Dropout is a regularization technique for reducing overfitting in neural networks by preventing complex co-adaptations on training data. It works by temporarily disabling neurons during each training pass. It also is a very efficient way of performing model averaging with neural networks.
|1||ratio||0.5||float||The ratio of neurons that are dropped during each training pass.|
use Rubix\ML\NeuralNet\Layers\Dropout; $layer = new Dropout(0.2);
- N. Srivastava et al. (2014). Dropout: A Simple Way to Prevent Neural Networks from Overfitting.