| | |
| | | l.out_h = l.out_w = 1; |
| | | } |
| | | if(state.train){ |
| | | mean_cpu(l.output, l.batch, l.out_c, l.out_h*l.out_w, l.mean); |
| | | variance_cpu(l.output, l.mean, l.batch, l.out_c, l.out_h*l.out_w, l.variance); |
| | | mean_cpu(l.output, l.batch, l.out_c, l.out_h*l.out_w, l.mean); |
| | | variance_cpu(l.output, l.mean, l.batch, l.out_c, l.out_h*l.out_w, l.variance); |
| | | |
| | | scal_cpu(l.out_c, .99, l.rolling_mean, 1); |
| | | axpy_cpu(l.out_c, .01, l.mean, 1, l.rolling_mean, 1); |
| | | scal_cpu(l.out_c, .99, l.rolling_variance, 1); |
| | | axpy_cpu(l.out_c, .01, l.variance, 1, l.rolling_variance, 1); |
| | | |
| | | copy_cpu(l.outputs*l.batch, l.output, 1, l.x, 1); |
| | | normalize_cpu(l.output, l.mean, l.variance, l.batch, l.out_c, l.out_h*l.out_w); |
| | | copy_cpu(l.outputs*l.batch, l.output, 1, l.x_norm, 1); |
| | | } else { |
| | | normalize_cpu(l.output, l.rolling_mean, l.rolling_variance, l.batch, l.out_c, l.out_h*l.out_w); |
| | | } |
| | | scale_bias(l.output, l.scales, l.batch, l.out_c, l.out_h*l.out_w); |
| | | } |
| | | |
| | | void backward_batchnorm_layer(const layer layer, network_state state) |
| | | void backward_batchnorm_layer(const layer l, network_state state) |
| | | { |
| | | backward_scale_cpu(l.x_norm, l.delta, l.batch, l.out_c, l.out_w*l.out_h, l.scale_updates); |
| | | |
| | | scale_bias(l.delta, l.scales, l.batch, l.out_c, l.out_h*l.out_w); |
| | | |
| | | mean_delta_cpu(l.delta, l.variance, l.batch, l.out_c, l.out_w*l.out_h, l.mean_delta); |
| | | variance_delta_cpu(l.x, l.delta, l.mean, l.variance, l.batch, l.out_c, l.out_w*l.out_h, l.variance_delta); |
| | | normalize_delta_cpu(l.x, l.mean, l.variance, l.mean_delta, l.variance_delta, l.batch, l.out_c, l.out_w*l.out_h, l.delta); |
| | | if(l.type == BATCHNORM) copy_cpu(l.outputs*l.batch, l.delta, 1, state.delta, 1); |
| | | } |
| | | |
| | | #ifdef GPU |