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Network Config
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| MultiLayerNetwork net; | |
| //two hidden layers of 3 neurons each | |
| final int[] LSTMLayers = new int[]{3,3}; | |
| NeuralNetConfiguration.ListBuilder list = new NeuralNetConfiguration.Builder() | |
| .optimizationAlgo(OptimizationAlgorithm.STOCHASTIC_GRADIENT_DESCENT).iterations(1) | |
| .learningRate(learningRate) | |
| .regularization(true).l2(0.0000001) | |
| .seed(76692) | |
| .weightInit(WeightInit.XAVIER) | |
| .updater(Updater.ADAM).adamMeanDecay(0.99).adamVarDecay(0.9999) | |
| .list(); | |
| int layerIdx = 0; | |
| for (; layerIdx < LSTMLayers.length; layerIdx++) { | |
| list = list.layer(layerIdx, new GravesLSTM.Builder().nIn(nIn).nOut(LSTMLayers[layerIdx]) | |
| .activation("softsign").build()); | |
| nIn = LSTMLayers[layerIdx]; | |
| } | |
| list.layer(layerIdx++, new RnnOutputLayer.Builder(LossFunctions.LossFunction.MCXENT).activation("softmax") | |
| .nIn(nIn).nOut(nOut).build()); | |
| MultiLayerConfiguration conf = list.pretrain(false).backprop(true).build(); |
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