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batch train

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Thomas 2023-09-23 21:00:23 +02:00
parent 66ed7afb9f
commit 45f39130c1
3 changed files with 16 additions and 16 deletions
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9
main.c
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@ -7,17 +7,12 @@ int main() {
Image** images = import_images("../data/train-images.idx3-ubyte", "../data/train-labels.idx1-ubyte", NULL, 60000);
// img_visualize(images[0]);
Neural_Network* nn = new_network(28*28, 100, 5, 10, 0.01);
Neural_Network* nn = new_network(28*28, 32, 3, 10, 0.01);
randomize_network(nn, 10);
// save_network(nn);
// Neural_Network* nn = load_network("../networks/test1.txt");
for (int i = 0; i < 60000; ++i) {
train_network(nn, images[i], images[i]->label);
}
// train_network(nn, images[0], images[0]->label);
// train_network(nn, images[0], images[0]->label);
batch_train(nn, images, 20000, 16);
printf("%lf\n", measure_network_accuracy(nn, images, 10000));

22
neuronal_network.c
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@ -7,7 +7,7 @@ double sigmoid(double input);
Matrix* predict(Neural_Network* network, Matrix* image_data);
double square(double input);
Matrix* sigmoid_derivative(Matrix* matrix);
Matrix* calculate_weights_delta(Matrix* previous_layer_output, Matrix* delta_matrix);
Matrix *calculate_weights_delta(Matrix *previous_layer_output, Matrix *delta_matrix, double learning_rate);
void apply_weights(Neural_Network* network, Matrix* delta_weights_matrix, int index);
Matrix* calculate_delta_hidden(Matrix* next_layer_delta, Matrix* weights, Matrix* current_layer_output);
@ -230,13 +230,13 @@ Matrix ** train_network(Neural_Network* network, Image *image, int label) {
Matrix* delta = multiply(sigmoid_prime, error);
//calculate and apply the delta for all weights in out-put layer
delta_weights[network->hidden_amount] = calculate_weights_delta(output[network->hidden_amount - 1], delta);
delta_weights[network->hidden_amount] = calculate_weights_delta(output[network->hidden_amount - 1], delta, network->learning_rate);
//hidden layers
Matrix* previous_delta = delta;
for (int i = network->hidden_amount; i > 1; i--) {
delta = calculate_delta_hidden(previous_delta, network->weights[i], output[i - 1]);
delta_weights[i - 1] = calculate_weights_delta(output[i - 2], delta);
delta_weights[i - 1] = calculate_weights_delta(output[i - 2], delta, network->learning_rate);
matrix_free(previous_delta);
previous_delta = delta;
@ -244,7 +244,7 @@ Matrix ** train_network(Neural_Network* network, Image *image, int label) {
// Input Layer
delta = calculate_delta_hidden(previous_delta, network->weights[1], output[0]);
delta_weights[0] = calculate_weights_delta(image_data, delta);
delta_weights[0] = calculate_weights_delta(image_data, delta, network->learning_rate);
for (int i = 0; i < network->hidden_amount + 1; ++i) {
apply_weights(network, delta_weights[i], i);
@ -258,9 +258,9 @@ Matrix ** train_network(Neural_Network* network, Image *image, int label) {
matrix_free(output[i]);
}
for (int i = 0; i < network->hidden_amount + 1; ++i) {
matrix_free(delta_weights[i]);
}
// for (int i = 0; i < network->hidden_amount + 1; ++i) {
// matrix_free(delta_weights[i]);
// }
matrix_free(sigmoid_prime);
matrix_free(wanted_output);
@ -318,16 +318,20 @@ void apply_weights(Neural_Network* network, Matrix* delta_weights_matrix, int in
}
}
Matrix* calculate_weights_delta(Matrix* previous_layer_output, Matrix* delta_matrix) {
Matrix *calculate_weights_delta(Matrix *previous_layer_output, Matrix *delta_matrix, double learning_rate) {
Matrix* previous_out_with_one = matrix_add_bias(previous_layer_output);
Matrix* transposed_previous_out_with_bias = transpose(previous_out_with_one);
Matrix* weights_delta_matrix = dot(delta_matrix, transposed_previous_out_with_bias);
// scale by learning rate
Matrix* result = scale(weights_delta_matrix, learning_rate);
matrix_free(previous_out_with_one);
matrix_free(transposed_previous_out_with_bias);
matrix_free(weights_delta_matrix);
return weights_delta_matrix;
return result;
}
Matrix* sigmoid_derivative(Matrix* matrix) {

1
neuronal_network.h
View file

@ -26,6 +26,7 @@ Neural_Network* load_network(char* file);
void print_network(Neural_Network* network);
void batch_train(Neural_Network* network, Image** images, int amount, int batch_size);
double measure_network_accuracy(Neural_Network* network, Image** images, int amount);
Matrix* predict_image(Neural_Network* network, Image* image);