model.py 文件源码

def parse_csv(rows_string_tensor):
  """Takes the string input tensor and returns a dict of rank-2 tensors."""
  columns = tf.decode_csv(
      rows_string_tensor, record_defaults=CSV_COLUMN_DEFAULTS)
  features = dict(zip(CSV_COLUMNS, columns))

  # Remove unused columns
  for col in UNUSED_COLUMNS:
    features.pop(col)

  for key, value in six.iteritems(features):
    features[key] = tf.expand_dims(features[key], -1)
  return features