archive/classification_marcel/tf-slim/nets/nasnet/nasnet.py [349:393]:
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             else copy.deepcopy(config))
  _update_hparams(hparams, is_training)

  if tf.test.is_gpu_available() and hparams.data_format == 'NHWC':
    tf.logging.info('A GPU is available on the machine, consider using NCHW '
                    'data format for increased speed on GPU.')

  if hparams.data_format == 'NCHW':
    images = tf.transpose(images, [0, 3, 1, 2])

  # Calculate the total number of cells in the network
  # Add 2 for the reduction cells
  total_num_cells = hparams.num_cells + 2
  # If ImageNet, then add an additional two for the stem cells
  total_num_cells += 2

  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,
                                stem_type='imagenet',
                                final_endpoint=final_endpoint,
                                current_step=current_step)
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archive/classification_marcel/tf-slim/nets/nasnet/nasnet.py [404:448]:
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             else copy.deepcopy(config))
  _update_hparams(hparams, is_training)

  if tf.test.is_gpu_available() and hparams.data_format == 'NHWC':
    tf.logging.info('A GPU is available on the machine, consider using NCHW '
                    'data format for increased speed on GPU.')

  if hparams.data_format == 'NCHW':
    images = tf.transpose(images, [0, 3, 1, 2])

  # Calculate the total number of cells in the network
  # Add 2 for the reduction cells
  total_num_cells = hparams.num_cells + 2
  # If ImageNet, then add an additional two for the stem cells
  total_num_cells += 2

  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,
                                stem_type='imagenet',
                                final_endpoint=final_endpoint,
                                current_step=current_step)
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