Method to_tensor

tensorflow/python/ops/ragged/ragged_tensor.py:1505–1589

Converts this `RaggedTensor` into a `tf.Tensor`. Example: ```python >>> rt = ragged.constant([[9, 8, 7], [], [6, 5], [4]]) >>> print rt.to_tensor() [[9 8 7] [0 0 0] [6 5 0] [4 0 0]] ``` Args: default_value: Value to set for indices not specified in

(self, default_value=None, name=None)

      return cls.from_row_splits(values, splits, validate=False)

  def to_tensor(self, default_value=None, name=None):
    """Converts this `RaggedTensor` into a `tf.Tensor`.

    Example:

    ```python
    >>> rt = ragged.constant([[9, 8, 7], [], [6, 5], [4]])
    >>> print rt.to_tensor()
    [[9 8 7]
     [0 0 0]
     [6 5 0]
     [4 0 0]]
    ```

    Args:
      default_value: Value to set for indices not specified in `self`. Defaults
        to zero.  `default_value` must be broadcastable to
        `self.shape[self.ragged_rank + 1:]`.
      name: A name prefix for the returned tensors (optional).

    Returns:
      A `Tensor` with shape `ragged.bounding_shape(self)` and the
      values specified by the non-empty values in `self`.  Empty values are
      assigned `default_value`.
    """
    with ops.name_scope(name, "RaggedToTensor", [self, default_value]):
      if default_value is not None:
        default_value = ops.convert_to_tensor(
            default_value, name="default_value", dtype=self.dtype)

      # If ragged_rank > 1, then recursively convert the ragged values into a
      # `Tensor` before we proceed.
      values = self.values
      if is_ragged(values):
        values = values.to_tensor(default_value)

      # Tile the default value, if necessary.
      if default_value is not None:
        if values.shape.ndims is not None:
          default_value.shape.with_rank_at_most(values.shape.ndims - 1)
        if (values.shape.ndims is None or default_value.shape.ndims is None or
            values.shape.ndims != default_value.shape.ndims + 1):
          value_shape = array_ops.shape(values)[1:]
          default_value = array_ops.broadcast_to(default_value, value_shape)
        default_value.shape.assert_is_compatible_with(values.shape[1:])

      # Get the expected dense shape ([nrows, ncols] + value_shape).
      rt_row_lengths = [self.row_splits[1:] - self.row_splits[:-1]]
      nrows = array_ops.shape(self.row_splits,
                              out_type=self._row_splits.dtype)[0] - 1
      ncols = math_ops.maximum(math_ops.reduce_max(rt_row_lengths), 0)
      values_shape = array_ops.shape(values, out_type=self._row_splits.dtype)
      value_shape = values_shape[1:]
      nvals = values_shape[0]

      # Build a default value if none was supplied.
      if default_value is None:
        default_value = array_ops.zeros(value_shape, dtype=values.dtype)