AdaDeltaTests.cpp

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#include <gtest/gtest.h>
#include <cmath>

#include <optimization/ArtificialLandscapesTests.hpp>

// Redefine word "public" so every class field/method will be accessible for tests.
#define private public
#include <optimization/AdaDelta.hpp>



// AdaDelta

TEST_F(Sphere1DLandscape, AdaDelta_Convergence) {
    // Optimization function - 1d AdaGrad with default values.
    mic::neural_nets::optimization::AdaDelta<double> opt(x->rows(), x->cols());

    // Simulate a simple gradient descent.
    size_t iteration = 0;
    double abs_diff = 1.0;
    while (abs_diff > eps) {
        mic::types::MatrixPtr<double> dx = fun.calculateGradient(x);
        // Update with default learning rate.
        opt.update(x, dx, 0.001);

        // Check whether value of the function is finite.
        double value = fun.calculateValue(x);
        ASSERT_EQ(true, std::isfinite(value)) << " at iteration i=" << iteration;

        // Calculate diff - std:abs!!
        abs_diff = std::fabs(value - fun.minValue());
        iteration++;
    }//: while
    ASSERT_GE(eps, std::fabs(fun.calculateValue(x) - fun.minValue()));
    std::cout << "              -> Converged after " << iteration << " iterations\n";
}


TEST_F(Sphere20DLandscape, AdaDelta_Convergence) {
    // Optimization function - 20d AdaDelta (with default values).
    mic::neural_nets::optimization::AdaDelta<double> opt(x->rows(), x->cols());

    // Simulate a simple gradient descent.
    size_t iteration = 0;
    double abs_diff = 1.0;
    while (abs_diff > eps) {
        mic::types::MatrixPtr<double> dx = fun.calculateGradient(x);
        // Update with default learning rate.
        opt.update(x, dx, 0.001);

        // Check whether value of the function is finite.
        double value = fun.calculateValue(x);
        ASSERT_EQ(true, std::isfinite(value)) << " at iteration i=" << iteration;

        // Calculate diff - std:abs!!
        abs_diff = std::fabs(value - fun.minValue());
        iteration++;
    }//: while
    ASSERT_GE(eps, std::fabs(fun.calculateValue(x) - fun.minValue()));
    std::cout << "              -> Converged after " << iteration << " iterations\n";
}


TEST_F(Beale2DLandscape, DISABLED_AdaDelta_Convergence) {
    // Optimization AdaDelta.
    mic::neural_nets::optimization::AdaDelta<double> opt(x->rows(), x->cols());

    // Simulate a simple gradient descent.
    size_t iteration = 0;
    double abs_diff = 1.0;
    while (abs_diff > eps) {
        mic::types::MatrixPtr<double> dx = fun.calculateGradient(x);
        // Update with default learning rate.
        opt.update(x, dx, 0.001);

        // Check whether value of the function is finite.
        double value = fun.calculateValue(x);
        ASSERT_EQ(true, std::isfinite(value)) << " at iteration i=" << iteration;

        // Calculate diff - std:abs!!
        abs_diff = std::fabs(value - fun.minValue());
        iteration++;
    }//: while
    ASSERT_GE(eps, std::fabs(fun.calculateValue(x) - fun.minValue()));
    std::cout << "              -> Converged after " << iteration << " iterations\n";
}