gistfile1.txt

        new Thread(() -> {
            MultiLayerConfiguration conf = new NeuralNetConfiguration.Builder()
                    .optimizationAlgo(OptimizationAlgorithm.STOCHASTIC_GRADIENT_DESCENT)
                    .weightInit(WeightInit.XAVIER)
                    .updater(new Sgd(0.1))
                    .list()
                    .layer(0, new DenseLayer.Builder()
                            .nIn(100)
                            .nOut(100)
                            .activation(Activation.LEAKYRELU)
                            .build())
                    .layer(1, new DenseLayer.Builder()
                            .nIn(100)
                            .nOut(100)
                            .activation(Activation.LEAKYRELU)
                            .build())
                    .layer(2, new OutputLayer.Builder(LossFunctions.LossFunction.MCXENT)
                            .nIn(100)
                            .nOut(3)
                            .activation(Activation.SOFTMAX)
                            .build())
                    .build();

            MultiLayerNetwork model = new MultiLayerNetwork(conf);
            model.init();

            INDArray inputs = Nd4j.rand(10_000, 100);
            INDArray outputs = Nd4j.rand(10_000, 3);

            for (int i = 0; i < 10_000; i++) {
                model.fit(inputs, outputs);
            }
        }).start();
        new Thread(() -> {
            MultiLayerConfiguration conf = new NeuralNetConfiguration.Builder()
                    .optimizationAlgo(OptimizationAlgorithm.STOCHASTIC_GRADIENT_DESCENT)
                    .weightInit(WeightInit.XAVIER)
                    .updater(new Sgd(0.1))
                    .list()
                    .layer(0, new DenseLayer.Builder()
                            .nIn(100)
                            .nOut(100)
                            .activation(Activation.LEAKYRELU)
                            .build())
                    .layer(1, new DenseLayer.Builder()
                            .nIn(100)
                            .nOut(100)
                            .activation(Activation.LEAKYRELU)
                            .build())
                    .layer(2, new OutputLayer.Builder(LossFunctions.LossFunction.MCXENT)
                            .nIn(100)
                            .nOut(3)
                            .activation(Activation.SOFTMAX)
                            .build())
                    .build();

            MultiLayerNetwork model = new MultiLayerNetwork(conf);
            model.init();

            INDArray inputs = Nd4j.rand(10_000, 100);
            INDArray outputs = Nd4j.rand(10_000, 3);

            for (int i = 0; i < 10_000; i++) {
                model.fit(inputs, outputs);
            }
        }).start();