Recurrent Neural Networks in DL4J

This document outlines the specifics training features and the practicalities of how to use them in DeepLearning4J. This document assumes some familiarity with recurrent neural networks and their use – it is not an introduction to recurrent neural networks, and assumes some familiarity with their both their use and terminology. If you are new to RNNs, read A Beginner’s Guide to Recurrent Networks and LSTMs before proceeding with this page. Contents DL4J currently supports one main type of recurrent neural network – the LSTM (Long Short-Term Memory) model (class name: GravesLSTM), though many more are planned for the future. Data for RNNs Consider for the moment a standard feed-forward network (a multi-layer perceptron or ‘DenseLayer’ in DL4J). These networks expect input and output data that is two-dimensional: that is, data…


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