Dropout regularization technique
时间: 2023-09-23 21:09:26 浏览: 40
Dropout regularization is a technique used in machine learning and deep learning to prevent overfitting in neural networks. It works by randomly dropping out (i.e., setting to zero) some of the neurons in a layer during training. This forces the remaining neurons to learn more robust and independent features, and prevents them from relying too heavily on any one particular feature.
The dropout technique helps the model generalize better by reducing the correlation between the neurons, which in turn reduces the overfitting of the model. Dropout is applied during training, and during inference all neurons are used, but their outputs are scaled down by the dropout probability.
The dropout probability is a hyperparameter that controls the percentage of neurons that are dropped out during training. A common value for the dropout probability is 0.5, meaning that each neuron has a 50% chance of being dropped out during each training iteration.
Overall, dropout regularization is a powerful and widely-used technique for improving the performance and generalization of neural networks.