Implement the **Leaky ReLU** activation function and its derivative.
Leaky ReLU:
leaky_relu(x) = x if x > 0
= alpha*x if x <= 0
Derivative:
leaky_relu'(x) = 1 if x > 0
= alpha if x <= 0
Example:
leaky_relu([-2, -1, 0, 1, 2], alpha=0.01) → [-0.02, -0.01, 0, 1, 2]
**Explanation:** Leaky ReLU fixes the "dying ReLU" problem by allowing a small gradient (alpha) for negative inputs instead of zero. This ensures neurons can still learn even when their input is negative.
Constraints:
Test Cases
Test Case 1
Input:
x=[-2,-1,0,1,2], alpha=0.01Expected:
[-0.02,-0.01,0,1,2]Test Case 2
Input:
x=5, alpha=0.01Expected:
5Test Case 3
Input:
x=-5, alpha=0.1Expected:
-0.5+ 2 hidden test cases