public static void testDNN()
{
Console.WriteLine("\nBegin Deep Neural Network input-output demo");
Console.WriteLine("\nCreating a 3-4-5-2 neural network");
int numInput = 3;
int numHiddenA = 4;
int numHiddenB = 5;
int numOutput = 3;
DnnStaticTwoHl dnn = new DnnStaticTwoHl(numInput, numHiddenA, numHiddenB, numOutput);
double[] weights = new double[] { 0.01, 0.02, 0.03, 0.04, 0.05, 0.06, 0.07, 0.08, 0.09, 0.10,
0.11, 0.12, 0.13, 0.14, 0.15, 0.16, 0.17, 0.18, 0.19, 0.20,
0.21, 0.22, 0.23, 0.24, 0.25, 0.26, 0.27, 0.28, 0.29, 0.30,
0.31, 0.32, 0.33, 0.34, 0.35, 0.36, 0.37, 0.38, 0.39, 0.40,
0.41, 0.42, 0.43, 0.44, 0.45, 0.46, 0.47, 0.48, 0.49, 0.50,
0.51, 0.52, 0.53, 0.54, 0.55, 0.56, 0.57, 0.58, 0.59 };
dnn.SetWeights(weights);
double[] xValues = new double[] { 1.0, 2.0, 3.0 };
Console.WriteLine("\nDummy weights and bias values are:");
ShowVector(weights, 10, 2, true);
Console.WriteLine("\nDummy inputs are:");
ShowVector(xValues, 3, 1, true);
double[] yValues = dnn.ComputeOutputs(xValues);
Console.WriteLine("\nComputed outputs are:");
ShowVector(yValues, 3, 4, true);
Console.WriteLine("\nEnd deep neural network input-output demo\n");
Console.ReadLine();
}
public static void testDNNTraining()
{
int numInput = 784;
int numHiddenA = 32; //5;
int numHiddenB = 32; //5;
int numOutput = 10; //3;
int numRows = 300; //100;
int seed = 1; // gives nice demo
Console.WriteLine("\nBegin DNN with back-propagation demo");
DateTime startTime = DateTime.Now;
Console.WriteLine("\nGenerating " + numRows +
" artificial data items with " + numInput + " features");
double[][] allData = MakeAllDataFromCSV(numInput, numHiddenA, numHiddenB, numOutput,
numRows, seed, "train1000.csv");
Console.WriteLine("Done");
//ShowMatrix(allData, allData.Length, 2, true);
Console.WriteLine("\nCreating train (80%) and test (20%) matrices");
double[][] trainData;
double[][] testData;
SplitTrainTest(allData, 0.80, seed, out trainData, out testData);
Console.WriteLine("Done\n");
Console.WriteLine("Training data:");
ShowMatrix(trainData, 4, 2, true);
Console.WriteLine("Test data:");
ShowMatrix(testData, 4, 2, true);
Console.WriteLine("Creating a " + numInput + "-" + numHiddenA + "-" + numHiddenB +
"-" + numOutput + " neural network");
DnnStaticTwoHl nn = new DnnStaticTwoHl(numInput, numHiddenA, numHiddenB, numOutput);
int maxEpochs = 1000;
double learnRate = 0.05;
double momentum = 0.01;
Console.WriteLine("Setting maxEpochs = " + maxEpochs);
Console.WriteLine("Setting learnRate = " + learnRate.ToString("F2"));
Console.WriteLine("Setting momentum = " + momentum.ToString("F2"));
Console.WriteLine("\nStarting training");
double[] weights = nn.Train(trainData, maxEpochs, learnRate, momentum);
Console.WriteLine("Done");
//Console.WriteLine("\nFinal neural network model weights and biases:\n");
//ShowVector(weights, 2, 10, true);
//double[] y = nn.ComputeOutputs(new double[] { 1.0, 2.0, 3.0, 4.0 });
//ShowVector(y, 3, 3, true);
double trainAcc = nn.Accuracy(trainData);
Console.WriteLine("\nFinal accuracy on training data = " +
trainAcc.ToString("F4"));
double testAcc = nn.Accuracy(testData);
Console.WriteLine("Final accuracy on test data = " +
testAcc.ToString("F4"));
TimeSpan runTime = DateTime.Now - startTime;
Console.WriteLine("\nTime elapsed: " + Convert.ToString(runTime.TotalMilliseconds / 1000) + " s");
Console.WriteLine("\nEnd DNN with back-propagation demo\n");
Console.ReadLine();
}
} // MakeAllData
static double[][] MakeAllDataFromCSV(int numInput, int numFirstHidden, int numSecondHidden,
int numOutput, int numRows, int seed, string file)
{
Random rnd = new Random(seed);
int numWeights = (numInput * numFirstHidden) + numFirstHidden + (numFirstHidden * numSecondHidden) +
numSecondHidden + (numSecondHidden * numOutput) + numOutput;
double[] weights = new double[numWeights]; // actually weights & biases
for (int i = 0; i < numWeights; ++i)
{
weights[i] = 20.0 * rnd.NextDouble() - 10.0; // [-10.0 to 10.0]
}
Console.WriteLine("Generating weights and biases:");
ShowVector(weights, 2, 10, true);
double[][] result = new double[numRows][]; // allocate return-result
for (int i = 0; i < numRows; ++i)
{
result[i] = new double[numInput + numOutput]; // 1-of-N in last column
}
DnnStaticTwoHl gnn =
new DnnStaticTwoHl(numInput, numFirstHidden, numSecondHidden, numOutput); // generating NN
gnn.SetWeights(weights);
StreamReader reader = File.OpenText(file);
reader.ReadLine(); // Ignore first line.
int r = 0;
while (!reader.EndOfStream)
{
string[] line = reader.ReadLine().Split(',');
outputsFromLabel = new double[numOutput];
labels.Add(line[0]);
//HARDCODED
if (line[0] == "0")
{
outputsFromLabel[0] = 0;
outputsFromLabel[1] = 0;
outputsFromLabel[2] = 0;
outputsFromLabel[3] = 0;
outputsFromLabel[4] = 0;
outputsFromLabel[5] = 0;
outputsFromLabel[6] = 0;
outputsFromLabel[7] = 0;
outputsFromLabel[8] = 0;
outputsFromLabel[9] = 1;
}
if (line[0] == "1")
{
outputsFromLabel[0] = 0;
outputsFromLabel[1] = 0;
outputsFromLabel[2] = 0;
outputsFromLabel[3] = 0;
outputsFromLabel[4] = 0;
outputsFromLabel[5] = 0;
outputsFromLabel[6] = 0;
outputsFromLabel[7] = 0;
outputsFromLabel[8] = 1;
outputsFromLabel[9] = 0;
}
if (line[0] == "2")
{
outputsFromLabel[0] = 0;
outputsFromLabel[1] = 0;
outputsFromLabel[2] = 0;
outputsFromLabel[3] = 0;
outputsFromLabel[4] = 0;
outputsFromLabel[5] = 0;
outputsFromLabel[6] = 0;
outputsFromLabel[7] = 1;
outputsFromLabel[8] = 0;
outputsFromLabel[9] = 0;
}
if (line[0] == "3")
{
outputsFromLabel[0] = 0;
outputsFromLabel[1] = 0;
outputsFromLabel[2] = 0;
outputsFromLabel[3] = 0;
outputsFromLabel[4] = 0;
outputsFromLabel[5] = 0;
outputsFromLabel[6] = 1;
outputsFromLabel[7] = 0;
outputsFromLabel[8] = 0;
outputsFromLabel[9] = 0;
}
if (line[0] == "4")
{
outputsFromLabel[0] = 0;
outputsFromLabel[1] = 0;
outputsFromLabel[2] = 0;
outputsFromLabel[3] = 0;
outputsFromLabel[4] = 0;
outputsFromLabel[5] = 1;
outputsFromLabel[6] = 0;
outputsFromLabel[7] = 0;
outputsFromLabel[8] = 0;
outputsFromLabel[9] = 0;
}
if (line[0] == "5")
{
outputsFromLabel[0] = 0;
outputsFromLabel[1] = 0;
outputsFromLabel[2] = 0;
outputsFromLabel[3] = 0;
outputsFromLabel[4] = 1;
outputsFromLabel[5] = 0;
outputsFromLabel[6] = 0;
outputsFromLabel[7] = 0;
outputsFromLabel[8] = 0;
outputsFromLabel[9] = 0;
}
if (line[0] == "6")
{
outputsFromLabel[0] = 0;
outputsFromLabel[1] = 0;
outputsFromLabel[2] = 0;
outputsFromLabel[3] = 1;
outputsFromLabel[4] = 0;
outputsFromLabel[5] = 0;
outputsFromLabel[6] = 0;
outputsFromLabel[7] = 0;
outputsFromLabel[8] = 0;
outputsFromLabel[9] = 1;
}
if (line[0] == "7")
{
outputsFromLabel[0] = 0;
outputsFromLabel[1] = 0;
outputsFromLabel[2] = 1;
outputsFromLabel[3] = 0;
outputsFromLabel[4] = 0;
outputsFromLabel[5] = 0;
outputsFromLabel[6] = 0;
outputsFromLabel[7] = 0;
outputsFromLabel[8] = 0;
outputsFromLabel[9] = 0;
}
if (line[0] == "8")
{
outputsFromLabel[0] = 0;
outputsFromLabel[1] = 1;
outputsFromLabel[2] = 0;
outputsFromLabel[3] = 0;
outputsFromLabel[4] = 0;
outputsFromLabel[5] = 0;
outputsFromLabel[6] = 0;
outputsFromLabel[7] = 0;
outputsFromLabel[8] = 0;
outputsFromLabel[9] = 0;
}
if (line[0] == "9")
{
outputsFromLabel[0] = 1;
outputsFromLabel[1] = 0;
outputsFromLabel[2] = 0;
outputsFromLabel[3] = 0;
outputsFromLabel[4] = 0;
outputsFromLabel[5] = 0;
outputsFromLabel[6] = 0;
outputsFromLabel[7] = 0;
outputsFromLabel[8] = 0;
outputsFromLabel[9] = 0;
}
double[] inputs = new double[numInput];
for (int i = 0; i < numInput; i++)
{
inputs[i] = double.Parse(line[i]);
}
//patterns.Add(inputs);
// place inputs and 1-of-N output values into curr row
int c = 0; // column into result[][]
for (int i = 0; i < numInput; ++i) // inputs
{
result[r][c++] = (inputs[i] / 255d);
}
for (int i = 0; i < numOutput; ++i) // outputs
{
result[r][c++] = outputsFromLabel[i];
}
r++;
}
reader.Close();
return(result);
} // MakeAllData
static double[][] MakeAllData(int numInput, int numFirstHidden, int numSecondHidden,
int numOutput, int numRows, int seed)
{
Random rnd = new Random(seed);
int numWeights = (numInput * numFirstHidden) + numFirstHidden + (numFirstHidden * numSecondHidden) +
numSecondHidden + (numSecondHidden * numOutput) + numOutput;
double[] weights = new double[numWeights]; // actually weights & biases
for (int i = 0; i < numWeights; ++i)
{
weights[i] = 20.0 * rnd.NextDouble() - 10.0; // [-10.0 to 10.0]
}
Console.WriteLine("Generating weights and biases:");
ShowVector(weights, 2, 10, true);
double[][] result = new double[numRows][]; // allocate return-result
for (int i = 0; i < numRows; ++i)
{
result[i] = new double[numInput + numOutput]; // 1-of-N in last column
}
DnnStaticTwoHl gnn =
new DnnStaticTwoHl(numInput, numFirstHidden, numSecondHidden, numOutput); // generating NN
gnn.SetWeights(weights);
for (int r = 0; r < numRows; ++r) // for each row
{
// generate random inputs
double[] inputs = new double[numInput];
for (int i = 0; i < numInput; ++i)
{
inputs[i] = 20.0 * rnd.NextDouble() - 10.0; // [-10.0 to -10.0]
}
// compute outputs
double[] outputs = gnn.ComputeOutputs(inputs);
// translate outputs to 1-of-N
double[] oneOfN = new double[numOutput]; // all 0.0
int maxIndex = 0;
double maxValue = outputs[0];
for (int i = 0; i < numOutput; ++i)
{
if (outputs[i] > maxValue)
{
maxIndex = i;
maxValue = outputs[i];
}
}
oneOfN[maxIndex] = 1.0;
// place inputs and 1-of-N output values into curr row
int c = 0; // column into result[][]
for (int i = 0; i < numInput; ++i) // inputs
{
result[r][c++] = inputs[i];
}
for (int i = 0; i < numOutput; ++i) // outputs
{
result[r][c++] = oneOfN[i];
}
} // each row
return(result);
} // MakeAllData