Method from_tensor

tensorflow/python/ops/ragged/ragged_tensor.py:1321–1503

Converts a `tf.Tensor` into a `RaggedTensor`. The set of absent/default values may be specified using a vector of lengths or a padding value (but not both). If `lengths` is specified, then the output tensor will satisfy `output[row] = tensor[row][:lengths[row]]`. If 'lengths' is a

(cls,
                  tensor,
                  lengths=None,
                  padding=None,
                  ragged_rank=1,
                  name=None,
                  row_splits_dtype=dtypes.int64)

  @classmethod
  def from_tensor(cls,
                  tensor,
                  lengths=None,
                  padding=None,
                  ragged_rank=1,
                  name=None,
                  row_splits_dtype=dtypes.int64):
    """Converts a `tf.Tensor` into a `RaggedTensor`.

    The set of absent/default values may be specified using a vector of lengths
    or a padding value (but not both).  If `lengths` is specified, then the
    output tensor will satisfy `output[row] = tensor[row][:lengths[row]]`. If
    'lengths' is a list of lists or tuple of lists, those lists will be used
    as nested row lengths. If `padding` is specified, then any row *suffix*
    consisting entirely of `padding` will be excluded from the returned
    `RaggedTensor`.  If neither `lengths` nor `padding` is specified, then the
    returned `RaggedTensor` will have no absent/default values.

    Examples:

    ```python
    >>> dt = tf.constant([[5, 7, 0], [0, 3, 0], [6, 0, 0]])
    >>> tf.RaggedTensor.from_tensor(dt)
    <tf.RaggedTensor [[5, 7, 0], [0, 3, 0], [6, 0, 0]]>
    >>> tf.RaggedTensor.from_tensor(dt, lengths=[1, 0, 3])
    <tf.RaggedTensor [[5], [], [6, 0, 0]]>

    >>> tf.RaggedTensor.from_tensor(dt, padding=0)
    <tf.RaggedTensor [[5, 7], [0, 3], [6]]>

    >>> dt = tf.constant([[[5, 0], [7, 0], [0, 0]],
                          [[0, 0], [3, 0], [0, 0]],
                          [[6, 0], [0, 0], [0, 0]]])
    >>> tf.RaggedTensor.from_tensor(dt, lengths=([2, 0, 3], [1, 1, 2, 0, 1]))
    <tf.RaggedTensor [[[5], [7]], [], [[6, 0], [], [0]]]>
    ```

    Args:
      tensor: The `Tensor` to convert.  Must have rank `ragged_rank + 1` or
        higher.
      lengths: An optional set of row lengths, specified using a 1-D integer
        `Tensor` whose length is equal to `tensor.shape[0]` (the number of rows
        in `tensor`).  If specified, then `output[row]` will contain
        `tensor[row][:lengths[row]]`.  Negative lengths are treated as zero. You
        may optionally pass a list or tuple of lengths to this argument, which
        will be used as nested row lengths to construct a ragged tensor with
        multiple ragged dimensions.
      padding: An optional padding value.  If specified, then any row suffix
        consisting entirely of `padding` will be excluded from the returned
        RaggedTensor.  `padding` is a `Tensor` with the same dtype as `tensor`
        and with `shape=tensor.shape[ragged_rank + 1:]`.
      ragged_rank: Integer specifying the ragged rank for the returned
        `RaggedTensor`.  Must be greater than zero.
      name: A name prefix for the returned tensors (optional).
      row_splits_dtype: `dtype` for the returned `RaggedTensor`&#x27;s `row_splits`
        tensor.  One of `tf.int32` or `tf.int64`.

    Returns: