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

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

What are activations?

At a simple level, activation functions help decide whether a neuron should be activated. This helps determine whether the information that the neuron is receiving is relevant for the input. The activation function is a non-linear transformation that happens over an input signal, and the transformed output is sent to the next neuron.

Usage

The recommended method to use activations is to add an activation layer in your neural network, and configure your desired activation:

GraphBuilder graphBuilder = new NeuralNetConfiguration.Builder()
	// add hyperparameters and other layers
	.addLayer("softmax", new ActivationLayer(Activation.SOFTMAX), "previous_input")
	// add more layers and output
	.build();

Available activations

{{autogenerated}}