| | |
| | | for(i = 0; i < net.n; ++i){ |
| | | layer l = net.layers[i]; |
| | | if(l.delta_gpu){ |
| | | scal_ongpu(l.outputs * l.batch, 0, l.delta_gpu, 1); |
| | | fill_ongpu(l.outputs * l.batch, 0, l.delta_gpu, 1); |
| | | } |
| | | if(l.type == CONVOLUTIONAL){ |
| | | forward_convolutional_layer_gpu(l, state); |
| | |
| | | network_state state; |
| | | int x_size = get_network_input_size(net)*net.batch; |
| | | int y_size = get_network_output_size(net)*net.batch; |
| | | if(net.layers[net.n-1].type == REGION) y_size = net.layers[net.n-1].truths*net.batch; |
| | | if(!*net.input_gpu){ |
| | | *net.input_gpu = cuda_make_array(x, x_size); |
| | | *net.truth_gpu = cuda_make_array(y, y_size); |