Implement the **inference (forward pass) of a simple Multi-Layer Perceptron (MLP)** so you can turn an input vector into an output prediction. You’ll compute two linear layers with a ReLU activation in between, using the standard MLP formula:
$$
\hat{y} = W_2 \,\text{ReLU}(W_1 x + b_1) + b_2
$$

## Requirements

Implement the function
```python
import numpy as np

def mlp_inference(x, W1, b1, W2, b2):
    \"""
    Runs a 2-layer MLP forward pass: Linear -> ReLU -> Linear.

    Args:
        x (np.ndarray): Input vector of shape (d_in,).
        W1 (np.ndarray): First layer weights of shape (d_hidden, d_in).
        b1 (np.ndarray): First layer bias of shape (d_hidden,).
        W2 (np.ndarray): Second layer weights of shape (d_out, d_hidden).
        b2 (np.ndarray): Second layer bias of shape (d_out,).

    Returns:
        np.ndarray: Output vector y_hat of shape (d_out,).
    \"""
    # Your solution below
```

**Rules:**
- Compute the first linear layer: $ z_1 = W_1 x + b_1 $.
- Apply ReLU elementwise: $ h = \max(0, z_1) $.
- Compute the output layer: $ \hat{y} = W_2 h + b_2 $.
- Return the output as a NumPy array.
- Use only NumPy and Python built-in libraries (no deep learning frameworks).

## Example

```python
x  = np.array([1.0, -2.0])
W1 = np.array([[1.0, 0.5],
               [-1.0, 2.0]])
b1 = np.array([0.0, 1.0])

W2 = np.array([[2.0, -1.0]])
b2 = np.array([0.5])

mlp_inference(x, W1, b1, W2, b2)
```

Output:
```python
array([0.5])
```

Encode and Decode TinyURL

Top-Selling Genres

Multi layer perceptron inference

Solve this easy level ML coding question from General. The premier LeetCode for ML platform to master Python, SQL, and Machine Learning algorithms.

924. Multi layer perceptron inference

Examples

Constraints

Similar Questions

Argument	Type
x	np.ndarray
W1	np.ndarray
W2	np.ndarray
b1	np.ndarray
b2	np.ndarray