dropout

			dropout([rng = rng_from_array(x)], x, p; dims=:, active=true)

The dropout function. If active is true, for each input, either sets that input to 0 (with probability p) or scales it by 1 / (1 - p). dims specifies the unbroadcasted dimensions, e.g. dims=1 applies dropout along columns and dims=2 along rows. If active is false, it just returns the input x.

Specify rng for custom RNGs instead of the default RNG. Note that custom RNGs are only supported on the CPU.

Warning: when using this function, you have to manually manage the activation state. Usually in fact, dropout is used while training but is deactivated in the inference phase. This can be automatically managed using the Dropout layer instead of the dropout function.

The Dropout layer is what you should use in most scenarios.