| | |
| | | l.delta = calloc(inputs*batch, sizeof(float)); |
| | | l.output = calloc(inputs*batch, sizeof(float)); |
| | | l.cost = calloc(1, sizeof(float)); |
| | | |
| | | l.forward = forward_cost_layer; |
| | | l.backward = backward_cost_layer; |
| | | #ifdef GPU |
| | | l.forward_gpu = forward_cost_layer_gpu; |
| | | l.backward_gpu = backward_cost_layer_gpu; |
| | | |
| | | l.delta_gpu = cuda_make_array(l.output, inputs*batch); |
| | | l.output_gpu = cuda_make_array(l.delta, inputs*batch); |
| | | #endif |
| | |
| | | cuda_push_array(l.delta_gpu, l.delta, l.batch*l.inputs); |
| | | } |
| | | |
| | | int float_abs_compare (const void * a, const void * b) |
| | | { |
| | | float fa = *(const float*) a; |
| | | if(fa < 0) fa = -fa; |
| | | float fb = *(const float*) b; |
| | | if(fb < 0) fb = -fb; |
| | | return (fa > fb) - (fa < fb); |
| | | } |
| | | |
| | | void forward_cost_layer_gpu(cost_layer l, network_state state) |
| | | { |
| | | if (!state.truth) return; |
| | |
| | | l2_gpu(l.batch*l.inputs, state.input, state.truth, l.delta_gpu, l.output_gpu); |
| | | } |
| | | |
| | | if(l.ratio){ |
| | | cuda_pull_array(l.delta_gpu, l.delta, l.batch*l.inputs); |
| | | qsort(l.delta, l.batch*l.inputs, sizeof(float), float_abs_compare); |
| | | int n = (1-l.ratio) * l.batch*l.inputs; |
| | | float thresh = l.delta[n]; |
| | | thresh = 0; |
| | | printf("%f\n", thresh); |
| | | supp_ongpu(l.batch*l.inputs, thresh, l.delta_gpu, 1); |
| | | } |
| | | |
| | | cuda_pull_array(l.output_gpu, l.output, l.batch*l.inputs); |
| | | l.cost[0] = sum_array(l.output, l.batch*l.inputs); |
| | | } |