tf.ragged.constant

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Constructs a constant RaggedTensor from a nested Python list.

tf.ragged.constant(
    pylist, dtype=None, ragged_rank=None, inner_shape=None, name=None,
    row_splits_dtype=tf.dtypes.int64
)

Example:

>>> tf.ragged.constant([[1, 2], [3], [4, 5, 6]])
<tf.RaggedTensor [[1, 2], [3], [4, 5, 6]]>

All scalar values in pylist must have the same nesting depth K, and the returned RaggedTensor will have rank K. If pylist contains no scalar values, then K is one greater than the maximum depth of empty lists in pylist. All scalar values in pylist must be compatible with dtype.

Args:

• pylist: A nested list, tuple or np.ndarray. Any nested element that is not a list, tuple or np.ndarray must be a scalar value compatible with dtype.
• dtype: The type of elements for the returned RaggedTensor. If not specified, then a default is chosen based on the scalar values in pylist.
• ragged_rank: An integer specifying the ragged rank of the returned RaggedTensor. Must be nonnegative and less than K. Defaults to max(0, K - 1) if inner_shape is not specified. Defaults to `max(0, K
  • 1 - len(inner_shape))ifinner_shape` is specified.
• inner_shape: A tuple of integers specifying the shape for individual inner values in the returned RaggedTensor. Defaults to () if ragged_rank is not specified. If ragged_rank is specified, then a default is chosen based on the contents of pylist.
• name: A name prefix for the returned tensor (optional).
• row_splits_dtype: data type for the constructed RaggedTensor's row_splits. One of tf.int32 or tf.int64.

Returns:

A potentially ragged tensor with rank K and the specified ragged_rank, containing the values from pylist.

Raises:

• ValueError: If the scalar values in pylist have inconsistent nesting depth; or if ragged_rank or inner_shape are incompatible with pylist.