NeuralNetworkImpl nn = NNFactory.convNN(new int[][] { { 28, 28, 1 }, { 5, 5, 6, 1 }, {2, 2}, {10} }, true);
nn.setLayerCalculator(NNFactory.lcSigmoid(nn, null));
NNFactory.lcMaxPooling(nn);
// MNIST dataset
MnistInputProvider trainInputProvider = new MnistInputProvider("train-images.idx3-ubyte", "train-labels.idx1-ubyte");
trainInputProvider.addInputModifier(new ScalingInputFunction(255));
MnistInputProvider testInputProvider = new MnistInputProvider("t10k-images.idx3-ubyte", "t10k-labels.idx1-ubyte");
testInputProvider.addInputModifier(new ScalingInputFunction(255));
// Backpropagation trainer that also works for convolutional and subsampling layers
BackPropagationTrainer<?> bpt = TrainerFactory.backPropagation(nn, trainInputProvider, testInputProvider, new MultipleNeuronsOutputError(), new NNRandomInitializer(new MersenneTwisterRandomInitializer(-0.01f, 0.01f)), 0.02f, 0.5f, 0f, 0f, 0f, 1, 1, 1);
// log data