Inverted Dropout Forward

mediumPRORegularization & TuningDeep LearningDeep Learning 1

Problem Statement

Implement the forward pass of Inverted Dropout.

Background

Dropout is a regularization technique that randomly "drops" neurons during training to prevent overfitting. In Inverted Dropout:

Each neuron is dropped with probability $p$ (drop_prob)
Remaining neurons are scaled by $\frac{1}{1 - p}$ to keep expected values the same
During inference (test mode), no dropout is applied

The scaling ensures that expected output values are consistent between training and testing.

Your Task

Write a function dropout_forward(activations, drop_prob, mask, train_mode=True) that:

If train_mode=False: return activations unchanged
If train_mode=True: apply the given mask (0s and 1s) and scale by 1/(1-drop_prob)

The mask is provided as input (list of 0s and 1s) to make testing deterministic.

Output Format

Return a list of values after applying dropout. Round each value to 4 decimal places.

Example

Input

activations=[10, 20, 30], drop_prob=0.5, mask=[1, 0, 1], train_mode=True

Output

[20.0, 0, 60.0]

Reasoning

Scale = 1/(1-0.5) = 2. First: $10 \times 1$ ×2=20. Second: $20 \times 0$ =0. Third: $30 \times 1$ ×2=60.

Constraints

0 < drop_prob < 1
len(activations) == len(mask)
mask contains only 0s and 1s
List length: 1 to 100 elements

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1. Background Knowledge

Dropout is a regularization technique introduced by Srivastava et al. (2014) that randomly sets neuron activations to zero during training with probability $p$ (drop probability), preventing co-adaptation of features and reducing overfitting. Inverted Dropout, a practical variant, scales surviving neurons by $\frac{1}{1-p}$ during training to maintain consistent expected values between training and inference phases.

Key mathematical insight: For input $x_i$ , the dropout operation is:

y_i = \begin{cases} 0 & \text{with probability } p \\ \frac{x_i}{1-p} & \text{with probability } 1-p \end{cases}

This ensures $\mathbb{E}[y_i] = x_i$ , preserving expected output magnitude.

Prerequisites:

Understanding of neural network forward pass
Basic probability (Bernoulli masking)
List operations in Python
Concept of train vs. inference modes

2. Algorithm Approach

Deterministic masked forward pass with conditional scaling:

Inference mode (train_mode=False): Identity function $y = x$
Training mode (train_mode=True): Element-wise multiplication with mask, then scaling: $y_i = x_i \cdot mask_i \cdot \frac{1}{1-p}$

The provided mask (0s and 1s) simulates the random Bernoulli sampling, making the operation deterministic for testing.

Pseudocode:

if not train_mode:
 return activations
else:
 scale = 1 / (1 - drop_prob)
 return [a * m * scale for a, m in zip(activations, mask)]

3. Step-by-Step Strategy

Check training mode: If train_mode=False, return activations unchanged
Compute scale factor: scale = 1 / (1 - drop_prob)
Apply element-wise operations:

For each index $i$ : result[i] = activations[i] * mask[i] * scale

Round to 4 decimal places: round(value, 4) for each element
Return result list

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solution.py

Python 3.13

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