infering from a frozen model in tensorflow using dataset API

I am trying to infer from a frozen graph(generated using freeze_graph.py) of tensorflow wideanddeep model. I use Dataset API to parse the test.csv file. Since feed dict only accepts numpy arrays and the contents of Dataset are tensors, I get batch next_element=iterator.get_next() and batch=sess.run(next_element) to get the numpy values and then feed into the placeholders using a feed dict. But this doesn't gives me good throughput when running on large datasets, since it is not efficient way to convert the tensors into numpy arrays and then feed into the placeholders. Is there an efficient way of doing this.

def input_fn(data_file, num_epochs, shuffle, batch_size):
  """Generate an input function for the Estimator."""
  assert tf.gfile.Exists(data_file), (
      '%s not found. Please make sure you have run census_dataset.py and '
      'set the --data_dir argument to the correct path.' % data_file)

  def parse_csv(value):
    tf.logging.info('Parsing {}'.format(data_file))
    cont_defaults = [ [0.0] for i in range(1,14) ]
    cate_defaults = [ [" "] for i in range(1,27) ]
    label_defaults = [ [0] ]
    column_headers = TRAIN_DATA_COLUMNS
    record_defaults = label_defaults + cont_defaults + cate_defaults
    columns = tf.decode_csv(value, record_defaults=record_defaults)
    all_columns = dict(zip(column_headers, columns))
    labels = all_columns.pop(LABEL_COLUMN[0])
    features = all_columns
    return features, labels

  # Extract lines from input files using the Dataset API.
  dataset = tf.data.TextLineDataset(data_file)

  if shuffle:
    dataset = dataset.shuffle(buffer_size=2000)

  dataset = dataset.map(parse_csv, num_parallel_calls=8)

  # We call repeat after shuffling, rather than before, to prevent separate
  # epochs from blending together.
  dataset = dataset.repeat(num_epochs)
  dataset = dataset.batch(batch_size)
  dataset = dataset.prefetch(56)
  return dataset 



with tf.Session(graph=graph) as sess:
  res_dataset = input_fn(predictioninputfile,1,False,batch_size)
  iterator = res_dataset.make_one_shot_iterator()
  next_element = iterator.get_next()
  inference_start = time.time()
  for i in range(no_of_batches):
    batch=sess.run(next_element)
    features,actual_label=batch[0],batch[1]
    #print("features",features)
    logistic = sess.run(output_tensor, dict(zip(input_tensor,list(features.values()))))