训练神经网络(上)——激活函数、数据 预处理
文章目录
Activateion
Sigmoid
σ ( x ) = 1 1 + e − x \sigma(x) = \frac{1}{1 + e^{-x}} σ(x)=1+e−x1
- Squashes numbers to range [0,1]
- Historically popular
3 problems:
- Saturated neurons kill the gradient
- Sigmoid outputs are not zero-centered
- exp() is a bit compute expensive
tanh(x)
- Squanshes numbers to range [-1, 1]
- zero centered