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11. Activation function relu
easy
Generalsenior
Implement the ReLU activation function, a common nonlinearity in deep learning that keeps positive values and zeroes out negative values.
ReLU is defined elementwise as:
Requirements
Implement the function
python
Rules:
- Compute ReLU for each element in
x. - Do not use any deep learning libraries (e.g., PyTorch, TensorFlow).
- Prefer vectorized NumPy operations over Python loops when possible.
- Keep the function pure: no printing, no in-place mutation required.
Example
python
Output:
python
Input Signature
| Argument | Type |
|---|---|
| x | np.ndarray |
Output Signature
| Return Name | Type |
|---|---|
| value | np.ndarray |
Constraints
Return NumPy array
Use NumPy vectorization; avoid deep learning libs
No prints; no in-place mutation
Hint 1
ReLU works elementwise: each output is max(0, x_i).
Hint 2
If you use NumPy, np.maximum(0.0, arr) applies ReLU to every element at once.
Roles
ML Engineer
AI Engineer
Companies
GeneralLevels
senior
entry
Tags
relu
numpy-vectorization
activation-function
elementwise-operation
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Python EditorLn 1, Col 1