From 46a51b36bb70f9f38c8488425581157f31a6d3c2 Mon Sep 17 00:00:00 2001 From: Thomas Schleicher Date: Thu, 21 Sep 2023 18:52:06 +0200 Subject: [PATCH] small changes --- neuronal_network.c | 31 ++++++++++++++++++------------- 1 file changed, 18 insertions(+), 13 deletions(-) diff --git a/neuronal_network.c b/neuronal_network.c index 3261857..533fce1 100644 --- a/neuronal_network.c +++ b/neuronal_network.c @@ -12,7 +12,7 @@ double square(double input); double loss_function(Matrix* output_matrix, int image_label); -void backPropagation(double learning_rate, Matrix* weights, Matrix* biases, Matrix* current_layer_activation, Matrix* previous_layer_activation, Matrix* sigma_old); +Matrix* backPropagation(double learning_rate, Matrix* weights, Matrix* biases, Matrix* current_layer_activation, Matrix* previous_layer_activation, Matrix* sigma_old); Neural_Network* new_network(int input_size, int hidden_size, int output_size, double learning_rate){ Neural_Network *network = malloc(sizeof(Neural_Network)); @@ -226,20 +226,20 @@ void train_network(Neural_Network* network, Image *image, int label) { Matrix* final_outputs = apply(sigmoid, final_add); // begin backpropagation - Matrix* sigma = matrix_create(final_outputs->rows, 1); - matrix_fill(sigma, 1); - Matrix* temp1 = subtract(sigma, final_outputs); + Matrix* sigma1 = matrix_create(final_outputs->rows, 1); + matrix_fill(sigma1, 1); + Matrix* temp1 = subtract(sigma1, final_outputs); Matrix* temp2 = multiply(temp1, final_outputs); // * soll-ist Matrix* temp3 = matrix_create(final_outputs->rows, final_outputs->columns); matrix_fill(temp3, 0); temp3->numbers[label][0] = 1; Matrix* temp4 = subtract(temp3, final_outputs); - sigma = multiply(temp2, temp4); + sigma1 = multiply(temp2, temp4); Matrix* temp5 = transpose(h3_outputs); - Matrix* temp6 = dot(sigma, temp5); + Matrix* temp6 = dot(sigma1, temp5); Matrix* weights_delta = scale(temp6, network->learning_rate); - Matrix* bias_delta = scale(sigma, network->learning_rate); + Matrix* bias_delta = scale(sigma1, network->learning_rate); Matrix* temp7 = add(weights_delta, network->weights_output); matrix_free(network->weights_output); @@ -250,9 +250,9 @@ void train_network(Neural_Network* network, Image *image, int label) { network->bias_output = temp8; // other levels - backPropagation(network->learning_rate, network->weights_3, network->bias_3, h3_outputs, h2_outputs, sigma); - backPropagation(network->learning_rate, network->weights_2, network->bias_2, h2_outputs, h1_outputs, sigma); - backPropagation(network->learning_rate, network->weights_1, network->bias_1, h1_outputs, input, sigma); + Matrix* sigma2 = backPropagation(network->learning_rate, network->weights_3, network->bias_3, h3_outputs, h2_outputs, sigma1); + Matrix* sigma3 = backPropagation(network->learning_rate, network->weights_2, network->bias_2, h2_outputs, h1_outputs, sigma2); + Matrix* sigma4 = backPropagation(network->learning_rate, network->weights_1, network->bias_1, h1_outputs, input, sigma3); matrix_free(input); @@ -275,7 +275,11 @@ void train_network(Neural_Network* network, Image *image, int label) { matrix_free(weights_delta); matrix_free(bias_delta); - matrix_free(sigma); + matrix_free(sigma1); + matrix_free(sigma2); + matrix_free(sigma3); + matrix_free(sigma4); + matrix_free(temp1); matrix_free(temp2); matrix_free(temp3); @@ -286,7 +290,7 @@ void train_network(Neural_Network* network, Image *image, int label) { matrix_free(temp8); } -void backPropagation(double learning_rate, Matrix* weights, Matrix* biases, Matrix* current_layer_activation, Matrix* previous_layer_activation, Matrix* sigma_old) { +Matrix* backPropagation(double learning_rate, Matrix* weights, Matrix* biases, Matrix* current_layer_activation, Matrix* previous_layer_activation, Matrix* sigma_old) { Matrix* sigma_new = matrix_create(current_layer_activation->rows, 1); matrix_fill(sigma_new, 1); @@ -318,7 +322,6 @@ void backPropagation(double learning_rate, Matrix* weights, Matrix* biases, Matr biases = temp6; matrix_free(sigma_old); - sigma_old = sigma_new; matrix_free(temp1); matrix_free(temp2); @@ -328,6 +331,8 @@ void backPropagation(double learning_rate, Matrix* weights, Matrix* biases, Matr matrix_free(temp6); matrix_free(weights_delta); matrix_free(bias_delta); + + return sigma_new; }