feat: ✨ Create Neural network for XOR

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LICENSE

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+MIT License
+
+Copyright (c) 2024 Louis Gallet
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.

README.md

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+# XOR Neural Network

main.c

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+//
+// Created by Louis Gallet on 24/09/2024.
+//
+
+#include "main.h"
+#include <stdio.h>
+
+
+double sigmoid(double x)
+{
+  if(x > 20) return 1.0;
+  if(x < -20) return 0.0;
+
+  double z = exp(-x);
+  return 1.0 / (1.0 + z);
+}
+
+double sigmoid_derivative(double x)
+{
+  return x * (1.0 - x);
+}
+double init_weights()
+{
+  double placeholder = ((double) rand()) / ((double) RAND_MAX);
+  return placeholder;
+}
+
+void shuffle(int *array, size_t n){
+  if(n > 1) {
+    size_t i;
+    for(i = 0; i < n - 1; i++) {
+      size_t j = i + rand() / (RAND_MAX / (n - i) + 1);
+      int t = array[j];
+      array[j] = array[i];
+      array[i] = t;
+    }
+  }
+}
+
+void backup_weights(char filename[], double hiddenWeights[NUM_INPUTS][NUM_HIDDEN], double hiddenLayerBias[NUM_HIDDEN],
+                    double outputWeights[NUM_HIDDEN][NUM_OUTPUTS], double outputLayerBias[NUM_OUTPUTS])
+{
+  FILE *fpt;
+  fpt = fopen(filename, "w+");
+  for(int j = 0; j < NUM_HIDDEN; j++){
+    for(int k = 0; k < NUM_INPUTS; k++){
+      fprintf(fpt, "%f,", hiddenWeights[k][j]);
+    }
+  }
+  printf("Final Hidden Biases: \n");
+  for(int j = 0; j < NUM_HIDDEN; j++){
+    fprintf(fpt, "%f,", hiddenLayerBias[j]);
+  }
+  printf("Final Output Weights: \n");
+  for(int j = 0; j < NUM_OUTPUTS; j++){
+    for(int k = 0; k < NUM_HIDDEN; k++){
+      fprintf(fpt, "%f,", outputWeights[k][j]);
+    }
+  }
+  printf("Final Output Biases: \n");
+  for(int j = 0; j < NUM_OUTPUTS; j++){
+    fprintf(fpt, "%f,", outputLayerBias[j]);
+  }
+
+
+
+}
+
+
+int main(){
+  //learning rate
+  const double lr = 0.1f;
+
+  double hiddenLayer[NUM_HIDDEN];
+  double outputLayer[NUM_OUTPUTS];
+
+  double hiddenLayerBias[NUM_HIDDEN];
+  double outputLayerBias[NUM_OUTPUTS];
+
+  double hiddenWeights[NUM_INPUTS][NUM_HIDDEN];
+  double outputWeights[NUM_HIDDEN][NUM_OUTPUTS];
+
+  double trainingInputs[NUM_TRAINING_SETS][NUM_INPUTS] = {{0.0f, 0.0f}, {1.0f, 0.0f},
+                                                          {0.0f, 1.0f}, {1.0f, 1.0f}};
+
+  double trainingOutputs[NUM_TRAINING_SETS][NUM_OUTPUTS] = {{0.0f}, {1.0f}, {1.0f}, {0.0f}};
+
+  for(int i = 0; i < NUM_INPUTS; i++){
+    for(int j = 0; j < NUM_HIDDEN; j++){
+      hiddenWeights[i][j] = init_weights();
+    }
+  }
+
+  for(int i = 0; i < NUM_HIDDEN; i++){
+    for(int j = 0; j < NUM_OUTPUTS; j++){
+      outputWeights[i][j] = init_weights();
+    }
+  }
+
+  for(int i = 0; i < NUM_OUTPUTS; i++){
+    outputLayerBias[i] = init_weights();
+  }
+
+  for(int i = 0; i < NUM_HIDDEN; i++){
+    hiddenLayerBias[i] = init_weights();
+  }
+
+  int trainingSetOrder[] = {0,1,2,3};
+
+  int numEpochs = 100000;
+
+  //training loop
+  for(int epoch = 0; epoch < numEpochs; epoch++){
+    shuffle(trainingSetOrder, NUM_TRAINING_SETS);
+
+    for(int x = 0; x < NUM_TRAINING_SETS; x++){
+      int i = trainingSetOrder[x];
+
+      //forward pass
+      //compute hidden layer activation
+      for(int j = 0; j < NUM_HIDDEN; j++){
+        double activation = hiddenLayerBias[j];
+
+        for(int k = 0; k < NUM_INPUTS; k++){
+          activation += trainingInputs[i][k] * hiddenWeights[k][j];
+        }
+        //printf("Hidden layer activation[%d] = %f\n", j, activation);
+        hiddenLayer[j] = sigmoid(activation);
+
+        if(isnan(hiddenLayer[j])){
+          //printf("NaN in hidden layer at index %d\n", j);
+        }
+      }
+
+      //compute output layer activation
+      for(int j = 0; j < NUM_OUTPUTS; j++){
+        double activation = outputLayerBias[j];
+        for(int k = 0; k < NUM_HIDDEN; k++){
+          activation += hiddenLayer[k] * outputWeights[k][j];
+        }
+        //printf("Output layer activation[%d] = %f\n", j, activation);
+        outputLayer[j] = sigmoid(activation);
+        if(isnan(outputLayer[j])){
+          //printf("NaN in output layer at index %d\n", j);
+        }
+      }
+
+      printf("Input: %g %g    Output: %g    Predicted Output: %g\n", trainingInputs[i][0], trainingInputs[i][1], outputLayer[0], trainingOutputs[i][0]);
+
+      //Back prop
+      //compute change in output weights
+
+      double deltaOutput[NUM_OUTPUTS];
+
+      for(int j = 0; j < NUM_OUTPUTS; j++){
+        double error = (trainingOutputs[i][j] - outputLayer[j]);
+        deltaOutput[j] = error * sigmoid_derivative(outputLayer[j]);
+        if(isnan(deltaOutput[j])){
+          //printf("NaN in deltaOutput at index %d\n", j);
+        }
+      }
+
+      //compute change in hidden weights
+
+      double deltaHidden[NUM_HIDDEN];
+
+      for(int j = 0; j < NUM_HIDDEN; j++){
+        double error = 0.0f;
+        for(int k = 0; k < NUM_OUTPUTS; k++){
+          error += deltaOutput[k] * outputWeights[j][k];
+        }
+        deltaHidden[j] = error * sigmoid_derivative(hiddenLayer[j]);
+        if(isnan(deltaHidden[j])){
+          //printf("NaN in deltaHidden at index %d\n", j);
+        }
+      }
+
+      //apply change in output weights
+      for(int j = 0; j < NUM_OUTPUTS; j++){
+        outputLayerBias[j] += deltaOutput[j] * lr;
+
+        for(int k = 0; k < NUM_HIDDEN; k++){
+          outputWeights[k][j] += hiddenLayer[k] * deltaOutput[j] * lr;
+          if(isnan(outputWeights[k][j])){
+            //printf("NaN in outputWeights at [%d][%d]\n", k, j);
+          }
+        }
+      }
+
+      for(int j = 0; j < NUM_HIDDEN; j++){
+        hiddenLayerBias[j] += deltaHidden[j] * lr;
+        for(int k = 0; k < NUM_INPUTS; k++){
+          hiddenWeights[k][j] += trainingInputs[i][k] * deltaHidden[j] * lr;
+          if(isnan(hiddenWeights[k][j])){
+            //printf("NaN in hiddenWeights at [%d][%d]\n", k, j);
+          }
+        }
+      }
+    }
+  }
+  //print final weights after training
+      printf("\nFinal Hidden Weights: \n");
+      for(int j = 0; j < NUM_HIDDEN; j++){
+        for(int k = 0; k < NUM_INPUTS; k++){
+          printf("%f ", hiddenWeights[k][j]);
+        }
+      }
+
+      printf("\n");
+
+      printf("Final Hidden Biases: \n");
+      for(int j = 0; j < NUM_HIDDEN; j++){
+        printf("%f ", hiddenLayerBias[j]);
+      }
+
+      printf("\n\n\n");
+
+      printf("Final Output Weights: \n");
+      for(int j = 0; j < NUM_OUTPUTS; j++){
+        for(int k = 0; k < NUM_HIDDEN; k++){
+          printf("%f ", outputWeights[k][j]);
+        }
+      }
+
+      printf("\n");
+
+
+      printf("Final Output Biases: \n");
+      for(int j = 0; j < NUM_OUTPUTS; j++){
+        printf("%f ", outputLayerBias[j]);
+      }
+
+  backup_weights("weights.csv", hiddenWeights, hiddenLayerBias, outputWeights, outputLayerBias);
+
+  return 0;
+
+}

main.h

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+//
+// Created by Louis Gallet on 24/09/2024.
+//
+
+#ifndef MAIN_H
+#define MAIN_H
+#include <sys/_types/_size_t.h>
+#include <math.h>
+#include <stdlib.h>
+
+#define NUM_INPUTS 2
+#define NUM_HIDDEN 2
+#define NUM_OUTPUTS 1
+#define NUM_TRAINING_SETS 4
+
+double sigmoid(double x);
+double sigmoid_derivative(double x);
+double init_weights();
+void shuffle(int *array, size_t n);
+void backup_weights(char filename[], double hiddenWeights[NUM_INPUTS][NUM_HIDDEN], double hiddenLayerBias[NUM_HIDDEN],
+                    double outputWeights[NUM_HIDDEN][NUM_OUTPUTS], double outputLayerBias[NUM_OUTPUTS]);
+
+
+int main();
+
+#endif //MAIN_H

use.c

@@ -0,0 +1,98 @@
+#include <stdio.h>
+#include <stdlib.h>
+#include <math.h>
+#include <err.h>
+
+// Function prototypes
+void getvalues(char filename[], double **HiddenWeights, double *HiddenBiases, double **FinalOutputWeights, double *FinalOutputBiases);
+double sigmoid(double x);
+double sigmoid_derivative(double x);
+
+int main() {
+    // Allocate memory for weights and biases
+    double **HiddenWeights = (double **)malloc(2 * sizeof(double *));
+    for (int i = 0; i < 2; i++) {
+        HiddenWeights[i] = (double *)malloc(2 * sizeof(double));
+    }
+    double *HiddenBiases = (double *)malloc(2 * sizeof(double));
+    double **FinalOutputWeights = (double **)malloc(2 * sizeof(double *));
+    for (int i = 0; i < 2; i++) {
+        FinalOutputWeights[i] = (double *)malloc(1 * sizeof(double));
+    }
+    double *FinalOutputBiases = (double *)malloc(1 * sizeof(double));
+
+    // Load weights and biases from CSV file
+    getvalues("weights.csv", HiddenWeights, HiddenBiases, FinalOutputWeights, FinalOutputBiases);
+
+    // Prompt user for input values
+    double input1, input2;
+    printf("Enter two values to compute XOR: ");
+    scanf("%lf %lf", &input1, &input2);
+
+    // Perform forward pass
+    double hiddenLayer[2];
+    double outputLayer[1];
+
+    // Compute hidden layer activation
+    for (int j = 0; j < 2; j++) {
+        double activation = HiddenBiases[j];
+        activation += input1 * HiddenWeights[0][j];
+        activation += input2 * HiddenWeights[1][j];
+        hiddenLayer[j] = sigmoid(activation);
+    }
+
+    // Compute output layer activation
+    for (int j = 0; j < 1; j++) {
+        double activation = FinalOutputBiases[j];
+        activation += hiddenLayer[0] * FinalOutputWeights[0][j];
+        activation += hiddenLayer[1] * FinalOutputWeights[1][j];
+        outputLayer[j] = sigmoid(activation);
+    }
+
+    // Print the result
+    printf("Input: %g %g    Predicted Output: %g     Output round: %g\n", input1, input2, outputLayer[0], round(outputLayer[0]));
+
+    // Free allocated memory
+    for (int i = 0; i < 2; i++) {
+        free(HiddenWeights[i]);
+        free(FinalOutputWeights[i]);
+    }
+    free(HiddenWeights);
+    free(HiddenBiases);
+    free(FinalOutputWeights);
+    free(FinalOutputBiases);
+
+    return 0;
+}
+
+void getvalues(char filename[], double **HiddenWeights, double *HiddenBiases, double **FinalOutputWeights, double *FinalOutputBiases) {
+    FILE *fp = fopen(filename, "r");
+    if (fp == NULL) {
+        errx(EXIT_FAILURE, "Could not open file %s", filename);
+    }
+    for (int i = 0; i < 2; i++) {
+        for (int j = 0; j < 2; j++) {
+            fscanf(fp, "%lf,", &HiddenWeights[i][j]);
+        }
+    }
+    for (int i = 0; i < 2; i++) {
+        fscanf(fp, "%lf,", &HiddenBiases[i]);
+    }
+    for (int i = 0; i < 2; i++) {
+        for (int j = 0; j < 1; j++) {
+            fscanf(fp, "%lf,", &FinalOutputWeights[i][j]);
+        }
+    }
+    for (int i = 0; i < 1; i++) {
+        fscanf(fp, "%lf,", &FinalOutputBiases[i]);
+    }
+    fclose(fp);
+}
+
+double sigmoid(double x) {
+    return 1.0 / (1.0 + exp(-x));
+}
+
+double sigmoid_derivative(double x) {
+    return x * (1.0 - x);
+}

use.h

@@ -0,0 +1,18 @@
+//
+// Created by Louis Gallet on 27/09/2024.
+//
+
+#ifndef USE_H
+#define USE_H
+
+#define NUM_INPUTS 2
+#define NUM_HIDDEN 2
+#define NUM_OUTPUTS 1
+#define NUM_TRAINING_SETS 4
+
+
+void getvalues(char filename[], double **HiddenWeights, double *HiddenBiases, double **FinalOutputWeights, double *FinalOutputBiases);
+int main();
+
+
+#endif //USE_H