cavis/docs/nd4j-nn/templates/updaters.md

title	short_title	description	category	weight
Updaters	Updaters	Special algorithms for gradient descent.	Models	10

What are updaters?

The main difference among the updaters is how they treat the learning rate. Stochastic Gradient Descent, the most common learning algorithm in deep learning, relies on Theta (the weights in hidden layers) and alpha (the learning rate). Different updaters help optimize the learning rate until the neural network converges on its most performant state.

Usage

To use the updaters, pass a new class to the updater() method in either a ComputationGraph or MultiLayerNetwork.

ComputationGraphConfiguration conf = new NeuralNetConfiguration.Builder()
    .updater(new Adam(0.01))
    // add your layers and hyperparameters below
    .build();

Available updaters

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