L1 Regularization Penalty
~10 mincode completion
Implement l1_penalty(W, lambda_) that returns the scalar L1 regularization term.
Examples
2x2 matrix, lambda=1
- Input
- l1_penalty([[1, -1], [2, -2]], 1)
- Output
- 6
Row vector, lambda=0.5 halves the sum
- Input
- l1_penalty([[1, 2, 3]], 0.5)
- Output
- 3
Zero weights: penalty is zero
- Input
- l1_penalty([[0, 0, 0]], 1)
- Output
- 0
Hints
Hint 1
Sum with , and check which axis you are summing over.
Hint 2
Reach for abs rather than squared values.
Requirements
: Weight matrix (any shape)
lambda_: Regularization strength (>= 0)Return Scalar: lambda_ * sum(|W|)
Constraints
Allowed library: NumPy only
Time limit: 200 ms, Memory: 64 MB
Where this shows up
~10 min
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Python
import numpy as np
def l1_penalty(W: np.ndarray, lambda_: float) -> float:
"""
Compute the L1 regularization penalty.
Args:
W: Weight matrix (any shape)
lambda_: Regularization strength (>= 0)
Returns:
Scalar: lambda_ * sum(|W|)
"""
# YOUR CODE HERE
pass