tf.ragged.stack

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

tf.ragged.stack(
    values, 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]]]>

Args:

• 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).

Returns:

A RaggedTensor with rank R+1. result.ragged_rank=1+max(axis, max(rt.ragged_rank for rt in values])).

Raises:

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