tf.ragged.stack

Stacks a list of rank-R tensors into one rank-(R+1) RaggedTensor.

tf.ragged.stack(
    values: typing.List[ragged_tensor.RaggedOrDense], axis=0, name=None
)

Given a list of tensors or ragged tensors with the same rank R (R >= axis), returns a rank-R+1 RaggedTensor result such that result[i0...iaxis] is [value[i0...iaxis] for value in values].

Examples:

# Stacking two ragged tensors.
t1 = tf.ragged.constant([[1, 2], [3, 4, 5]])
t2 = tf.ragged.constant([[6], [7, 8, 9]])
tf.ragged.stack([t1, t2], axis=0)
<tf.RaggedTensor [[[1, 2], [3, 4, 5]], [[6], [7, 8, 9]]]>
tf.ragged.stack([t1, t2], axis=1)
<tf.RaggedTensor [[[1, 2], [6]], [[3, 4, 5], [7, 8, 9]]]>
 
# Stacking two dense tensors with different sizes.
t3 = tf.constant([[1, 2, 3], [4, 5, 6]])
t4 = tf.constant([[5], [6], [7]])
tf.ragged.stack([t3, t4], axis=0)
<tf.RaggedTensor [[[1, 2, 3], [4, 5, 6]], [[5], [6], [7]]]>

values A list of tf.Tensor or tf.RaggedTensor. May not be empty. All values must have the same rank and the same dtype; but unlike tf.stack, they can have arbitrary dimension sizes.
axis A python integer, indicating the dimension along which to stack. (Note: Unlike tf.stack, the axis parameter must be statically known.) Negative values are supported only if the rank of at least one values value is statically known.
name A name prefix for the returned tensor (optional).

A RaggedTensor with rank R+1 (if R>0). If R==0, then the result will be returned as a 1D Tensor, since RaggedTensor can only be used when rank>1. result.ragged_rank=1+max(axis, max(rt.ragged_rank for rt in values])).

ValueError If values is empty, if axis is out of bounds or if the input tensors have different ranks.