| | |
| | | cl_write_array(*net.input_cl, x, x_size); |
| | | cl_write_array(*net.truth_cl, y, y_size); |
| | | } |
| | | //printf("trans %f\n", sec(clock()-time)); |
| | | //time = clock(); |
| | | |
| | | forward_network_gpu(net, *net.input_cl, *net.truth_cl, 1); |
| | | |
| | | //printf("forw %f\n", sec(clock()-time)); |
| | | //time = clock(); |
| | | backward_network_gpu(net, *net.input_cl); |
| | | //printf("back %f\n", sec(clock()-time)); |
| | | //time = clock(); |
| | | |
| | | update_network_gpu(net); |
| | | float error = get_network_cost(net); |
| | | |
| | | //printf("updt %f\n", sec(clock()-time)); |
| | | //time = clock(); |
| | | return error; |
| | | } |
| | | |
| | |
| | | float network_accuracy_gpu(network net, data d) |
| | | { |
| | | matrix guess = network_predict_data_gpu(net, d); |
| | | float acc = matrix_accuracy(d.y, guess); |
| | | float acc = matrix_topk_accuracy(d.y, guess,1); |
| | | free_matrix(guess); |
| | | return acc; |
| | | } |
| | | |
| | | float *network_accuracies_gpu(network net, data d) |
| | | { |
| | | static float acc[2]; |
| | | matrix guess = network_predict_data_gpu(net, d); |
| | | acc[0] = matrix_topk_accuracy(d.y, guess,1); |
| | | acc[1] = matrix_topk_accuracy(d.y, guess,5); |
| | | free_matrix(guess); |
| | | return acc; |
| | | } |
| | | |
| | | |
| | | #else |
| | | void forward_network_gpu(network net, cl_mem input, cl_mem truth, int train){} |
| | | void backward_network_gpu(network net, cl_mem input){} |
| | | void update_network_gpu(network net){} |
| | | float train_network_sgd_gpu(network net, data d, int n){return 0;} |
| | | float train_network_data_gpu(network net, data d, int n){return 0;} |
| | | float *network_predict_gpu(network net, float *input){return 0;} |
| | | float network_accuracy_gpu(network net, data d){return 0;} |
| | | |
| | | #endif |