| | |
| | | l.outputs = inputs; |
| | | l.cost_type = cost_type; |
| | | l.delta = calloc(inputs*batch, sizeof(float)); |
| | | l.output = calloc(1, sizeof(float)); |
| | | l.output = calloc(inputs*batch, sizeof(float)); |
| | | l.cost = calloc(1, sizeof(float)); |
| | | #ifdef GPU |
| | | l.delta_gpu = cuda_make_array(l.delta, inputs*batch); |
| | | l.delta_gpu = cuda_make_array(l.output, inputs*batch); |
| | | l.output_gpu = cuda_make_array(l.delta, inputs*batch); |
| | | #endif |
| | | return l; |
| | | } |
| | |
| | | l->inputs = inputs; |
| | | l->outputs = inputs; |
| | | l->delta = realloc(l->delta, inputs*l->batch*sizeof(float)); |
| | | l->output = realloc(l->output, inputs*l->batch*sizeof(float)); |
| | | #ifdef GPU |
| | | cuda_free(l->delta_gpu); |
| | | cuda_free(l->output_gpu); |
| | | l->delta_gpu = cuda_make_array(l->delta, inputs*l->batch); |
| | | l->output_gpu = cuda_make_array(l->output, inputs*l->batch); |
| | | #endif |
| | | } |
| | | |
| | |
| | | } |
| | | } |
| | | if(l.cost_type == SMOOTH){ |
| | | smooth_l1_cpu(l.batch*l.inputs, state.input, state.truth, l.delta); |
| | | smooth_l1_cpu(l.batch*l.inputs, state.input, state.truth, l.delta, l.output); |
| | | } else { |
| | | copy_cpu(l.batch*l.inputs, state.truth, 1, l.delta, 1); |
| | | axpy_cpu(l.batch*l.inputs, -1, state.input, 1, l.delta, 1); |
| | | l2_cpu(l.batch*l.inputs, state.input, state.truth, l.delta, l.output); |
| | | } |
| | | *(l.output) = dot_cpu(l.batch*l.inputs, l.delta, 1, l.delta, 1); |
| | | //printf("cost: %f\n", *l.output); |
| | | l.cost[0] = sum_array(l.output, l.batch*l.inputs); |
| | | } |
| | | |
| | | void backward_cost_layer(const cost_layer l, network_state state) |
| | |
| | | } |
| | | |
| | | if(l.cost_type == SMOOTH){ |
| | | smooth_l1_gpu(l.batch*l.inputs, state.input, state.truth, l.delta_gpu); |
| | | smooth_l1_gpu(l.batch*l.inputs, state.input, state.truth, l.delta_gpu, l.output_gpu); |
| | | } else { |
| | | copy_ongpu(l.batch*l.inputs, state.truth, 1, l.delta_gpu, 1); |
| | | axpy_ongpu(l.batch*l.inputs, -1, state.input, 1, l.delta_gpu, 1); |
| | | l2_gpu(l.batch*l.inputs, state.input, state.truth, l.delta_gpu, l.output_gpu); |
| | | } |
| | | |
| | | cuda_pull_array(l.delta_gpu, l.delta, l.batch*l.inputs); |
| | | *(l.output) = dot_cpu(l.batch*l.inputs, l.delta, 1, l.delta, 1); |
| | | cuda_pull_array(l.output_gpu, l.output, l.batch*l.inputs); |
| | | l.cost[0] = sum_array(l.output, l.batch*l.inputs); |
| | | } |
| | | |
| | | void backward_cost_layer_gpu(const cost_layer l, network_state state) |