// Start is called before the first frame update
void Start()
{
StreamReader streamReader = new StreamReader(path);
while (!streamReader.EndOfStream)
{
double[] datosEntrada = new double[10];
for (int i = 0; i < datosEntrada.Length; i++)
{
string linea = streamReader.ReadLine();
datosEntrada[i] = System.Double.Parse(linea);
}
double[] datosSalida = new double[14];
for (int i = 0; i < datosSalida.Length; i++)
{
string linea = streamReader.ReadLine();
datosSalida[i] = double.Parse(linea);
}
NeuralNetwork.DataSet dataSet = new NeuralNetwork.DataSet(datosEntrada, datosSalida);
dataSets.Add(dataSet);
}
net.Train(dataSets, 0.1);
nombresAlumnos[0] = "Orlando";
nombresAlumnos[1] = "DOC";
nombresAlumnos[2] = "Alex";
nombresAlumnos[3] = "Fernando";
nombresAlumnos[4] = "Raul";
nombresAlumnos[5] = "Luis";
nombresAlumnos[6] = "Edgardo";
nombresAlumnos[7] = "Mauricio";
nombresAlumnos[8] = "Mauro";
nombresAlumnos[9] = "Juan";
nombresAlumnos[10] = "Alda";
nombresAlumnos[11] = "Martin";
nombresAlumnos[12] = "Arturo";
nombresAlumnos[13] = "Chema";
preguntas[0] = "¿Usa lentes?";
preguntas[1] = "¿Es foraneo?";
preguntas[2] = "¿Es de cabello chino?";
preguntas[3] = "¿Es gordo?";
preguntas[4] = "¿Es programador?";
preguntas[5] = "¿Es artista?";
preguntas[6] = "¿Fuma?";
preguntas[7] = "¿Se encuentra realizando sus prácticas profesionales?";
preguntas[8] = "¿Es alto?";
preguntas[9] = "¿Tiene barba?";
pregunta.text = preguntas[0];
}
private void Awake()
{
instance = this;
thisNet = new NeuralNetwork.NeuralNet(4, 3, 1);
DataSets.Add(new NeuralNetwork.DataSet(new double[] { 1.0, 1.0, 1.0, 0.5 }, new double[] { 0.5 }));
DataSets.Add(new NeuralNetwork.DataSet(new double[] { 1.0, 0.0, 1.0, 0.5 }, new double[] { 0.5 }));
DataSets.Add(new NeuralNetwork.DataSet(new double[] { 1.0, 1.0, 0.0, 0.5 }, new double[] { 0.5 }));
DataSets.Add(new NeuralNetwork.DataSet(new double[] { 1.0, 0.0, 0.0, 0.5 }, new double[] { 0.5 }));
DataSets.Add(new NeuralNetwork.DataSet(new double[] { 0.0, 1.0, 1.0, 0.5 }, new double[] { 0.5 }));
DataSets.Add(new NeuralNetwork.DataSet(new double[] { 0.0, 0.0, 1.0, 0.5 }, new double[] { 0.5 }));
DataSets.Add(new NeuralNetwork.DataSet(new double[] { 0.0, 1.0, 0.0, 0.5 }, new double[] { 0.5 }));
DataSets.Add(new NeuralNetwork.DataSet(new double[] { 0.0, 0.0, 0.0, 0.5 }, new double[] { 0.5 }));
DataSets.Add(new NeuralNetwork.DataSet(new double[] { 1.0, 1.0, 1.0, 0.1 }, new double[] { 0.1 }));
DataSets.Add(new NeuralNetwork.DataSet(new double[] { 1.0, 1.0, 0.0, 0.1 }, new double[] { 0.1 }));
DataSets.Add(new NeuralNetwork.DataSet(new double[] { 1.0, 0.5, 0.0, 0.1 }, new double[] { 0.5 }));
DataSets.Add(new NeuralNetwork.DataSet(new double[] { 1.0, 0.5, 1.0, 0.1 }, new double[] { 0.5 }));
DataSets.Add(new NeuralNetwork.DataSet(new double[] { 0.0, 1.0, 1.0, 0.1 }, new double[] { 0.1 }));
DataSets.Add(new NeuralNetwork.DataSet(new double[] { 0.0, 1.0, 0.0, 0.1 }, new double[] { 0.1 }));
DataSets.Add(new NeuralNetwork.DataSet(new double[] { 0.0, 0.5, 0.0, 0.1 }, new double[] { 0.5 }));
DataSets.Add(new NeuralNetwork.DataSet(new double[] { 0.0, 0.5, 1.0, 0.1 }, new double[] { 0.5 }));
DataSets.Add(new NeuralNetwork.DataSet(new double[] { 1.0, 1.0, 1.0, 0.9 }, new double[] { 0.9 }));
DataSets.Add(new NeuralNetwork.DataSet(new double[] { 1.0, 0.0, 1.0, 0.9 }, new double[] { 0.9 }));
DataSets.Add(new NeuralNetwork.DataSet(new double[] { 1.0, 1.0, 0.5, 0.9 }, new double[] { 0.5 }));
DataSets.Add(new NeuralNetwork.DataSet(new double[] { 1.0, 0.0, 0.5, 0.9 }, new double[] { 0.5 }));
DataSets.Add(new NeuralNetwork.DataSet(new double[] { 0.0, 1.0, 1.0, 0.9 }, new double[] { 0.9 }));
DataSets.Add(new NeuralNetwork.DataSet(new double[] { 0.0, 0.0, 1.0, 0.9 }, new double[] { 0.9 }));
DataSets.Add(new NeuralNetwork.DataSet(new double[] { 0.0, 1.0, 0.5, 0.9 }, new double[] { 0.5 }));
DataSets.Add(new NeuralNetwork.DataSet(new double[] { 0.0, 0.0, 0.5, 0.9 }, new double[] { 0.5 }));
thisNet.Train(DataSets, 10);
}
private void OnEnable()
{
net = new NeuralNet(3, 1, 1);
dataSets = new List <DataSet>();
Debug.Log("inicializando");
double[] inputs = new double[3];
double[] desireOutputs = new double[1];
inputs[0] = 0.1f;
inputs[1] = 0.1f;
inputs[2] = 0.1f;
desireOutputs[0] = 0.1f;
dataSets.Add(new DataSet(inputs, desireOutputs));
net.Train(dataSets, 0.001f);
double[] vals = new double[3];
vals[0] = 0.1f;
vals[1] = 0.1f;
vals[2] = 0.9f;
double[] respuesta = net.Compute(vals);
Debug.Log(respuesta[0]);
if (respuesta[0] < 0.09f)
{
col = 0;
}
if (respuesta[0] > 0.09f && respuesta[0] < 0.1)
{
col = 1;
}
if (respuesta[0] > 0.1f)
{
col = 2;
}
sonidoNotificacion.SetActive(true);
indice++;
botones[0].SetActive(true);
botones[1].SetActive(true);
botones[2].SetActive(true);
cajasMensaje[indice - 1].SetActive(true);
textoMensaje[indice - 1].text = maquina.mensajesMaquina[indice - 1, col];
textoRespuesta[0].text = maquina.mensajesUsuario[indice - 1, 0];
textoRespuesta[1].text = maquina.mensajesUsuario[indice - 1, 1];
textoRespuesta[2].text = maquina.mensajesUsuario[indice - 1, 2];
if (indice == 6)
{
maquina.ActivarEstado(maquina.EstadoCaptura);
}
}
private static void TrainXORTest(NeuralNet NN)
{
double[][] TrainData =
{
new double[] { 0, 1 },
new double[] { 1, 1 },
new double[] { 1, 0 },
new double[] { 0, 0 },
};
var Labels = new double[][]
{
new double[] { 0 },
new double[] { 1 },
new double[] { 0 },
new double[] { 0 },
};
for (int i = 1; i < 10000; i++)
{
TrainData.Shuffle();
NN.Train(TrainData, Labels, 0.01);
}
}
public TrainAndTestPage(NeuralNetRunType runtype, NeuralNet neuralnet, MNISTDataManager mnistdata)
{
// Define GUI
// Page properties
Title = string.Format("{0} Net", runtype.ToString().ToUpperFirstOnly());
BackgroundColor = Color.SteelBlue;
Padding = new Thickness(Application.Current.MainPage.Width * 0.05, Application.Current.MainPage.Height * 0.05);
// Label for description
Label description = new Label
{
Text = string.Format("Please define the number of data sets from the MNIST {0} data base to be used for {0}ing. Each data set represents one digit. The {0} data base holds about {1:N0} data sets in total.", runtype.ToString(), mnistdata.CountData),
Margin = new Thickness(0, 0, 0, Application.Current.MainPage.Height * 0.075)
};
// Label for number of training / testing sets used
Label label_useddatasets = new Label
{
Text = String.Format("{0:N0} data set{1} selected", mnistdata.UsedDataSets, mnistdata.UsedDataSets == 0 ? "" : "s"),
Margin = new Thickness(0, 0, 0, Application.Current.MainPage.Height * 0.075)
};
// Slider for number of data sets used for training / testing
Slider slider_useddatasets = new Slider(1, mnistdata.CountData, mnistdata.UsedDataSets);
slider_useddatasets.ValueChanged += (s, e) =>
{
mnistdata.UsedDataSets = (int)e.NewValue;
label_useddatasets.Text = String.Format("{0:N0} data set{1} selected", mnistdata.UsedDataSets, mnistdata.UsedDataSets == 0 ? "" : "s");
};
// Progress bar
ProgressBar progressbar = new ProgressBar {
Progress = 0
};
// Label for showing progress
Label label_progress = new Label
{
Text = "Currently no calculation running",
};
// Button to start training / testing
Button button = new Button
{
Text = string.Format("Start {0}ing", runtype.ToString().ToUpperFirstOnly()),
WidthRequest = Application.Current.MainPage.Width * 0.5,
HeightRequest = Application.Current.MainPage.Height * 0.10
};
// Define page
Content = new ScrollView
{
Content = new StackLayout
{
Children =
{
description,
label_useddatasets,
slider_useddatasets,
button,
progressbar,
label_progress
}
}
};
// Button event
// Here training and test sessions are executed
button.Clicked += async(s, e) =>
{
// Check if another calculation is currently running
if (activerun == true)
{
var answer = await DisplayAlert("Warning", "Do you want to stop current calculation?", "Yes", "No");
if (answer)
{
endrun = true;
}
return;
}
// Check if selected data set is big
if (mnistdata.UsedDataSets > mnistdata.UsedDataSetsWarning)
{
bool answer = await DisplayAlert("Warning", "Please be aware that big data sets effect run time. Continue?", "Yes", "No");
if (!answer)
{
return;
}
}
// Set activerun and endrun flags
activerun = true;
endrun = false;
// Disable Back button
NavigationPage.SetHasBackButton(this, false);
// Rename Button to Stop
button.Text = string.Format("Stop {0}ing", runtype.ToString().ToUpperFirstOnly());
// Define parameters for progress bar calculation (increase bar 100 times or each 20 runs)
int progresssteps = Math.Max(100, mnistdata.UsedDataSets / 20);
int progressspan = (int)Math.Ceiling((double)mnistdata.UsedDataSets / progresssteps);
// Counter for training / testing runs
int i = 0;
// Initialize progress bar
label_progress.Text = i.ToString() + " / " + mnistdata.UsedDataSets.ToString();
await progressbar.ProgressTo((double)i / mnistdata.UsedDataSets, 1, Easing.Linear);
// Train neural net
if (runtype == NeuralNetRunType.train)
{
while (i < mnistdata.UsedDataSets && endrun == false)
{
// Counter for runs in progress step
int j = 0;
// Training loop for one progress step done as task
await Task.Run(() =>
{
while (j < progressspan && i < mnistdata.UsedDataSets)
{
// Train net with data set i
neuralnet.Train(mnistdata.Input(i), mnistdata.Output(i));
j++;
i++;
}
// Remember number of training data sets
neuralnet.TrainingDataCounter += j;
});
// Update progress bar
label_progress.Text = i.ToString() + " / " + mnistdata.UsedDataSets.ToString();
await progressbar.ProgressTo((double)i / mnistdata.UsedDataSets, 1, Easing.Linear);
}
// Show mesage
await DisplayAlert("Result", string.Format("Neural net trained with {0:N0} data sets", i), "OK");
}
if (runtype == NeuralNetRunType.test)
{
// Setup scorecard for capturing test results
// digittotal counts all trained figures, digitcorrect the ones that are identified correctly
Vector <double> digittotal = Vector <double> .Build.Dense(10);
Vector <double> digitcorrect = Vector <double> .Build.Dense(10);
// Testing loop
while (i < mnistdata.UsedDataSets && endrun == false)
{
// Counter for runs in progress step
int j = 0;
// Testing loop for one progress step done as task
await Task.Run(() =>
{
while (j < progressspan && i < mnistdata.UsedDataSets)
{
// Increase counter for testet digit (0..9)
int number = mnistdata.Number(i);
digittotal[number]++;
// Ask net
Vector <double> answer = neuralnet.Query(mnistdata.Input(i));
// Check if it's correct
if (answer.AbsoluteMaximumIndex() == number)
{
digitcorrect[number]++;
}
j++;
i++;
}
// Remember number of training data sets
neuralnet.TrainingDataCounter += j;
});
// Update progress bar
label_progress.Text = i.ToString() + " / " + mnistdata.UsedDataSets.ToString();
await progressbar.ProgressTo((double)i / mnistdata.UsedDataSets, 1, Easing.Linear);
}
// Remeber performance result
neuralnet.Performance.Add(digitcorrect.Sum() / digittotal.Sum());
// Show test results
await Navigation.PushAsync(new ResultsMNISTPage(neuralnet, mnistdata, digittotal, digitcorrect));
}
// Reset progress bar
label_progress.Text = "Currently no calculation running";
await progressbar.ProgressTo(0.0, 250, Easing.Linear);
// Rename Button to Start
button.Text = string.Format("Start {0}ing", runtype.ToString().ToUpperFirstOnly());
// Enable Back button
NavigationPage.SetHasBackButton(this, true);
// Cancel activerun flag
activerun = false;
};
}
public ResultsCamPage(NeuralNet neuralnet, Vector <double> input, Vector <double> result)
{
// Define GUI
// Page properties
Title = "Results";
BackgroundColor = Color.SteelBlue;
Padding = new Thickness(Application.Current.MainPage.Width * 0.05, Application.Current.MainPage.Height * 0.05);
// Define styles for labels in grid
var labelgridStyle = new Style(typeof(Label))
{
Setters =
{
new Setter {
Property = Label.TextColorProperty, Value = Color.GhostWhite
},
new Setter {
Property = Label.BackgroundColorProperty, Value = Color.SteelBlue
},
new Setter {
Property = Label.HorizontalOptionsProperty, Value = LayoutOptions.FillAndExpand
},
new Setter {
Property = Label.HorizontalTextAlignmentProperty, Value = TextAlignment.Center
},
new Setter {
Property = Label.VerticalOptionsProperty, Value = LayoutOptions.FillAndExpand
},
new Setter {
Property = Label.VerticalTextAlignmentProperty, Value = TextAlignment.Center
}
}
};
var labelgridhighlightStyle = new Style(typeof(Label))
{
BasedOn = labelgridStyle,
Setters =
{
new Setter {
Property = Label.TextColorProperty, Value = Color.GhostWhite
},
new Setter {
Property = Label.BackgroundColorProperty, Value = Color.DarkOrange
},
new Setter {
Property = Label.FontAttributesProperty, Value = FontAttributes.Bold
}
}
};
var labelgridheadlineStyle = new Style(typeof(Label))
{
BasedOn = labelgridStyle,
Setters =
{
new Setter {
Property = Label.TextColorProperty, Value = Color.SteelBlue
},
new Setter {
Property = Label.BackgroundColorProperty, Value = Color.GhostWhite
},
new Setter {
Property = Label.FontAttributesProperty, Value = FontAttributes.Bold
}
}
};
// Define 11x3 grid for representing results
Grid grid = new Grid();
grid.BackgroundColor = Color.GhostWhite;
grid.ColumnSpacing = 1;
grid.RowSpacing = 1;
grid.Padding = new Thickness(1);
grid.HorizontalOptions = LayoutOptions.FillAndExpand;
grid.VerticalOptions = LayoutOptions.FillAndExpand;
for (int i = 0; i < 3; i++)
{
grid.ColumnDefinitions.Add(new ColumnDefinition {
Width = new GridLength(1, GridUnitType.Star)
});
}
for (int i = 0; i < 11; i++)
{
grid.RowDefinitions.Add(new RowDefinition {
Height = new GridLength(1, GridUnitType.Star)
});
}
// Add headlines to grid
grid.Children.Add(new Label {
Text = "Digit", Style = labelgridheadlineStyle
}, 0, 0);
grid.Children.Add(new Label {
Text = "Ouput", Style = labelgridheadlineStyle
}, 1, 0);
grid.Children.Add(new Label {
Text = "Share", Style = labelgridheadlineStyle
}, 2, 0);
// Add content to grid
for (int i = 0; i < 10; i++)
{
if (result.AbsoluteMaximumIndex() == i)
{
grid.Children.Add(new Label {
Text = i.ToString(), Style = labelgridhighlightStyle
}, 0, i + 1);
grid.Children.Add(new Label {
Text = String.Format("{0:N3}", result[i]), Style = labelgridhighlightStyle
}, 1, i + 1);
grid.Children.Add(new Label {
Text = String.Format("{0:P1}", result[i] / result.Sum()), Style = labelgridhighlightStyle
}, 2, i + 1);
}
else
{
grid.Children.Add(new Label {
Text = i.ToString(), Style = labelgridStyle
}, 0, i + 1);
grid.Children.Add(new Label {
Text = String.Format("{0:N3}", result[i]), Style = labelgridStyle
}, 1, i + 1);
grid.Children.Add(new Label {
Text = String.Format("{0:P1}", result[i] / result.Sum()), Style = labelgridStyle
}, 2, i + 1);
}
}
// Define 2x3 grid for training net with own handwriting
Grid grid2 = new Grid();
grid2.BackgroundColor = Color.Transparent;
grid2.ColumnSpacing = 0;
grid2.RowSpacing = 0;
grid2.HorizontalOptions = LayoutOptions.Center;
grid2.ColumnDefinitions.Add(new ColumnDefinition {
Width = new GridLength(8, GridUnitType.Star)
});
grid2.ColumnDefinitions.Add(new ColumnDefinition {
Width = new GridLength(2, GridUnitType.Star)
});
grid2.ColumnDefinitions.Add(new ColumnDefinition {
Width = new GridLength(1, GridUnitType.Star)
});
grid2.ColumnDefinitions.Add(new ColumnDefinition {
Width = new GridLength(2, GridUnitType.Star)
});
grid2.ColumnDefinitions.Add(new ColumnDefinition {
Width = new GridLength(1, GridUnitType.Star)
});
grid2.ColumnDefinitions.Add(new ColumnDefinition {
Width = new GridLength(2, GridUnitType.Star)
});
grid2.RowDefinitions.Add(new RowDefinition {
Height = new GridLength(1, GridUnitType.Star)
});
grid2.RowDefinitions.Add(new RowDefinition {
Height = new GridLength(1, GridUnitType.Star)
});
// Place content
grid2.Children.Add(new Label {
Text = "Net's answer:", Style = labelgridStyle, HorizontalTextAlignment = TextAlignment.Start
}, 0, 0);
grid2.Children.Add(new Label {
Text = "Correct answer:", Style = labelgridStyle, HorizontalTextAlignment = TextAlignment.Start
}, 0, 1);
grid2.Children.Add(new Label {
Text = result.AbsoluteMaximumIndex().ToString(), Style = labelgridStyle
}, 1, 0);
// labelresult keeps correct answer in Text property
Label labelresult = new Label {
Text = result.AbsoluteMaximumIndex().ToString(), Style = labelgridStyle
};
grid2.Children.Add(labelresult, 1, 1);
// Buttons for changing value of correct answer
Button buttondown = new Button()
{
Text = "-"
};
grid2.Children.Add(buttondown, 3, 4, 0, 2);
buttondown.Clicked += (s, e) =>
{
if (Convert.ToInt32(labelresult.Text) > 0)
{
labelresult.Text = (Convert.ToInt32(labelresult.Text) - 1).ToString();
}
else
{
labelresult.Text = "9";
}
};
Button buttonup = new Button()
{
Text = "+"
};
grid2.Children.Add(buttonup, 5, 6, 0, 2);
buttonup.Clicked += (s, e) =>
{
if (Convert.ToInt32(labelresult.Text) < 9)
{
labelresult.Text = (Convert.ToInt32(labelresult.Text) + 1).ToString();
}
else
{
labelresult.Text = "0";
}
};
// Define button to train net
Button button = new Button
{
Text = "Train this Digit",
WidthRequest = Application.Current.MainPage.Width * 0.8,
HeightRequest = Application.Current.MainPage.Height * 0.10,
VerticalOptions = LayoutOptions.Center,
};
// Train net
button.Clicked += (s, e) =>
{
// Determine output vector from correct answer
Vector <double> output = Vector <double> .Build.Dense(10, 0.01);
output[Convert.ToInt32(labelresult.Text)] = 0.99;
// Train net with this data set
neuralnet.Train(input, output);
// Increase number of training data sets used so far
neuralnet.TrainingDataCounter++;
DisplayAlert("Training completed", string.Format("Net trained with {0} digit{1} so far.", neuralnet.TrainingDataCounter, (neuralnet.TrainingDataCounter == 1 ? "" : "s")), "OK");
};
// Define page
Content = new ScrollView
{
Content = new StackLayout
{
Children = { grid, grid2, button },
Spacing = Application.Current.MainPage.Height * 0.05
}
};
}
public static void Train()
{
net.Train(dataSets, 0.05);
}