rawalcher/c-net
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i will nerma

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Tocuro 2023-09-19 14:46:40 +02:00
parent 13540377ff
commit 0c8b2c9044
2 changed files with 34 additions and 3 deletions
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4
.gitignore vendored
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@ -53,4 +53,6 @@ Kernel Module Compile Results
modules.order
Module.symvers
Mkfile.old
dkms.conf
dkms.conf
/.idea/.name
/.idea/misc.xml

33
neuronal_network.c
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@ -8,9 +8,38 @@ void free_network(Neural_Network* network);
void save_network(Neural_Network* network, char* file);
Neural_Network* load_network(char* file);
double predict_images(Neural_Network* network, Image** images, int amount);
double predict_images(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]->image_label) {
num_correct++;
}
matrix_free(prediction);
}
return 1.0 * num_correct / amount;
}
Matrix* predict_image(Neural_Network* network, Image*);
Matrix* predict(Neural_Network* network, Matrix* image_data);
Matrix* predict(Neural_Network* network, Matrix* image_data) {
Matrix* hidden1_inputs = dot(network->weights_1, image_data);
Matrix* hidden1_outputs = apply(relu, hidden1_inputs);
Matrix* hidden2_inputs = dot(network->weights_2, hidden1_outputs);
Matrix* hidden2_outputs = apply(relu, hidden2_inputs);
Matrix* final_inputs = dot(net->output_weights, hidden_outputs);
Matrix* final_outputs = apply(sigmoid, final_inputs);
Matrix* result = softmax(final_outputs);
matrix_free(hidden_inputs);
matrix_free(hidden_outputs);
matrix_free(final_inputs);
matrix_free(final_outputs);
return result;
}
void train_network(Neural_Network* network, Matrix* input, Matrix* output);
void batch_train_network(Neural_Network* network, Image** images, int size);