Dropout-0.5.9a-pc.zip Official

: It is most effective in large, complex networks where the risk of overfitting is high.

: Dropout is only active during training. During evaluation or production (inference), all neurons are used, but their weights are scaled to account for the missing power during training. Best Practices for Implementation

: Typically, you apply dropout after the activation function of hidden layers.