Pythonで学ぶ勾配降下法と誤差逆伝播法

# Train one neuron y = w * x + b to fit points on the line y = 2x + 1.
data = [(0, 1), (1, 3), (2, 5), (3, 7)]
w, b = 0.0, 0.0
learning_rate = 0.05

for epoch in range(1, 201):
    grad_w = grad_b = 0.0
    for x, y in data:
        error = (w * x + b) - y          # prediction minus target
        # Chain rule: d(error²)/dw = 2 * error * x, d(error²)/db = 2 * error
        grad_w += 2 * error * x
        grad_b += 2 * error
    # Step against the gradient to make the loss smaller.
    w -= learning_rate * grad_w / len(data)
    b -= learning_rate * grad_b / len(data)
    if epoch in (1, 10, 50, 200):
        loss = sum(((w * x + b) - y) ** 2 for x, y in data) / len(data)
        print(f"epoch {epoch:>3}: w={w:.3f} b={b:.3f} loss={loss:.4f}")