scripts/tf_cnn_benchmarks/models/tf1_only/nasnet_model.py [346:381]:
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  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.')

  # 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)
  reduction_cell = nasnet_utils.NasNetAReductionCell(
      hparams.num_conv_filters, hparams.drop_path_keep_prob, total_num_cells,
      hparams.total_training_steps)
  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)
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scripts/tf_cnn_benchmarks/models/tf1_only/nasnet_model.py [397:432]:
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  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.')

  # 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)
  reduction_cell = nasnet_utils.NasNetAReductionCell(
      hparams.num_conv_filters, hparams.drop_path_keep_prob, total_num_cells,
      hparams.total_training_steps)
  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)
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