{
  "id": "inverted-dropout",
  "title": "Inverted dropout",
  "kind": "implement",
  "summary": "Inverted dropout with a given random generator: training forward with an exact mask, backward with the same mask, identity at inference.",
  "difficulty": "easy",
  "tags": ["dropout", "regularization", "gradients"],
  "lesson": "phases/03-deep-learning-core/07-regularization",
  "cheatsheet": "deep-learning-training",
  "minutes": 15,
  "entry": ["dropout_forward", "dropout_backward"],
  "collections": ["deep-learning-core"],
  "time_limit_s": 10,
  "mutants": {
    "backward_no_scale": "Multiplies the gradient by the mask but forgets to divide by 1 - p.",
    "eval_draws_from_rng": "Draws from the generator in inference mode, so later random numbers change.",
    "eval_scales_down": "Multiplies the input by 1 - p in inference mode, as the non-inverted form does.",
    "keep_with_probability_p": "Keeps an entry when the random number is below p, so the mask is the complement.",
    "no_scaling": "Applies the mask but does not divide the kept entries by 1 - p.",
    "scale_by_p": "Divides the kept entries by p, not by 1 - p."
  }
}