Method train

src/SGDTrainer.cpp:23–48

}

double SGDTrainer::train(net::NeuralNet *network, const std::vector< std::vector<double> > &input, const std::vector< std::vector<double> > &correctOutput) {
	double totalError = 0;
	int iterations = 0;
	resetNetworkVectors(network);

	do {
		for(unsigned int a = 0; a < input.size(); a++) {
			trainOnDataPoint(network, input[a], correctOutput[a]);
		}

    totalError = 0;
    for(unsigned int trialIndex = 0; trialIndex < input.size(); trialIndex++) {
      std::vector<double> output = network->getOutput(input[trialIndex]);
    	for(unsigned int outputIndex = 0; outputIndex < output.size(); outputIndex++) {
    		totalError += pow(correctOutput[trialIndex][outputIndex] - output[outputIndex], 2);
    	}
    }
		iterations++;
	} while(totalError > targetErrorLevel && iterations < maximumEpochs);

	//if(iterations >= maximumEpochs-1) std::cout << "SGDTrainer hit max epochs with an error level of " << totalError << ".\n";

	finalWeights = network->getWeights3D();

  return totalError;
}

double SGDTrainer::trainEpocs(double numberOfEpochs, net::NeuralNet *network, const std::vector< std::vector<double> > &input, const std::vector< std::vector<double> > &correctOutput) {
	resetNetworkVectors(network);