FeedForward_double_Xor.ino

#define NumberOf(arg) ((unsigned int) (sizeof (arg) / sizeof (arg [0]))) // calculates the number of layers (in this case 4)
#define _1_OPTIMIZE 0B01010010 // https://github.com/GiorgosXou/NeuralNetworks#define-macro-properties

#include <NeuralNetwork.h>

const unsigned int layers[] = {3, 9, 9, 1};
float *output; // 4th layer's output(s)

// Default Test/Input data
const float inputs[8][3] = {
  {0, 0, 0}, // = 0
  {0, 0, 1}, // = 1
  {0, 1, 0}, // = 1
  {0, 1, 1}, // = 0
  {1, 0, 0}, // = 1
  {1, 0, 1}, // = 0
  {1, 1, 0}, // = 0
  {1, 1, 1}  // = 1
};

// 1 for each layer-layer [Pretrained Biases ]
float biases[] = {1, 1, 0.99308};

// It is 3*9 + 9*9 + 9*1  [Pretrained weights]
float weights[] = {
  -0.676266,  3.154561, -1.76689 ,
   1.589422, -2.340522,  1.447924,
   0.291685, -1.222407,  0.669717,
  -1.059862,  2.059782, -1.113708,
  -1.790229,  1.472432, -1.903783,
  -5.094713,  7.437615, -5.033135,
   2.341339,  3.370419,  2.185228,
  -3.887402,  1.453663, -3.861217,
  -1.555083,  2.943702, -0.472324,

  -1.171853, -0.45975 , -0.986132, -0.583541, -1.250889, -1.064349, -0.656225, -0.689616, -0.570443,
  -5.30186 ,  1.078257,  0.864669, -2.917707, -2.280059, -2.018297,  1.577451, -3.758011, -4.153339,
  -0.556209, -0.998336, -0.80149 , -0.232561, -1.087017, -1.286771, -1.034251, -0.05806 , -0.415967,
  -1.475901, -0.039556,  0.144446, -0.485774, -0.041879,  0.955343, -1.492304, -0.577319, -0.466558,
  -0.307791, -0.624868, -0.733248, -0.572921,  1.156592,  9.843138, -2.721857, -0.064086, -1.642469,
  -0.824234, -0.440457,  0.180901, -0.683897, -0.487519,  0.189743, -1.430297,  0.238511, -0.824287,
   0.251094, -3.009409, -1.58829 ,  0.590185,  0.597326, -5.243015,  2.710771,  2.596604,  0.969508,
  -1.344488,  2.618552,  0.642735, -0.947158, -0.286999,  3.797427, -2.443925, -0.833397, -1.654542,
  -0.138234, -0.931373, -0.183022, -0.493784, -0.784119, -0.275703, -2.113665,  0.761188, -0.810006,

  -0.049101, -6.781154,  0.14872 , -2.332737, -4.983434, -1.396086, 10.86302 , -5.551509, -1.648114
};


void setup()
{
  Serial.begin(9600);
  NeuralNetwork NN(layers, weights, biases, NumberOf(layers)); // Creating a NeuralNetwork with pretrained Weights and Biases

  //Goes through all the input arrays
  for (unsigned int i = 0; i < NumberOf(inputs); i++)
  {
    output = NN.FeedForward(inputs[i]); // FeedForwards the input[i]-array through the NN  |  returns the predicted output(s)
    Serial.println(output[0], 7);       // Prints the first 7 digits after the comma
  }
  NN.print();                           // Prints the weights & biases of each layer
}
void loop(){}