Generates a feature cross from a list of tensors, and returns it as a
tf.raw_ops.RaggedCross(
ragged_values, ragged_row_splits, sparse_indices, sparse_values, sparse_shape,
dense_inputs, input_order, hashed_output, num_buckets, hash_key,
out_values_type, out_row_splits_type, name=None
)
RaggedTensor. See tf.ragged.cross for more details.
Args:
ragged_values: A list of Tensor objects with types from: int64, string.
The values tensor for each RaggedTensor input.
ragged_row_splits: A list of Tensor objects with types from: int32, int64.
The row_splits tensor for each RaggedTensor input.
sparse_indices: A list of Tensor objects with type int64.
The indices tensor for each SparseTensor input.
sparse_values: A list of Tensor objects with types from: int64, string.
The values tensor for each SparseTensor input.
sparse_shape: A list with the same length as sparse_indices of Tensor objects with type int64.
The dense_shape tensor for each SparseTensor input.
dense_inputs: A list of Tensor objects with types from: int64, string.
The tf.Tensor inputs.
input_order: A string.
String specifying the tensor type for each input. The ith character in
this string specifies the type of the ith input, and is one of: 'R' (ragged),
'D' (dense), or 'S' (sparse). This attr is used to ensure that the crossed
values are combined in the order of the inputs from the call to tf.ragged.cross.
hashed_output: A bool.
num_buckets: An int that is >= 0.
hash_key: An int.
out_values_type: A tf.DType from: tf.int64, tf.string.
out_row_splits_type: A tf.DType from: tf.int32, tf.int64.
name: A name for the operation (optional).
Returns:
A tuple of Tensor objects (output_values, output_row_splits).
output_values: A `Tensor` of type `out_values_type`.
output_row_splits: A `Tensor` of type `out_row_splits_type`.