done with bugs
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Thomas
parent
0140ef3f37
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abd888e2f8
3 changed files with 112 additions and 35 deletions
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@ -6,7 +6,10 @@
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double sigmoid(double input);
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Matrix* predict(Neural_Network* network, Matrix* image_data);
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double square(double input);
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Matrix * backPropagation(double learning_rate, Matrix* weights, Matrix* biases, Matrix* current_layer_activation, Matrix* previous_layer_activation, Matrix* sigma_old);
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Matrix* sigmoid_derivative(Matrix* matrix);
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Matrix* calculate_weights_delta(Matrix* previous_layer_output, Matrix* delta_matrix);
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void apply_weights(Neural_Network* network, Matrix* delta_weights_matrix, int index);
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Matrix* calculate_delta_hidden(Matrix* next_layer_delta, Matrix* weights, Matrix* current_layer_output);
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Neural_Network* new_network(int input_size, int hidden_size, int hidden_amount, int output_size, double learning_rate){
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Neural_Network* network = malloc(sizeof(Neural_Network));
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@ -154,7 +157,7 @@ Matrix* predict(Neural_Network* network, Matrix* image_data) {
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matrix_free(input);
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return output[network->hidden_amount + 1];
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return output[network->hidden_amount];
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}
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void train_network(Neural_Network* network, Image *image, int label) {
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@ -175,28 +178,41 @@ void train_network(Neural_Network* network, Image *image, int label) {
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input = matrix_add_bias(neuron_activation);
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}
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// calculate the derivative of the sigmoid function of the input of the result layer
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Matrix* ones = matrix_create(output[network->hidden_amount]->rows, output[network->hidden_amount]->columns);
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matrix_fill(ones, 1);
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Matrix* ones_minus_out = subtract(ones, output[network->hidden_amount]);
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Matrix* sigmoid_derivative = multiply(output[network->hidden_amount], ones_minus_out);
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// back propagation
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// create wanted out-put matrix
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//list to store the new weights
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Matrix* delta_weights[network->hidden_amount + 1];
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// calculate the derivative of the sigmoid function of the input of the result layer
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Matrix* sigmoid_prime = sigmoid_derivative(output[network->hidden_amount]);
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// create wanted out-put matrix, calculate the difference and delta values (output layer only)
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Matrix* wanted_output = matrix_create(output[network->hidden_amount]->rows, output[network->hidden_amount]->columns);
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matrix_fill(wanted_output, 0);
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wanted_output->numbers[label][0] = 1;
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// calculate difference to actual out-put
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Matrix* error = subtract(wanted_output, output[network->hidden_amount]);
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Matrix* delta = multiply(sigmoid_prime, error);
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// calculate delta for output layer nodes
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Matrix* delta = multiply(sigmoid_derivative, error);
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//calculate and apply the delta for all weights in out-put layer
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delta_weights[network->hidden_amount] = calculate_weights_delta(output[network->hidden_amount - 1], delta);
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//calculate the delta for all weights
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Matrix* previous_out_with_one = matrix_add_bias(output[network->hidden_amount - 1]);
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Matrix* transposed_previous_out_with_bias = transpose(previous_out_with_one);
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Matrix* weights_delta_matrix = dot(delta, transposed_previous_out_with_bias);
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//hidden layers
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Matrix* previous_delta = delta;
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for (int i = network->hidden_amount; i > 1; i--) {
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delta = calculate_delta_hidden(previous_delta, network->weights[i], output[i - 1]);
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delta_weights[i - 1] = calculate_weights_delta(output[i - 2], delta);
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matrix_free(previous_delta);
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previous_delta = delta;
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}
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// Input Layer
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delta = calculate_delta_hidden(previous_delta, network->weights[1], output[0]);
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delta_weights[0] = calculate_weights_delta(image_data, delta);
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for (int i = 0; i < network->hidden_size ; ++i) {
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apply_weights(network, delta_weights[i], i);
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}
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// De-allocate stuff
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matrix_free(image_data);
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@ -206,22 +222,87 @@ void train_network(Neural_Network* network, Image *image, int label) {
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matrix_free(output[i]);
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}
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matrix_free(ones);
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matrix_free(sigmoid_derivative);
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matrix_free(sigmoid_derivative);
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for (int i = 0; i < network->hidden_amount + 1; ++i) {
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matrix_free(delta_weights[i]);
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}
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matrix_free(sigmoid_prime);
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matrix_free(wanted_output);
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matrix_free(error);
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matrix_free(delta);
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matrix_free(previous_out_with_one);
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matrix_free(weights_delta_matrix);
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matrix_free(previous_delta);
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}
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Matrix * backPropagation(double learning_rate, Matrix* weights, Matrix* biases, Matrix* current_layer_activation, Matrix* previous_layer_activation, Matrix* sigma_old) {
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Matrix* calculate_delta_hidden(Matrix* next_layer_delta, Matrix* weights, Matrix* current_layer_output) {
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return NULL;
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// remove bias weights from weights
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Matrix* weights_without_biases = matrix_create(weights->rows, weights->columns - 1);
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for (int i = 0; i < weights->rows; ++i) {
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for (int j = 0; j < weights->columns - 1; ++j) {
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weights_without_biases->numbers[i][j] = weights->numbers[i][j + 1];
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}
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}
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// transpose the new weights and multiply with deltas
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Matrix* transposed_weight_without_biases = transpose(weights_without_biases);
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Matrix* sum_delta_weights = dot(transposed_weight_without_biases, next_layer_delta);
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//multiply with derivative of current layer output
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Matrix* sigmoid_prime = sigmoid_derivative(current_layer_output);
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// multiply to find deltas for current layer
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Matrix* new_deltas = multiply(sigmoid_prime, sum_delta_weights);
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matrix_free(weights_without_biases);
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matrix_free(transposed_weight_without_biases);
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matrix_free(sum_delta_weights);
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matrix_free(sigmoid_prime);
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return new_deltas;
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}
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void apply_weights(Neural_Network* network, Matrix* delta_weights_matrix, int index) {
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if(index > network->hidden_amount + 1 || index < 0) {
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printf("ERROR: Index out of range! (apply_weights)");
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exit(1);
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}
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if(delta_weights_matrix->rows != network->weights[index]->rows ||
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delta_weights_matrix->columns != network->weights[index]->columns) {
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printf("ERROR: Size of weight matrices do not match! (apply_weights)");
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exit(1);
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}
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for (int i = 0; i < delta_weights_matrix->rows; ++i) {
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for (int j = 0; j < delta_weights_matrix->columns; ++j) {
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network->weights[index]->numbers[i][j] += delta_weights_matrix->numbers[i][j];
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}
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}
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}
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Matrix* calculate_weights_delta(Matrix* previous_layer_output, Matrix* delta_matrix) {
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Matrix* previous_out_with_one = matrix_add_bias(previous_layer_output);
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Matrix* transposed_previous_out_with_bias = transpose(previous_out_with_one);
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Matrix* weights_delta_matrix = dot(delta_matrix, transposed_previous_out_with_bias);
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matrix_free(previous_out_with_one);
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matrix_free(transposed_previous_out_with_bias);
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return weights_delta_matrix;
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}
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Matrix* sigmoid_derivative(Matrix* matrix) {
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Matrix* ones = matrix_create(matrix->rows, matrix->columns);
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matrix_fill(ones, 1);
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Matrix* ones_minus_out = subtract(ones, matrix);
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Matrix* sigmoid_derivative = multiply(matrix, ones_minus_out);
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matrix_free(ones);
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matrix_free(ones_minus_out);
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return sigmoid_derivative;
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}
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double sigmoid(double input) {
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