tf.compat.v1.ragged.constant_value

Constructs a RaggedTensorValue from a nested Python list.

tf.compat.v1.ragged.constant_value(
    pylist, dtype=None, ragged_rank=None, inner_shape=None, row_splits_dtype='int64'
)

Warning: This function returns a RaggedTensorValue, not a RaggedTensor. If you wish to construct a constant RaggedTensor, use ragged.constant(...) instead.

Example:

>>> tf.compat.v1.ragged.constant_value([[1, 2], [3], [4, 5, 6]])
tf.RaggedTensorValue(values=array([1, 2, 3, 4, 5, 6]),
                     row_splits=array([0, 2, 3, 6]))

All scalar values in pylist must have the same nesting depth K, and the returned RaggedTensorValue 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 or tuple must be a scalar value compatible with dtype.
dtype: numpy.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 RaggedTensorValue. 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 RaggedTensorValue. Defaults to () if ragged_rank is not specified. If ragged_rank is specified, then a default is chosen based on the contents of pylist.
row_splits_dtype: data type for the constructed RaggedTensorValue's row_splits. One of numpy.int32 or numpy.int64.

Returns:

A tf.RaggedTensorValue or numpy.array 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.