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]]]>

valuesA 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.
axisA 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.
nameA 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])).

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