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Gradient Descent with Momentum

Apply momentum updates, which keep part of the previous step so training speeds up along consistent directions.

배우게 될 내용

  • Keep a velocity for each weight
  • Speed up training along steady directions
  • Compare momentum with plain gradient descent

문제

번역본 보기

Momentum makes gradient descent behave like a ball rolling downhill: each step keeps part of the previous one.

Start with a velocity of 0 for every weight. For each step t, with gradient vector g (one value per weight):

  1. v ← beta × v + g
  2. w ← w - learning_rate × v

Implement momentum_steps(gradients, weights, learning_rate, beta), applying one update per row of gradients, in order.

Input

gradients: One row per step; each row has one gradient per weight.

weights: The starting weights.

learning_rate: The step size.

beta: The momentum coefficient (0 means plain gradient descent).

Output

Return the weights after all steps.

제약 조건

0 ≤ steps ≤ 1,000, 1 ≤ weights ≤ 100

0 < learning_rate ≤ 1, 0 ≤ beta < 1

-100 ≤ each value ≤ 100

예시

예시 1
입력:
beta = 0.9
weights = [0.0]
gradients = [
    [1.0],
    [1.0],
    [1.0]
]
learning_rate = 0.1

예상 출력:
expected = [-0.561]
입력:
double beta = 0.9;
std::vector<double> weights = {0.0};
std::vector<std::vector<double>> gradients = {
    {1.0},
    {1.0},
    {1.0}
};
double learning_rate = 0.1;

예상 출력:
std::vector<double> expected = {-0.561};
입력:
double beta = 0.9;
double[] weights = new double[] {0.0};
double[][] gradients = new double[][] {
    new double[] {1.0},
    new double[] {1.0},
    new double[] {1.0}
};
double learning_rate = 0.1;

예상 출력:
double[] expected = new double[] {-0.561};

설명

The velocity grows 1 → 1.9 → 2.71 because the gradient keeps pointing the same way, so the steps are 0.1, 0.19 and 0.271: w = -0.561.

예시 2
입력:
beta = 0.0
weights = [1.0, 1.0]
gradients = [
    [2.0, -4.0]
]
learning_rate = 0.5

예상 출력:
expected = [0.0, 3.0]
입력:
double beta = 0.0;
std::vector<double> weights = {1.0, 1.0};
std::vector<std::vector<double>> gradients = {
    {2.0, -4.0}
};
double learning_rate = 0.5;

예상 출력:
std::vector<double> expected = {0.0, 3.0};
입력:
double beta = 0.0;
double[] weights = new double[] {1.0, 1.0};
double[][] gradients = new double[][] {
    new double[] {2.0, -4.0}
};
double learning_rate = 0.5;

예상 출력:
double[] expected = new double[] {0.0, 3.0};

설명

With beta = 0 this is plain gradient descent: w = [1 - 0.5 × 2, 1 - 0.5 × (-4)] = [0, 3].

참고 자료

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