| | |
| | | //printf("start\n"); |
| | | int i; |
| | | for(i = 0; i < net.n; ++i){ |
| | | clock_t time = clock(); |
| | | //clock_t time = clock(); |
| | | if(net.types[i] == CONVOLUTIONAL){ |
| | | convolutional_layer layer = *(convolutional_layer *)net.layers[i]; |
| | | forward_convolutional_layer_gpu(layer, input); |
| | |
| | | forward_softmax_layer_gpu(layer, input); |
| | | input = layer.output_cl; |
| | | } |
| | | printf("%d %f\n", i, sec(clock()-time)); |
| | | //printf("%d %f\n", i, sec(clock()-time)); |
| | | /* |
| | | else if(net.types[i] == CROP){ |
| | | crop_layer layer = *(crop_layer *)net.layers[i]; |
| | |
| | | { |
| | | int x_size = get_network_input_size(net)*net.batch; |
| | | int y_size = get_network_output_size(net)*net.batch; |
| | | clock_t time = clock(); |
| | | if(!*net.input_cl){ |
| | | *net.input_cl = cl_make_array(x, x_size); |
| | | *net.truth_cl = cl_make_array(y, y_size); |
| | |
| | | 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(); |
| | | float error = get_network_cost(net); |
| | | update_network_gpu(net); |
| | | //printf("updt %f\n", sec(clock()-time)); |
| | | time = clock(); |
| | | return error; |
| | | } |
| | | |