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test

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Thomas 2023-09-23 21:33:51 +02:00
parent 45f39130c1
commit accb195dd9
4 changed files with 67 additions and 47 deletions
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95
neuronal_network.c
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@ -117,11 +117,20 @@ void print_network(Neural_Network* network) {
double measure_network_accuracy(Neural_Network* network, Image** images, int amount) {
int num_correct = 0;
for (int i = 0; i < amount; i++) {
Matrix* prediction = predict_image(network, images[i]);
if (matrix_argmax(prediction) == images[i]->label) {
matrix_print(prediction);
printf("Label: %c\n", images[i]->label);
int guess = matrix_argmax(prediction);
int answer = (unsigned char) images[i]->label;
if (guess == answer) {
num_correct++;
}
matrix_free(prediction);
}
return ((double) num_correct) / amount;
@ -160,43 +169,47 @@ Matrix* predict(Neural_Network* network, Matrix* image_data) {
return output[network->hidden_amount];
}
void batch_train(Neural_Network* network, Image** images, int amount, int batch_size) {
//void batch_train(Neural_Network* network, Image** images, int amount, int batch_size) {
//
// for (int i = 0; i < amount; ++i) {
//
// if(amount % 1000 == 0) {
// printf("1k pics!\n");
// }
//
// Matrix* batch_weights[network->hidden_amount + 1];
//
// for (int j = 0; j < batch_size; ++j) {
// Matrix** delta_weights = train_network(network, images[i], images[i]->label);
//
// for (int k = 0; k < network->hidden_amount + 1; k++) {
// if(j == 0) {
// batch_weights[k] = delta_weights[k];
// continue;
// }
//
// Matrix* temp_result = add(batch_weights[k], delta_weights[k]);
//
// matrix_free(batch_weights[k]);
// matrix_free(delta_weights[k]);
//
// batch_weights[k] = temp_result;
// }
//
// free(delta_weights);
// }
//
// for (int j = 0; j < network->hidden_amount + 1; ++j) {
// Matrix* average_delta_weight = scale(batch_weights[j], (1.0 / batch_size));
// apply_weights(network, average_delta_weight, j);
//
// matrix_free(average_delta_weight);
// matrix_free(batch_weights[j]);
// }
// }
//}
for (int i = 0; i < amount; ++i) {
Matrix* batch_weights[network->hidden_amount + 1];
for (int j = 0; j < batch_size; ++j) {
Matrix** delta_weights = train_network(network, images[i], images[i]->label);
for (int k = 0; k < network->hidden_amount + 1; k++) {
if(j == 0) {
batch_weights[k] = delta_weights[k];
continue;
}
Matrix* temp_result = add(batch_weights[k], delta_weights[k]);
matrix_free(batch_weights[k]);
matrix_free(delta_weights[k]);
batch_weights[k] = temp_result;
}
free(delta_weights);
}
for (int j = 0; j < network->hidden_amount + 1; ++j) {
Matrix* average_delta_weight = scale(batch_weights[j], (1.0 / batch_size));
apply_weights(network, average_delta_weight, j);
matrix_free(average_delta_weight);
matrix_free(batch_weights[j]);
}
}
}
Matrix ** train_network(Neural_Network* network, Image *image, int label) {
void train_network(Neural_Network* network, Image *image, int label) {
Matrix* image_data = matrix_flatten(image->pixel_values, 0);
Matrix* input = matrix_add_bias(image_data);
@ -258,9 +271,9 @@ 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);
@ -268,7 +281,7 @@ Matrix ** train_network(Neural_Network* network, Image *image, int label) {
matrix_free(delta);
matrix_free(previous_delta);
return delta_weights;
// return delta_weights;
}
Matrix* calculate_delta_hidden(Matrix* next_layer_delta, Matrix* weights, Matrix* current_layer_output) {