tf.compat.v1.ragged.constant_value | TensorFlow v2.16.1 (original) (raw)

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'
) -> Union[tf.compat.v1.ragged.RaggedTensorValue, np.ndarray]

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 inpylist.
ragged_rank	An integer specifying the ragged rank of the returnedRaggedTensorValue. Must be nonnegative and less than K. Defaults tomax(0, K - 1) if inner_shape is not specified. Defaults to `max(0, K 1 - len(inner_shape))ifinner_shapeis specified.
inner_shape	A tuple of integers specifying the shape for individual inner values in the returnedRaggedTensorValue. Defaults to()ifragged_rankis not specified. Ifragged_rankis specified, then a default is chosen based on the contents ofpylist.
row_splits_dtype	data type for the constructedRaggedTensorValue's row_splits. One ofnumpy.int32ornumpy.int64`.

Returns
A tf.RaggedTensorValue or numpy.array with rank K and the specifiedragged_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.