| | |
| | | CONNECTED, |
| | | MAXPOOL, |
| | | SOFTMAX, |
| | | NORMALIZATION |
| | | NORMALIZATION, |
| | | DROPOUT, |
| | | CROP |
| | | } LAYER_TYPE; |
| | | |
| | | typedef struct { |
| | | int n; |
| | | int batch; |
| | | float learning_rate; |
| | | float momentum; |
| | | float decay; |
| | | void **layers; |
| | | LAYER_TYPE *types; |
| | | int outputs; |
| | |
| | | #endif |
| | | } network; |
| | | |
| | | #ifdef GPU |
| | | void forward_network_gpu(network net, cl_mem input, int train); |
| | | #endif |
| | | |
| | | network make_network(int n, int batch); |
| | | void forward_network(network net, float *input, int train); |
| | | float backward_network(network net, float *input, float *truth); |
| | | void update_network(network net, float step, float momentum, float decay); |
| | | float train_network_sgd(network net, data d, int n, float step, float momentum,float decay); |
| | | float train_network_batch(network net, data d, int n, float step, float momentum,float decay); |
| | | void train_network(network net, data d, float step, float momentum, float decay); |
| | | void update_network(network net); |
| | | float train_network_sgd(network net, data d, int n); |
| | | float train_network_batch(network net, data d, int n); |
| | | void train_network(network net, data d); |
| | | matrix network_predict_data(network net, data test); |
| | | float network_accuracy(network net, data d); |
| | | float network_accuracy_multi(network net, data d, int n); |
| | | float *get_network_output(network net); |
| | | float *get_network_output_layer(network net, int i); |
| | | float *get_network_delta_layer(network net, int i); |
| | |
| | | int get_predicted_class_network(network net); |
| | | void print_network(network net); |
| | | void visualize_network(network net); |
| | | void save_network(network net, char *filename); |
| | | int resize_network(network net, int h, int w, int c); |
| | | int get_network_input_size(network net); |
| | | |