length / 10);
DigitNN.splitIntoTrainAndValidsetLabels(labelsDigit, out trainLabels, out validsetLabels, labelsDigit.Length - labelsDigit.Length / 10);
}
if (labelsDigit.Length == 60000)
{
DigitNN.splitIntoTrainAndValidset(transformedData, out trainData, out validset, labelsDigit.Length - labelsDigit.Length / 6);
DigitNN.splitIntoTrainAndValidsetLabels(labelsDigit, out trainLabels, out validsetLabels, labelsDigit.Length - labelsDigit.Length / 6);
}
Train <byte> .trainNetwork(digitNN, trainData, validset, labelsDigit, validsetLabels, epochs, batchSize, aiTextBox);
pictureBoxAI.Image = Properties.Resources.ai;
}