Java Deep Learning Cookbook

How it works...

While constructing the output layer, make note of the nOut value of the preceding LSTM input layer. This will be taken as nIn for the output layer. nIn should be the same as nOut of the preceding LSTM input layer.

In steps 1 and step 2, we are essentially creating an LSTM neural network, an extended version of a regular RNN. We used gated cells to have some sort of internal memory to hold long-term dependencies. For a predictive model to make predictions (patient mortality), we need to have probability produced by the output layer. In step 2, we see that SOFTMAX is used at the output layer of a neural network. This activation function is very helpful for computing the probability for the specific label. MCXENT is the ND4J ...