Function dfdx::tensor_ops::dropout

pub fn dropout<T: Tensor<Dtype = f32>, R: Rng>(t: T, p: f32, rng: &mut R) -> T

Does nothing if no tape is in t. Zeros elements with probability p and scales all elements by 1 / (1 - p). See Tape::OWNS_TAPE.

Described in paper: Improving neural networks by preventing co-adaptation of feature detectors

Example:

let mut rng = StdRng::seed_from_u64(4);
let t = Tensor1D::new([1.0, 2.0, 3.0, 4.0]);

// no tape in t, this won't do anything
let a = dropout(t.clone(), 0.5, &mut rng);
assert_eq!(a.data(), t.data());

// now t has the tape, dropout!
let a = dropout(t.trace(), 0.5, &mut rng);
assert_eq!(a.data(), &[2.0, 4.0, 0.0, 8.0]);