Medium

Inverted Dropout Forward Pass

~18 mincode completion

Implement dropout_forward(x, mask, p, training).

  • x is the activation array (any shape).
  • is a same-shaped array of 1 (keep) and 0 (drop), passed in rather than sampled so the result is reproducible.
  • p is the drop probability, 0 <= p < 1.
  • training is a bool. When , return x unchanged, ignoring the mask entirely.
  • Return an array of the same shape as x.

Note p = 0: the scale factor is 1/(1-0) = 1, so training and eval agree. That is the sanity check that your scaling is on the right side of the fraction.

Examples

Training with p=0.5: kept units are doubled, dropped units zeroed

Input
dropout_forward([1, 2, 3, 4], [1, 0, 1, 0], 0.5, True)
Output
[2, 0, 6, 0]

Eval mode returns x unchanged and ignores the mask

Input
dropout_forward([1, 2, 3, 4], [1, 0, 1, 0], 0.5, False)
Output
[1, 2, 3, 4]

p=0.2 scales kept units by 1/0.8 = 1.25

Input
dropout_forward([10, -4, 0, 8], [1, 1, 0, 1], 0.2, True)
Output
[12.5, -5, 0, 10]

Hints

Hint 1

Convert the input with before doing elementwise work.

Hint 2

A common slip here: multiplied by one minus p instead of dividing.

Requirements

  • x: activation array

  • : same-shaped array of 1 (keep) / 0 (drop)

  • p: drop probability in [0, 1)

  • training: if False, return x unchanged

  • Return Array of the same shape as x.

Constraints

  • Allowed library: NumPy only

  • Time limit: 200 ms, Memory: 64 MB

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Where this shows up

~18 min

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Python
import numpy as np

def dropout_forward(x: np.ndarray, mask: np.ndarray, p: float,
                    training: bool) -> np.ndarray:
    """
    Apply inverted dropout.

    Args:
        x:        activation array
        mask:     same-shaped array of 1 (keep) / 0 (drop)
        p:        drop probability in [0, 1)
        training: if False, return x unchanged

    Returns:
        Array of the same shape as x.
    """
    # YOUR CODE HERE
    pass
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