Java Deep Learning Cookbook

How to do it...

1. Choose an activation function according to the network layers: We need to know the activation functions to be used for the input/hidden layers and output layers. Use ReLU for input/hidden layers preferably.
2. Choose the right activation function to handle data impurities: Inspect the data that you feed to the neural network. Do you have inputs with a majority of negative values observing dead neurons? Choose the appropriate activation functions accordingly. Use Leaky ReLU if dead neurons are observed during training.
3. Choose the right activation function to handle overfitting: Observe the evaluation metrics and their variation for each training period. Understand gradient behavior and how well your model performs on new unseen ...