public UnifiedCameraNeuralNetwork(int inputLayerSize, int hiddenLayerSize, int outputLayerSize,
float learningRate,
bool isStochastic = true, float momentum = 0)
{
// Network functional parameters
_nonLinearityFunc = sigmoid;
_nonLinearityFuncDeriv = sigmoidDerivative;
_errorFunction = meanSquareError;
_errorFunctionDerv = meanSquareErrorDerivative;
// Network initialization
this.LearningRate = learningRate;
_momentum = momentum;
_inputLayerSize = inputLayerSize;
_hiddenLayerSize = hiddenLayerSize;
_outputLayerSize = outputLayerSize;
_input = new Matrix(inputLayerSize + 1, 1); // Vector (+ 1 for bias)
_expected = new Matrix(outputLayerSize, 1); // Vector
// Add 1 for bias
_hiddenLayerWeights = new Matrix(_hiddenLayerSize, _inputLayerSize + 1);
_outputLayerWeights = new Matrix(_outputLayerSize, _hiddenLayerSize + 1);
_prevHiddenLayerWeightsDelta = new Matrix(_hiddenLayerSize, _inputLayerSize + 1);
_prevOutputLayerWeightsDelta = new Matrix(_outputLayerSize, _hiddenLayerSize + 1);
_isStochastic = isStochastic;
_hiddenWeightUpdates = new Matrix(_hiddenLayerSize, _inputLayerSize + 1);
_outputWeightUpdates = new Matrix(_outputLayerSize, _hiddenLayerSize + 1);
_weightUpdatesCount = 0;
_random = new Random(RANDOM_SEED);
randomizeWeights();
}
public static void runMatrixTest()
{
Matrix mat1 = new Matrix(3, 3);
mat1[0, 0] = 1;
mat1[0, 1] = 0;
mat1[0, 2] = 0;
mat1[1, 0] = 0;
mat1[1, 1] = 1;
mat1[1, 2] = 0;
mat1[2, 0] = 0;
mat1[2, 1] = 0;
mat1[2, 2] = 1;
Matrix mat2 = new Matrix(3, 3);
mat2[0, 0] = 1;
mat2[0, 1] = 0;
mat2[0, 2] = 0;
mat2[1, 0] = 0;
mat2[1, 1] = 1;
mat2[1, 2] = 0;
mat2[2, 0] = 0;
mat2[2, 1] = 0;
mat2[2, 2] = 1;
if (!mat1.Equals(mat2))
{
Console.WriteLine("Equal test #1 failed");
return;
}
mat2[0, 0] = 0;
if (mat1.Equals(mat2))
{
Console.WriteLine("Equal test #2 failed");
return;
}
if (!mat1.Equals(Matrix.identity(3, 3)) || mat2.Equals(Matrix.identity(3, 3)))
{
Console.WriteLine("Equal test #3 failed");
return;
}
float[] vals = new float[9];
for (int i = 0; i < 9; i++)
{
vals[i] = i;
}
Matrix matVals = new Matrix(vals);
Matrix matClone = new Matrix(matVals);
if (!matVals.Equals(matClone))
{
Console.WriteLine("Copy constructor failed");
return;
}
Matrix mul = new Matrix(1, 9);
mul = mul.transpose();
mul.init(vals);
if (!mul.Equals(matClone))
{
Console.WriteLine("Init failed");
return;
}
matVals = new Matrix(3, 3);
for (int i = 0; i < 3; i++)
{
for (int j = 0; j < 3; j++)
{
matVals[i, j] = i * 3 + j;
}
}
matClone = new Matrix(matVals);
mul = matVals.mul(mat1);
if (!mul.Equals(matClone))
{
Console.WriteLine("Mul with identity failed");
return;
}
mul = matVals.mul(matClone);
if (mul.Equals(matClone))
{
Console.WriteLine("Mul with non-identity failed");
return;
}
// Debug window zone
Matrix add = matClone + matVals;
Matrix sub = matClone - matVals;
Matrix transpose = matClone.transpose();
Matrix mulScalar = matClone * 0.5f;
Matrix dot = matClone.dot(matVals);
Matrix.MatrixPerElementOperation testfunc1 = testMatrixPerElementOperation;
Matrix.MatrixPerElementProduct testfunc2 = testMatrixProduct;
Matrix invoke1 = matClone.invoke(testfunc1);
Matrix invoke2 = matClone.invoke(testfunc2, matVals);
Console.WriteLine("Test ended");
}
