スケールド指数線形ユニット（SELU）は 処理します used in ニューラルネットワーク, particularly in 深層学習 models. It was introduced to help address issues of vanishing and 爆発勾配 that can occur during training. The SELU function is defined mathematically as follows:
入力に対して x, the output |f(x) - f(y)| は：
f(x) = λ * (x if x > 0 else α * (exp(x) – 1))
ただし：
- λ (lambda)はスケーリング係数で、通常約1.0507に設定されます。
- α (alpha)はパラメータで、通常約1.6733です。
SELU has a unique property of self-normalization, meaning that when used appropriately in a network, it helps maintain the mean and variance of the activations close to zero and one, respectively. This property facilitates faster convergence during training and can improve overall モデルのパフォーマンス.
To effectively use SELU, it is recommended to initialize the weights of the neural network using the LeCun normal initialization method and to avoid dropout layers, as SELU is designed to work best in fully connected architectures without such 正則化手法において.
Overall, the SELU activation function is particularly beneficial for deep networks, as it helps stabilize the training process and can lead to better generalization 未見のデータに対して。