Class SGDTrainer

include/SGDTrainer.h:16–83

A classic backpropagation SGD Trainer. */

	/** A classic backpropagation SGD Trainer. */
	class SGDTrainer : public Trainer {
	public:

		/**
		 * \brief Initialize empty Backpropagation object
		 */
		SGDTrainer();

    /**
		 * \brief Initialize the object with necessary constants.
		 * \param targetErrorLevel_ at this error level, a net will be considered trained
		 * \param maximumEpochs_ after this number of training iterations (one pass through all of the data points), a net will stop being trained no matter what
		 */
		SGDTrainer(double targetErrorLevel_, int maximumEpochs_);

		/**
		 * \brief Trains a neural network on a training set until the target error level is reached.
		 *
		 * Edits the weights of the neural network until its error in predicting the correctOutput of each input reaches the value of targetErrorLevel
		 * or the number of training cycles reaches the value of maximumIterations.
		 * NOTE: If learning rate is not low enough, the weights of the neural network may got to infinity due to the nature of backpropagation.
		 *
		 * \param network the neural network to be trained
		 * \param input a vector of neural network inputs; each element in input, should have a corresponding output in correctOutput
		 * \param correctOutput network is trained to output an element of correctOutput when fed a corresponding element of the input vector
		 */
		double train(net::NeuralNet *network, const std::vector< std::vector<double> > &input, const std::vector< std::vector<double> > &correctOutput);

		/**
		 * \brief Trains a neural network on a training set for a specified number of epochs.
		 *
		 * Edits the weights of the neural network until its error in predicting the correctOutput of each input reaches the value of targetErrorLevel
		 * or the number of training cycles reaches the value of maximumIterations.
		 * NOTE: If learning rate is not low enough, the weights of the neural network may got to infinity due to the nature of backpropagation.
		 *
		 * \param numberOfEpochs the number of training passes that will be made through the data
		 * \param network the neural network to be trained
		 * \param input a vector of neural network inputs; each element in input, should have a corresponding output in correctOutput
		 * \param correctOutput network is trained to output an element of correctOutput when fed a corresponding element of the input vector
		 */
		double trainEpocs(double numberOfEpochs, net::NeuralNet *network, const std::vector< std::vector<double> > &input, const std::vector< std::vector<double> > &correctOutput);

		void store(std::ofstream *out);

		bool initFromStream(std::ifstream *in);


		double targetErrorLevel; /**< The target error level, set by constructor */
		int maximumEpochs; /**< The maximum number of iterations, set by constructor */

	protected:

		/**
		 * \brief Gets the output of the neural network, calculates the error of each neuron, and edits the weights of the neurons to reduce error
		 *
		 * \param network the neural network to be trained
		 * \param input the input fed to the neural network
		 * \param correctOutput network is trained to output this when fed the input vector