public static void CreateConvBatFile(string scriptName, string scriptsPath, string datasetPath, string modelPath, string labelPath, string plotPath, string networkName, int epoch, /*AutonomicParams ap,*/ int indexNumber, int mode)
{
string modeDir = string.Empty;
if (mode == 0)
{
modeDir = @"\genetic\";
}
else if (mode == 1)
{
modeDir = @"\custom\";
}
DirectoryInfo di;
// добавить безопасность созданию директорий
// Models Directory
di = Directory.CreateDirectory(@"C:\keras\folder");
string currentModelsDirectory = modelPath + modeDir + di.CreationTime.ToString("MM-dd-yyyy");
if (!Directory.Exists(currentModelsDirectory))
{
Directory.Move(@"C:\keras\folder", currentModelsDirectory);
}
else
{
Directory.Delete(@"C:\keras\folder");
}
// Labels Directory
di = Directory.CreateDirectory(@"C:\keras\folder");
string currentLabelsDirectory = labelPath + modeDir + di.CreationTime.ToString("MM-dd-yyyy");
if (!Directory.Exists(currentLabelsDirectory))
{
Directory.Move(@"C:\keras\folder", currentLabelsDirectory);
}
else
{
Directory.Delete(@"C:\keras\folder");
}
// Plots Directory
di = Directory.CreateDirectory(@"C:\keras\folder");
string currentPlotsDirectory = plotPath + modeDir + di.CreationTime.ToString("MM-dd-yyyy");
if (!Directory.Exists(currentPlotsDirectory))
{
Directory.Move(@"C:\keras\folder", currentPlotsDirectory);
}
else
{
Directory.Delete(@"C:\keras\folder");
}
string time = File.GetLastWriteTime(scriptsPath + @"\" + scriptName).ToString("HH-mm-ss");
string filenamePart = epoch + "-ep_" + indexNumber + "chr_" + networkName + "_" + time;
//string currentTask = networkName;
string code = @"C:\Users\Rinnetensei\Anaconda3\envs\keras\python.exe " + scriptsPath + @"\" + scriptName + " -d " +
datasetPath + " -m " + currentModelsDirectory + @"\" + filenamePart + "_m -l " + currentLabelsDirectory + @"\" +
filenamePart + "_l -p " + currentPlotsDirectory + @"\" + filenamePart + "_p.png";
try
{
File.Copy(@"C:\keras\Directory\scripts\train.bat", @"C:\keras\Directory\scripts\convolutional\temp_train.bat", true);
Support.insertLineToFile(@"C:\keras\Directory\scripts\convolutional\temp_train.bat", 3, code);
}
catch (Exception ex)
{
//MessageBox.Show(ex.Message);
}
}
public static void InsertConvNetworkCode(String path, int line, ConvolutionalNetwork network, List <String> convActivations, List <String> denseActivations)
{
string loss_function = "\"categorical_crossentropy\"";
if (network.outputs == 2)
{
loss_function = "\"binary_crossentropy\"";
}
string init_lr = network.trainConstSpeed.ToString();
init_lr = init_lr.Replace(",", ".");
string callbacks_str = "";
string code = "";
for (int i = 0; i < network.filters.Count; i++)
{
if (i == 0)
{
code += "model.add(ZeroPadding2D((1, 1), input_shape = (128, 128, 3)))" + Environment.NewLine;
}
code += "model.add(Conv2D(" + network.filters[i].ToString() + ", kernel_size = (" +
network.slidingWindows[i][0].ToString() + ", " + network.slidingWindows[i][1].ToString() +
"), strides = (1, 1), padding='same', activation = '" + convActivations[network.convActivationIndexes[i]] + "'))" + Environment.NewLine;
code += "model.add(MaxPooling2D(pool_size = (2, 2), strides = (2, 2)))" + Environment.NewLine;
}
code += "model.add(Flatten())" + Environment.NewLine;
int dropoutCounter = 0;
for (int i = 0; i < network.neurons.Count; i++)
{
code += "model.add(Dense(" + network.neurons[i].ToString() + ", activation = '" +
denseActivations[network.denseActivationIndexes[i]] + "'))" + Environment.NewLine;
if (network.denseDropoutIndexes.Count > 0)
{
if (network.denseDropoutIndexes[dropoutCounter] == i)
{
code += "model.add(Dropout(" + ((float)network.denseDropoutRates[dropoutCounter] / 100).ToString() + "))" + Environment.NewLine;
if ((network.denseDropoutIndexes.Count - 1) != dropoutCounter)
{
dropoutCounter++;
}
}
}
}
if (network.outputs == 2)
{
code += "model.add(Dense(2, activation = \"softmax\"))" + Environment.NewLine;
}
else
{
code += "model.add(Dense(len(lb.classes_), activation = \"softmax\"))" + Environment.NewLine;
}
code += "INIT_LR = " + init_lr + Environment.NewLine +
"EPOCHS = " + network.networkLearningEpochs.ToString() + Environment.NewLine +
"BS = " + network.batchSize.ToString() + Environment.NewLine + Environment.NewLine;
code += "print(\"[INFO] training network...\")" + Environment.NewLine;
code += "opt = SGD(lr=INIT_LR)" + Environment.NewLine;
code += "model.compile(loss=" + loss_function + ", optimizer=opt, metrics=[\"acc\"])" + Environment.NewLine;
//code += "" + Environment.NewLine;
Support.insertLineToFile(path, line, code);
string forBinaryCode = "trainY = to_categorical(trainY)" + Environment.NewLine +
"testY = to_categorical(testY)" + Environment.NewLine;
if (network.outputs == 2)
{
Support.insertLineToFile(path, 66, forBinaryCode);
}
}
