{

const int inputSize = 2;

const int hiddenSize = 3;

const int outputSize = 1;

const int numSamples = 4;

const double lr = 0.8;

float hInput[] = {1, 1,

0, 0,

0, 1,

1, 0};

float hOutput[] = {0,

0,

1,

1};

af::array in(inputSize, numSamples, hInput);

af::array out(outputSize, numSamples, hOutput);

FFNet network(inputSize);

network.addLinearNode(hiddenSize, 5).addActivationNode();

network.addLinearNode(outputSize, 5).addActivationNode();

for (int i = 0; i < 1000; i++) {

ArrayVector data = network.forward({in});

double err = af::norm(data[0] - out);

data[0] = out - data[0];

if ((i + 1) % 100 == 0) {

printf("Error at iteration(%d) : %2.10lf\n", i + 1, err);

}

network.backward({in}, data);

network.update(lr);

}

af_print(af::round(network.forward({in})[0]));