GradientDescent.hpp

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#ifndef GRADIENTDESCENT_HPP_
#define GRADIENTDESCENT_HPP_

#include <optimization/OptimizationFunction.hpp>

namespace mic {
namespace neural_nets {
namespace optimization {

template <typename eT=float>
class GradientDescent : public OptimizationFunction<eT> {
public:

    GradientDescent(size_t rows_, size_t cols_) {
        // Allocate and reset delta.
        delta = MAKE_MATRIX_PTR(eT, rows_, cols_);
        delta->zeros();
    }

    mic::types::MatrixPtr<eT> calculateUpdate(mic::types::MatrixPtr<eT> x_, mic::types::MatrixPtr<eT> dx_, eT learning_rate_ = 0.001) {
        assert(x_->size() == dx_->size());
        // daltea = - alpha * dW.
        //for (size_t i=0; i<(size_t)x_->size(); i++)
            (*delta) = learning_rate_ * (*dx_);

        // Return the update.
        return delta;
    }

private:
    mic::types::MatrixPtr<eT> delta;

};

} //: optimization
} //: neural_nets
} //: mic

#endif /* GRADIENTDESCENT_HPP_ */