Java Deep Learning Projects

Output layer

The number of input neurons is equal to the output of the hidden layer 1. Then the number of outputs is equal to the number of predicted labels. We set a smaller value yet again, considering a very few inputs and features.

Here we used the Softmax activation function, which gives us a probability distribution over classes (the outputs sum to 1.0), and the losses function as cross-entropy for binary classification (XNET) since we want to convert the output (probability) to a discrete class, that is, zero or one:

OutputLayer output_layer = new OutputLayer.Builder(LossFunction.XENT) // XENT for Binary Classification                .weightInit(WeightInit.XAVIER)                .activation(Activation.SOFTMAX)                .nIn(16).nOut(numOutputs)                .build();

XNET is used for ...