Java Deep Learning Projects

Network training

First, we create a MultiLayerNetwork  using the preceding MultiLayerConfiguration. Then we initialize the network and start the training on the training set:

MultiLayerNetwork model = new MultiLayerNetwork(LSTMconf);model.init();log.info("Train model....");for(int i=0; i<numEpochs; i++ ){    model.fit(trainingDataIt); }

Typically, this type of network has so many hyperparameters. Let's print the number of parameters in the network (and for each layer):

Layer[] layers = model.getLayers();int totalNumParams = 0;for( int i=0; i<layers.length; i++ ){         int nParams = layers[i].numParams();        System.out.println("Number of parameters in layer " + i + ": " + nParams);       totalNumParams += nParams;}System.out.println("Total number of network ...