| | |
| | | #define NETWORK_H |
| | | |
| | | #include "image.h" |
| | | #include "detection_layer.h" |
| | | #include "layer.h" |
| | | #include "data.h" |
| | | #include "params.h" |
| | | |
| | | typedef enum { |
| | | CONSTANT, STEP, EXP, POLY, STEPS, SIG |
| | | } learning_rate_policy; |
| | | |
| | | typedef struct { |
| | | typedef struct network{ |
| | | int n; |
| | | int batch; |
| | | int *seen; |
| | | float epoch; |
| | | int subdivisions; |
| | | float momentum; |
| | | float decay; |
| | |
| | | #endif |
| | | } network; |
| | | |
| | | typedef struct network_state { |
| | | float *truth; |
| | | float *input; |
| | | float *delta; |
| | | int train; |
| | | int index; |
| | | network net; |
| | | } network_state; |
| | | |
| | | #ifdef GPU |
| | | float train_network_datum_gpu(network net, float *x, float *y); |
| | | float *network_predict_gpu(network net, float *input); |
| | |
| | | float *get_network_output_gpu(network net); |
| | | void forward_network_gpu(network net, network_state state); |
| | | void backward_network_gpu(network net, network_state state); |
| | | void update_network_gpu(network net); |
| | | #endif |
| | | |
| | | float get_current_rate(network net); |
| | |
| | | matrix network_predict_data(network net, data test); |
| | | float *network_predict(network net, float *input); |
| | | float network_accuracy(network net, data d); |
| | | float *network_accuracies(network net, data d); |
| | | float *network_accuracies(network net, data d, int n); |
| | | float network_accuracy_multi(network net, data d, int n); |
| | | void top_predictions(network net, int n, int *index); |
| | | float *get_network_output(network net); |
| | |
| | | void set_batch_network(network *net, int b); |
| | | int get_network_input_size(network net); |
| | | float get_network_cost(network net); |
| | | detection_layer get_network_detection_layer(network net); |
| | | |
| | | int get_network_nuisance(network net); |
| | | int get_network_background(network net); |