/**
This trains the neural network according to the back propagation algorithm.
The primary steps are:
for each training pattern:
feed forward
propagate backward
until the MSE becomes suitably small
*/
void CNeuralNet::train(std::vector<std::vector<double>> inputs, std::vector<std::vector<double>> outputs, uint trainingSetSize)
{
while (m_MSE > m_mse_cutoff)
{
for (int n = 0; n < trainingSetSize; ++n)
{
// Feed forward and update each nodes outputs
feedForward(inputs[n]);
// Propagate backwards
propagateErrorBackward(outputs[n]);
// Update the MSE
m_MSE = meanSquaredError(outputs[n]);
//cout << m_MSE << endl;
// Once the cutoff value is met the network is considered trained
if (m_MSE < m_mse_cutoff)
{
break;
}
}
}
//std::cout << "Training complete" << std::endl;
}
virtual inline double computeDistance(const Shape * lhs, const Shape * rhs) const {
return (meanSquaredError(lhs->x, rhs->x) + meanSquaredError(lhs->y, rhs->y)) / 2;
}
