private static Task <Tuple <long, TrainedNework_Simple> > TrainAsync(Tuple <string, EncogOCR_SketchData[]>[] sketches, long token, bool shouldGenerateGarbage, CancellationTokenSource cancel)
{
CancellationToken cancelToken = cancel.Token;
return(Task.Run(() =>
{
string[] outputMap = sketches.
Select(o => o.Item1).
ToArray();
int inputSize = sketches[0].Item2[0].NNInput.Length;
#region Training Data
List <double[]> inputs = new List <double[]>();
List <double[]> outputs = new List <double[]>();
int groupIndex = 0;
foreach (var group in sketches)
{
double[] output = Enumerable.Range(0, outputMap.Length).
Select((o, i) => i == groupIndex ? 1d : 0d).
ToArray();
foreach (var input in group.Item2)
{
inputs.Add(input.NNInput);
outputs.Add(output);
}
groupIndex++;
}
if (shouldGenerateGarbage)
{
GenerateGarbageData(sketches.SelectMany(o => o.Item2).ToArray(), outputMap.Length, inputs, outputs);
}
#endregion
//NOTE: If there is an exception, the network couldn't be trained
BasicNetwork network = null;
try
{
network = UtilityEncog.GetTrainedNetwork2(inputs.ToArray(), outputs.ToArray(), 20, 300, cancelToken).NetworkOrNull;
}
catch (Exception) { }
var returnProps = new TrainedNework_Simple()
{
InputSize = inputSize,
Outputs = outputMap,
Network = network,
};
return Tuple.Create(token, returnProps);
}, cancelToken));
}
public void NetworkChanged(TrainedNework_Simple network)
{
_network = new Network(network);
// Detect input size change
if (_networkInputs == null || _networkInputs.Size != network.InputSize)
{
_networkInputs = new NetworkInputs(network.InputSize);
}
// Detect output size change
if (_networkOutputs == null || !_networkOutputs.IsSame(network.Outputs))
{
_networkOutputs = new NetworkOutputs(network.Outputs);
BuildLegend(_networkOutputs.Names, _networkOutputs.Hues);
}
#region Dots
if (_dots == null)
{
EnsureDotsCreated();
}
else
{
_dots.Distances_Input.Clear();
_dots.Distances_Output.Clear();
// Clear
foreach (Dot dot in _dots.Dots)
{
dot.NNInput = null;
dot.NNOutput = null;
}
// Rebuild
foreach (Dot dot in _dots.Dots)
{
ReconstructDot(dot);
}
}
#endregion
}
private static SketchDots TestSamples(TrainedNework_Simple network, EncogOCR_SketchData[] inputSketches, double[] hues)
{
// Run a bunch of test images through the NN
//NOTE: Only .NNInput and .NNOutput props are populated
SketchSample[] sketches = TestSamples_InputOutput(network, inputSketches);
// Build a visual with each sketch as a dot
Visual3D visual = TestSamples_Draw(sketches, hues);
// Calculate the forces between each sketch
Tuple <int, int, double>[] distance_Input, distance_Output;
TestSamples_Distance(out distance_Input, out distance_Output, sketches);
return(new SketchDots()
{
Sketches = sketches,
Visual = visual,
Distances_Input = distance_Input,
Distances_Output = distance_Output,
});
}
private static SketchSample[] TestSamples_InputOutput(TrainedNework_Simple network, EncogOCR_SketchData[] inputSketches)
{
List <SketchSample> retVal = new List <SketchSample>();
int inputSize = network.Network.InputCount;
int outputSize = network.Network.OutputCount;
// Inputs
foreach (EncogOCR_SketchData input in inputSketches)
{
double[] output = new double[outputSize];
network.Network.Compute(input.NNInput, output);
retVal.Add(new SketchSample()
{
NNInput = input.NNInput, NNOutput = output
});
}
// Solid Colors
for (int cntr = 0; cntr <= 16; cntr++)
{
double val = cntr / 16d;
double[] input = Enumerable.Range(0, inputSize).Select(o => val).ToArray();
double[] output = new double[outputSize];
network.Network.Compute(input, output);
retVal.Add(new SketchSample()
{
NNInput = input, NNOutput = output
});
}
//TODO: Generate random brush stroke images
//TODO: Generate random pixelated images
return(retVal.ToArray());
}
//TODO: If they are only adding a new training sketch, then add to existing instead of repositioning everything (but do reavaluate all outputs, because the network is different)
public void VisualizeThis(TrainedNework_Simple network, IEnumerable <EncogOCR_SketchData> sketches)
{
panelLegend.Children.Clear();
//_viewport.Children.RemoveAll()
//_viewport.Children.Remove(_colorWheelVisual);
if (_sketches != null)
{
_viewport.Children.Remove(_sketches.Visual);
}
_network = network;
if (sketches == null)
{
_inputSketches = null;
}
else
{
_inputSketches = sketches.ToArray();
}
if (_network == null || _inputSketches == null || _inputSketches.Length == 0)
{
return;
}
_hues = GetHues(_network.Outputs.Length);
// Make a 2D legend that shows color of each name
BuildLegend(_network.Outputs, _hues);
//_colorWheelVisual = GetColorWheel(_hues.Item1, _hues.Item2);
//_viewport.Children.Add(_colorWheelVisual);
_sketches = TestSamples(_network, _inputSketches, _hues);
_viewport.Children.Add(_sketches.Visual);
}
/// <summary>
/// This clears everything except:
/// session names out of the combo box
/// pixels
/// pen size
/// </summary>
private void ClearEverything()
{
if (_visualizer != null)
{
_visualizer.Clear();
}
_currentSketch = null;
grdGuessDetails.Children.Clear();
grdGuessDetails.RowDefinitions.Clear();
pnlPreviousDrawings.Items.Clear();
_prevSketches.Clear();
cboDrawingLabel.Items.Clear();
canvasInk.Strokes.Clear();
_network = null;
RedrawSmallImage();
RedrawPreviousImages(true);
}
public Network(TrainedNework_Simple network)
{
this.Net = network;
}
public void NetworkChanged(TrainedNework_Simple network)
{
_network = new Network(network);
// Detect input size change
if (_networkInputs == null || _networkInputs.Size != network.InputSize)
{
_networkInputs = new NetworkInputs(network.InputSize);
}
// Detect output size change
if (_networkOutputs == null || !_networkOutputs.IsSame(network.Outputs))
{
_networkOutputs = new NetworkOutputs(network.Outputs);
BuildLegend(_networkOutputs.Names, _networkOutputs.Hues);
}
#region Dots
if (_dots == null)
{
EnsureDotsCreated();
}
else
{
_dots.Distances_Input.Clear();
_dots.Distances_Output.Clear();
// Clear
foreach (Dot dot in _dots.Dots)
{
dot.NNInput = null;
dot.NNOutput = null;
}
// Rebuild
foreach (Dot dot in _dots.Dots)
{
ReconstructDot(dot);
}
}
#endregion
}
/// <summary>
/// This clears everything except:
/// session names out of the combo box
/// pixels
/// pen size
/// </summary>
private void ClearEverything()
{
if (_visualizer != null)
{
_visualizer.Clear();
}
_currentSketch = null;
grdGuessDetails.Children.Clear();
grdGuessDetails.RowDefinitions.Clear();
pnlPreviousDrawings.Items.Clear();
_prevSketches.Clear();
cboDrawingLabel.Items.Clear();
canvasInk.Strokes.Clear();
_network = null;
RedrawSmallImage();
RedrawPreviousImages(true);
}
private static Task<Tuple<long, TrainedNework_Simple>> TrainAsync(Tuple<string, EncogOCR_SketchData[]>[] sketches, long token, bool shouldGenerateGarbage, CancellationTokenSource cancel)
{
CancellationToken cancelToken = cancel.Token;
return Task.Run(() =>
{
string[] outputMap = sketches.
Select(o => o.Item1).
ToArray();
int inputSize = sketches[0].Item2[0].NNInput.Length;
#region Training Data
List<double[]> inputs = new List<double[]>();
List<double[]> outputs = new List<double[]>();
int groupIndex = 0;
foreach (var group in sketches)
{
double[] output = Enumerable.Range(0, outputMap.Length).
Select((o, i) => i == groupIndex ? 1d : 0d).
ToArray();
foreach (var input in group.Item2)
{
inputs.Add(input.NNInput);
outputs.Add(output);
}
groupIndex++;
}
if (shouldGenerateGarbage)
{
GenerateGarbageData(sketches.SelectMany(o => o.Item2).ToArray(), outputMap.Length, inputs, outputs);
}
#endregion
//NOTE: If there is an exception, the network couldn't be trained
BasicNetwork network = null;
try
{
network = UtilityEncog.GetTrainedNetwork2(inputs.ToArray(), outputs.ToArray(), 20, 300, cancelToken).NetworkOrNull;
}
catch (Exception) { }
var returnProps = new TrainedNework_Simple()
{
InputSize = inputSize,
Outputs = outputMap,
Network = network,
};
return Tuple.Create(token, returnProps);
}, cancelToken);
}
public Network(TrainedNework_Simple network)
{
this.Net = network;
}
private static SketchSample[] TestSamples_InputOutput(TrainedNework_Simple network, EncogOCR_SketchData[] inputSketches)
{
List<SketchSample> retVal = new List<SketchSample>();
int inputSize = network.Network.InputCount;
int outputSize = network.Network.OutputCount;
// Inputs
foreach (EncogOCR_SketchData input in inputSketches)
{
double[] output = new double[outputSize];
network.Network.Compute(input.NNInput, output);
retVal.Add(new SketchSample() { NNInput = input.NNInput, NNOutput = output });
}
// Solid Colors
for (int cntr = 0; cntr <= 16; cntr++)
{
double val = cntr / 16d;
double[] input = Enumerable.Range(0, inputSize).Select(o => val).ToArray();
double[] output = new double[outputSize];
network.Network.Compute(input, output);
retVal.Add(new SketchSample() { NNInput = input, NNOutput = output });
}
//TODO: Generate random brush stroke images
//TODO: Generate random pixelated images
return retVal.ToArray();
}
private static SketchDots TestSamples(TrainedNework_Simple network, EncogOCR_SketchData[] inputSketches, double[] hues)
{
// Run a bunch of test images through the NN
//NOTE: Only .NNInput and .NNOutput props are populated
SketchSample[] sketches = TestSamples_InputOutput(network, inputSketches);
// Build a visual with each sketch as a dot
Visual3D visual = TestSamples_Draw(sketches, hues);
// Calculate the forces between each sketch
Tuple<int, int, double>[] distance_Input, distance_Output;
TestSamples_Distance(out distance_Input, out distance_Output, sketches);
return new SketchDots()
{
Sketches = sketches,
Visual = visual,
Distances_Input = distance_Input,
Distances_Output = distance_Output,
};
}
//TODO: If they are only adding a new training sketch, then add to existing instead of repositioning everything (but do reavaluate all outputs, because the network is different)
public void VisualizeThis(TrainedNework_Simple network, IEnumerable<EncogOCR_SketchData> sketches)
{
panelLegend.Children.Clear();
//_viewport.Children.RemoveAll()
//_viewport.Children.Remove(_colorWheelVisual);
if (_sketches != null)
{
_viewport.Children.Remove(_sketches.Visual);
}
_network = network;
if (sketches == null)
{
_inputSketches = null;
}
else
{
_inputSketches = sketches.ToArray();
}
if (_network == null || _inputSketches == null || _inputSketches.Length == 0)
{
return;
}
_hues = GetHues(_network.Outputs.Length);
// Make a 2D legend that shows color of each name
BuildLegend(_network.Outputs, _hues);
//_colorWheelVisual = GetColorWheel(_hues.Item1, _hues.Item2);
//_viewport.Children.Add(_colorWheelVisual);
_sketches = TestSamples(_network, _inputSketches, _hues);
_viewport.Children.Add(_sketches.Visual);
}