private void Bw1_DoWork(object sender, DoWorkEventArgs e)
{
try
{
//定义BP神经网络类
BpNet bp = new BpNet(400, 10);
int right_count = 0;
string[] files;
double[] tmp = new double[20];
//读取文件
for (int i = 0; i < 10; i++)
{
right_count = 0;
string dir = test_path + @"\" + i + @"\";
files = Directory.GetFiles(dir);
//共files.Length个样本，每个样本数据有400个字节
double[] input = new double[400];
double[] output = new double[10];
for (int j = 0; j < files.Length; j++)
{
Bitmap bmp = new Bitmap(files[j]);
for (int k = 0; k < bmp.Height; k++)
{
for (int l = 0; l < bmp.Width; l++)
{
input[k * bmp.Width + l] = bmp.GetPixel(l, k).R;
}
}
//交换行，因为位图存储时，先存储最后一行，从图片的底部开始，逐渐向上扫描
for (int k = 0; k < bmp.Height / 2; k++)
{
for (int l = 0; l < bmp.Width; l++)
{
tmp[l] = input[k * bmp.Width + l];
input[k * bmp.Width + l] = input[(bmp.Height - 1 - k) * bmp.Width + l];
input[(bmp.Height - 1 - k) * bmp.Width + l] = tmp[l];
}
}
if (i == bp.test(input))
{
right_count++;
}
}
this.lbTestResult.Items.Add(files.Length + "个" + i + "样本识别成功率：" + (1.0 * right_count / files.Length * 100).ToString("0.00") + "%");
}
this.lblMessage.Text = "测试成功！";
}
catch (Exception ex)
{
MessageBox.Show("出错了" + ex.Message);
}
}
private void Bw_DoWork(object sender, DoWorkEventArgs e)
{
//定义BP神经网络类
BpNet bp = new BpNet(400, 10);
double[] tmp = new double[20];
try
{
//学习率
double lr = Double.Parse(this.txtLearnRate.Text.Trim());
int count = 0; //计数器
int study = 0; //学习（训练）次数
//数据字典
Dictionary <string, int> filedictionary = new Dictionary <string, int>();
for (int i = 0; i < 10; i++)
{
string dir = train_path + @"\" + i + @"\";
string[] files = Directory.GetFiles(dir);
foreach (string item in files)
{
filedictionary.Add(item, i);
}
}
//声明数据存储区域
double[,] input = new double[filedictionary.Count, 400];
double[,] output = new double[filedictionary.Count, 10];
//数据装载
foreach (KeyValuePair <string, int> item in filedictionary)
{
Bitmap bmp = new Bitmap(item.Key);
for (int k = 0; k < bmp.Height; k++)
{
for (int l = 0; l < bmp.Width; l++)
{
input[count, k *bmp.Width + l] = bmp.GetPixel(l, k).R;
}
}
//交换行，因为位图存储时，先存储最后一行，从图片的底部开始，逐渐向上扫描
for (int k = 0; k < bmp.Height / 2; k++)
{
for (int l = 0; l < bmp.Width; l++)
{
tmp[l] = input[count, k *bmp.Width + l];
input[count, k *bmp.Width + l] = input[count, (bmp.Height - 1 - k) * bmp.Width + l];
input[count, (bmp.Height - 1 - k) * bmp.Width + l] = tmp[l];
}
}
output[count, item.Value] = 1;//第j个图片被分为第i类
count++;
}
do
{
if (!bw.CancellationPending)//2.检测用户是否取消
{
//训练
bp.train(input, output, lr);
study++;
this.lblMessage.Text = "第" + study + "次训练的误差： " + bp.e;
}
else
{
break;//停止训练
}
} while (bp.e > 0.01 && study < 50000);
}
catch (Exception ex)
{
MessageBox.Show("出错了" + ex.Message);
}
finally//出错或者中途取消也会保存权值矩阵的信息
{
bp.saveMatrix(bp.w, "w.txt");
bp.saveMatrix(bp.v, "v.txt");
bp.saveMatrix(bp.b1, "b1.txt");
bp.saveMatrix(bp.b2, "b2.txt");
this.lblMessage.Text = "训练终止！";
}
}
