Method get

tensorflow/python/feature_column/feature_column.py:2195–2237

Returns a `Tensor` for the given key. A `str` key is used to access a base feature (not-transformed). When a `_FeatureColumn` is passed, the transformed feature is returned if it already exists, otherwise the given `_FeatureColumn` is asked to provide its transformed output, which i

(self, key)

    self._adaptive_mask_tensors = adaptive_mask_tensors

  def get(self, key):
    """Returns a `Tensor` for the given key.

    A `str` key is used to access a base feature (not-transformed). When a
    `_FeatureColumn` is passed, the transformed feature is returned if it
    already exists, otherwise the given `_FeatureColumn` is asked to provide its
    transformed output, which is then cached.

    Args:
      key: a `str` or a `_FeatureColumn`.

    Returns:
      The transformed `Tensor` corresponding to the `key`.

    Raises:
      ValueError: if key is not found or a transformed `Tensor` cannot be
        computed.
    """
    if key in self._feature_tensors:
      # FeatureColumn is already transformed or converted.
      return self._feature_tensors[key]

    if key in self._features:
      feature_tensor = self._get_raw_feature_as_tensor(key)
      self._feature_tensors[key] = feature_tensor
      return feature_tensor

    if isinstance(key, six.string_types):
      raise ValueError('Feature {} is not in features dictionary.'.format(key))

    if not isinstance(key, _FeatureColumn):
      raise TypeError('"key" must be either a "str" or "_FeatureColumn". '
                      'Provided: {}'.format(key))

    column = key
    logging.debug('Transforming feature_column %s.', column)
    if self._adaptive_mask_tensors is not None and column.name in self._adaptive_mask_tensors:
      column.set_adaptive_mask_tensor(self._adaptive_mask_tensors[column.name])
    transformed = column._transform_feature(self)  # pylint: disable=protected-access
    if transformed is None:
      raise ValueError('Column {} is not supported.'.format(column.name))
    self._feature_tensors[column] = transformed
    return transformed

  def _get_raw_feature_as_tensor(self, key):
    """Gets the raw_feature (keyed by `key`) as `tensor`.