| | |
| | | #include "data.h" |
| | | |
| | | typedef enum { |
| | | CONSTANT, STEP, EXP, POLY |
| | | CONSTANT, STEP, EXP, POLY, STEPS, SIG |
| | | } learning_rate_policy; |
| | | |
| | | typedef struct { |
| | | int n; |
| | | int batch; |
| | | int *seen; |
| | | float epoch; |
| | | int subdivisions; |
| | | float momentum; |
| | | float decay; |
| | |
| | | |
| | | float learning_rate; |
| | | float gamma; |
| | | float scale; |
| | | float power; |
| | | int step; |
| | | int max_batches; |
| | | float *scales; |
| | | int *steps; |
| | | int num_steps; |
| | | |
| | | int inputs; |
| | | int h, w, c; |
| | |
| | | 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); |