Output.h

#ifndef OUTPUT_H_
#define OUTPUT_H_

#include <Eigen/Core>
#include <stdexcept>
#include "Config.h"

namespace MiniDNN {



class Output
{
protected:
    typedef Eigen::Matrix<Scalar, Eigen::Dynamic, Eigen::Dynamic> Matrix;
    typedef Eigen::Matrix<Scalar, Eigen::Dynamic, 1> Vector;
    typedef Eigen::RowVectorXi IntegerVector;

public:
    virtual ~Output() {}

    // Check the format of target data, e.g. in classification problems the
    // target data should be binary (either 0 or 1)
    virtual void check_target_data(const Matrix& target) {}

    // Another type of target data where each element is a class label
    // This version may not be sensible for regression tasks, so by default
    // we raise an exception
    virtual void check_target_data(const IntegerVector& target)
    {
        throw std::invalid_argument("[class Output]: This output type cannot take class labels as target data");
    }

    // A combination of the forward stage and the back-propagation stage for the output layer
    // The computed derivative of the input should be stored in this layer, and can be retrieved by
    // the backprop_data() function
    virtual void evaluate(const Matrix& prev_layer_data, const Matrix& target) = 0;

    // Another type of target data where each element is a class label
    // This version may not be sensible for regression tasks, so by default
    // we raise an exception
    virtual void evaluate(const Matrix& prev_layer_data, const IntegerVector& target)
    {
        throw std::invalid_argument("[class Output]: This output type cannot take class labels as target data");
    }

    // The derivative of the input of this layer, which is also the derivative
    // of the output of previous layer
    virtual const Matrix& backprop_data() const = 0;

    // Return the loss function value after the evaluation
    // This function can be assumed to be called after evaluate(), so that it can make use of the
    // intermediate result to save some computation
    virtual Scalar loss() const = 0;
};


} // namespace MiniDNN


#endif /* OUTPUT_H_ */