| | |
| | | momentum=0.9 |
| | | decay=0.0005 |
| | | |
| | | learning_rate=0.1 |
| | | policy=poly |
| | | power=4 |
| | | max_batches=500000 |
| | | learning_rate=0.01 |
| | | policy=sigmoid |
| | | gamma=.00002 |
| | | step=400000 |
| | | max_batches=800000 |
| | | |
| | | [crop] |
| | | crop_height=224 |
| | |
| | | fast_mean_gpu(l.output_gpu, l.batch, l.n, l.out_h*l.out_w, l.mean_gpu); |
| | | fast_variance_gpu(l.output_gpu, l.mean_gpu, l.batch, l.n, l.out_h*l.out_w, l.variance_gpu); |
| | | |
| | | /* |
| | | cuda_pull_array(l.variance_gpu, l.mean, 1); |
| | | printf("%f\n", l.mean[0]); |
| | | */ |
| | | |
| | | |
| | | scal_ongpu(l.n, .95, l.rolling_mean_gpu, 1); |
| | | axpy_ongpu(l.n, .05, l.mean_gpu, 1, l.rolling_mean_gpu, 1); |
| | | scal_ongpu(l.n, .95, l.rolling_variance_gpu, 1); |