public bool StopTensorboard(string id)
{
bool result = false;
string ResourceName = $"{id}.pmml";
string ResourceNameWithoutExtension = id;
string ResourcePath = DirectoryHelper.GetModelDirectoryPath() + ResourceName;
string Message = "Error";
System.Console.WriteLine("Resource Path " + ResourcePath);
Dictionary <string, string> Result = new Dictionary <string, string>();
try
{
TensorBoard TBTool = this.tbClient.GetTensorBoardTool();
ITask TBNoteBookTask = TBTool.FindTask(ResourcePath);
if (TBNoteBookTask.IsEmpty())
{
Message = "Error : There is no such task running";
}
else
{
TBTool.StopTask(ResourcePath);
result = true;
InstancePayload.Delete(id);
Message = "Tensorboard successfully stop";
}
}
catch (Exception ex)
{
Message = ex.Message;
}
return(result);
}
public UnitTests()
{
if (TensorBoardTool == null)
{
string LogDirectory = System.Environment.CurrentDirectory + System.IO.Path.DirectorySeparatorChar + "logs";
if (!Directory.Exists(LogDirectory))
{
Directory.CreateDirectory(LogDirectory);
}
TensorBoardTool = new TensorBoard("http://localhost", LogDirectory);
}
}
int Train(List <string[]>[] filesByExtension)
{
var random = this.Seed is null ? new Random() : new Random(this.Seed.Value);
if (this.Seed != null)
{
tf.set_random_seed(this.Seed.Value);
}
var test = Split(random, filesByExtension, this.TestSplit, out filesByExtension);
byte[] trainData = Sample(random, filesByExtension, CSharpOrNot.Size,
count: this.TrainingSamples / SamplePart, out int[] trainValues);
byte[] testData = Sample(random, test, CSharpOrNot.Size,
count: this.TestSamples / SamplePart, out int[] testValues);
//var checkpoint = new ModelCheckpoint(
// filepath: Path.Combine(Environment.CurrentDirectory, "weights.{epoch:02d}-{val_loss:.4f}.hdf5"),
// save_best_only: true, save_weights_only: true);
var checkpointBest = new ModelCheckpoint(
filepath: Path.Combine(Environment.CurrentDirectory, "weights.best.hdf5"),
save_best_only: true, save_weights_only: true);
// not present in 1.10, but present in 1.14
((dynamic)checkpointBest).save_freq = "epoch";
ndarray <float> @in = GreyscaleImageBytesToNumPy(trainData, imageCount: this.TrainingSamples / SamplePart,
width: Width, height: Height);
ndarray <int> expectedOut = OutputToNumPy(trainValues);
string runID = DateTime.Now.ToString("s").Replace(':', '-');
string logDir = Path.Combine(".", "logs", runID);
Directory.CreateDirectory(logDir);
var tensorboard = new TensorBoard(log_dir: logDir);
var model = CreateModel(classCount: IncludeExtensions.Length);
model.compile(
optimizer: new Adam(),
loss: tf.keras.losses.sparse_categorical_crossentropy_fn,
metrics: new dynamic[] { "accuracy" });
model.build(input_shape: new TensorShape(null, Height, Width, 1));
model.summary();
tf.io.write_graph(
tf.keras.backend.get_session().graph_def,
Path.GetFullPath("logs"),
name: "model.pbtxt",
as_text: true);
GC.Collect();
var validationInputs = GreyscaleImageBytesToNumPy(testData,
imageCount: this.TestSamples / SamplePart,
width: Width, height: Height);
var validationOutputs = OutputToNumPy(testValues);
model.fit(@in, expectedOut,
batchSize: this.BatchSize, epochs: this.Epochs,
callbacks: new ICallback[] {
checkpointBest,
tensorboard,
}, validationInput: validationInputs, validationTarget: validationOutputs);
model.save_weights(Path.GetFullPath("weights.final.hdf5"));
var fromCheckpoint = CreateModel(classCount: IncludeExtensions.Length);
fromCheckpoint.build(new TensorShape(null, Height, Width, 1));
fromCheckpoint.load_weights(Path.GetFullPath("weights.best.hdf5"));
var evaluationResults = fromCheckpoint.evaluate(@in, (ndarray)expectedOut);
Console.WriteLine($"reloaded: loss: {evaluationResults[0]} acc: {evaluationResults[1]}");
evaluationResults = model.evaluate(@in, (ndarray)expectedOut);
Console.WriteLine($"original: loss: {evaluationResults[0]} acc: {evaluationResults[1]}");
return(0);
}
public PyTensorServiceClient(string HostURL, string RoutePrefix, int[] PortRangeInUse, string ContentDir)
{
TensorBoardTool = new TensorBoard(HostURL, RoutePrefix, PortRangeInUse, ContentDir + System.IO.Path.DirectorySeparatorChar + "data");
}
public PyTensorServiceClient(string HostURL, string ContentDir)
{
TensorBoardTool = new TensorBoard(HostURL, ContentDir + System.IO.Path.DirectorySeparatorChar + "data");
}
public async Task <IActionResult> PostModelTrainAsync(string id)
{
string response = string.Empty;
string dirFullpath = DirectoryHelper.GetDataDirectoryPath();//create new folder in data folder with the name of the file
string reqBody = string.Empty;
string filePath = string.Empty;
string dataFolder = string.Empty;
JObject jObjOrig = new JObject();
using (var reader = new StreamReader(Request.Body))
{
var body = reader.ReadToEnd();
reqBody = body.ToString();
}
try
{
//get file name
if (responseData.Count > 0)
{
foreach (var record in responseData)
{
if (record.Id.ToString() == id)
{
//set the filePath
filePath = record.FilePath;
//create dir with filename in data folder
if (!Directory.Exists(dirFullpath + record.Name.Replace(".pmml", string.Empty)))
{
Directory.CreateDirectory(dirFullpath + record.Name.Replace(".pmml", string.Empty));
dataFolder = dirFullpath + record.Name.Replace(".pmml", string.Empty);
}
}
}
}
//json merge
//Add TensorBoard Info
if (!string.IsNullOrEmpty(reqBody))
{
jObjOrig = JObject.Parse(reqBody);
}
if (!string.IsNullOrEmpty(filePath))
{
string ResourcePath = filePath;
string TensorBoardLink = string.Empty;
string TensorboardLogFolder = string.Empty;
var obj = new
{
base_url = "/",
ResourcePath = $"{ResourcePath}"
};
try
{
var portRegex = new Regex(@"(?<![^/]/[^/]*):\d+");//to remove port number
TensorBoard TBTool = this.tbClient.GetTensorBoardTool();
ZMM.Tasks.ITask TensorBoardTask = TBTool.FindTask(ResourcePath);
if (TensorBoardTask.IsEmpty())
{
TBTool.StartTaskAsync((int)TaskTypes.Start, ResourcePath, (JObject)JObject.FromObject(obj));
}
TensorBoardLink = TBTool.GetResourceLink(ResourcePath, out TensorboardLogFolder);
//
//TB redirection
string tbLink = "";
if (TensorBoardLink.Contains("6006"))
{
tbLink = TensorBoardLink.Replace(":6006", "/tb1");
}
else if (TensorBoardLink.Contains("6007"))
{
tbLink = TensorBoardLink.Replace(":6007", "/tb2");
}
else if (TensorBoardLink.Contains("6008"))
{
tbLink = TensorBoardLink.Replace(":6008", "/tb3");
}
//
jObjOrig.Add("filePath", ResourcePath);
jObjOrig.Add("tensorboardLogFolder", TensorboardLogFolder);
jObjOrig.Add("tensorboardUrl", tbLink);
Logger.LogInformation("PostModelTrainAsync", jObjOrig.ToString());
//for asset
// int sIdx = TensorBoardLink.IndexOf(":6");
// var tbInst = new List<InstanceProperty>();
// tbInst.Add(new InstanceProperty(){ key = "port", value = TensorBoardLink.Substring(sIdx,6)});
//
var objJNBInst = new InstanceResponse()
{
Id = id,
Name = $"{id}",
Type = "TB"
};
InstancePayload.Create(objJNBInst);
//
}
catch (Exception ex)
{
Logger.LogCritical("PostModelTrainAsync", ex.Message);
return(BadRequest(new { user = CURRENT_USER, id = id, message = ex.Message }));
}
}
if (!string.IsNullOrEmpty(dataFolder))
{
jObjOrig.Add("dataFolder", dataFolder);
}
response = await nnclient.TrainModel(jObjOrig.ToString());
return(Json(response));
}
catch (Exception ex)
{
//TO DO: ILogger
string _ex = ex.Message;
return(BadRequest());
}
}
