research/improve_nas/trainer/nasnet.py [319:344]:
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
  normal_cell = nasnet_utils.NasNetANormalCell(
      hparams.num_conv_filters, hparams.drop_path_keep_prob,
      total_num_cells, hparams.total_training_steps,
      hparams.use_bounded_activation)
  reduction_cell = nasnet_utils.NasNetAReductionCell(
      hparams.num_conv_filters, hparams.drop_path_keep_prob,
      total_num_cells, hparams.total_training_steps,
      hparams.use_bounded_activation)
  with arg_scope([slim.dropout, nasnet_utils.drop_path, slim.batch_norm],
                 is_training=is_training):
    with arg_scope([slim.avg_pool2d,
                    slim.max_pool2d,
                    slim.conv2d,
                    slim.batch_norm,
                    slim.separable_conv2d,
                    nasnet_utils.factorized_reduction,
                    nasnet_utils.global_avg_pool,
                    nasnet_utils.get_channel_index,
                    nasnet_utils.get_channel_dim],
                   data_format=hparams.data_format):
      return _build_nasnet_base(images,
                                normal_cell=normal_cell,
                                reduction_cell=reduction_cell,
                                num_classes=num_classes,
                                hparams=hparams,
                                is_training=is_training,
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -



research/improve_nas/trainer/nasnet.py [373:398]:
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
  normal_cell = nasnet_utils.NasNetANormalCell(
      hparams.num_conv_filters, hparams.drop_path_keep_prob,
      total_num_cells, hparams.total_training_steps,
      hparams.use_bounded_activation)
  reduction_cell = nasnet_utils.NasNetAReductionCell(
      hparams.num_conv_filters, hparams.drop_path_keep_prob,
      total_num_cells, hparams.total_training_steps,
      hparams.use_bounded_activation)
  with arg_scope([slim.dropout, nasnet_utils.drop_path, slim.batch_norm],
                 is_training=is_training):
    with arg_scope([slim.avg_pool2d,
                    slim.max_pool2d,
                    slim.conv2d,
                    slim.batch_norm,
                    slim.separable_conv2d,
                    nasnet_utils.factorized_reduction,
                    nasnet_utils.global_avg_pool,
                    nasnet_utils.get_channel_index,
                    nasnet_utils.get_channel_dim],
                   data_format=hparams.data_format):
      return _build_nasnet_base(images,
                                normal_cell=normal_cell,
                                reduction_cell=reduction_cell,
                                num_classes=num_classes,
                                hparams=hparams,
                                is_training=is_training,
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -