Environment.getInstance().setUseDataSharedMemory(true);
Environment.getInstance().setUseWeightsSharedMemory(true);
// CNN
NeuralNetworkImpl cnn = NNFactory.convNN(new int[][] { { 2, 1, 1 }, { 1, 1 }, { 4 }, {1} }, false);
cnn.setLayerCalculator(NNFactory.lcSigmoid(cnn, null));
NNFactory.lcMaxPooling(cnn);
FullyConnected cnnfci = (FullyConnected) cnn.getOutputLayer().getConnections().get(0).getInputLayer().getConnections().get(0);
cnnfci.getWeights().set(0.02f, 0, 0);
cnnfci.getWeights().set(0.01f, 1, 0);
cnnfci.getWeights().set(0.03f, 2, 0);
cnnfci.getWeights().set(0.001f, 3, 0);
cnnfci.getWeights().set(0.005f, 0, 1);
cnnfci.getWeights().set(0.04f, 1, 1);
cnnfci.getWeights().set(0.02f, 2, 1);
cnnfci.getWeights().set(0.009f, 3, 1);
FullyConnected cnnfco = (FullyConnected) cnn.getOutputLayer().getConnections().get(0);
cnnfco.getWeights().set(0.05f, 0, 0);
cnnfco.getWeights().set(0.08f, 0, 1);
// MLP
NeuralNetworkImpl mlp = NNFactory.mlpSigmoid(new int[] { 2, 4, 1 }, false);
FullyConnected mlpfci = (FullyConnected) mlp.getOutputLayer().getConnections().get(0).getInputLayer().getConnections().get(0);
mlpfci.getWeights().set(0.02f, 0, 0);
mlpfci.getWeights().set(0.01f, 1, 0);
mlpfci.getWeights().set(0.03f, 2, 0);
mlpfci.getWeights().set(0.001f, 3, 0);
mlpfci.getWeights().set(0.005f, 0, 1);
mlpfci.getWeights().set(0.04f, 1, 1);
mlpfci.getWeights().set(0.02f, 2, 1);
mlpfci.getWeights().set(0.009f, 3, 1);
FullyConnected mlpfco = (FullyConnected) mlp.getOutputLayer().getConnections().get(0);
mlpfco.getWeights().set(0.05f, 0, 0);
mlpfco.getWeights().set(0.08f, 0, 1);
// compare bp
SimpleInputProvider inputProvider = new SimpleInputProvider(new float[][] { {0, 0}, {0, 1}, {1, 0}, {1, 1} }, new float[][] { {0}, {1}, {1}, {0} });