| | |
| | | int i = 0; |
| | | float mean = 0; |
| | | for(i = 0; i < n; ++i){ |
| | | mean += abs(input[i*size + s]); |
| | | mean += fabs(input[i*size + s]); |
| | | } |
| | | mean = mean / n; |
| | | for(i = 0; i < n; ++i){ |
| | |
| | | int i = 0; |
| | | float mean = 0; |
| | | for(i = 0; i < size; ++i){ |
| | | mean += abs(weights[f*size + i]); |
| | | mean += fabs(weights[f*size + i]); |
| | | } |
| | | mean = mean / size; |
| | | for(i = 0; i < size; ++i){ |
| | |
| | | |
| | | if (l.batch_normalize) { |
| | | forward_batchnorm_layer_gpu(l, state); |
| | | } |
| | | add_bias_gpu(l.output_gpu, l.biases_gpu, l.batch, l.n, l.out_w*l.out_h); |
| | | } |
| | | else { |
| | | add_bias_gpu(l.output_gpu, l.biases_gpu, l.batch, l.n, l.out_w*l.out_h); |
| | | } |
| | | |
| | | activate_array_ongpu(l.output_gpu, l.outputs*l.batch, l.activation); |
| | | //if(l.dot > 0) dot_error_gpu(l); |