pythontensorflowtensorragged-tensors

Error when trying to stack two ragged tensors

I'm getting an odd error when trying to use tf.ragged.stack. I'm trying to stack two tensors t1, t2, however they have different rank. t2 is 1 rank less than t1. Therefore I used tf.expand_dims to increase the rank of t2 so the ranks match. However when I go to stack them I get an error:

import tensorflow as tf
t1 = tf.ones([2,10,10], tf.int32)
t2 = tf.ragged.constant([
                         [0,1,2,3,4,5],
                         [0,1,2,3,4]
])
t2 = tf.expand_dims(t2, -1)
tf.ragged.stack([t1, t2])

The error I get is

InvalidArgumentError: ConcatOp : Dimensions of inputs should match: shape[0] = [20,10] vs. shape[1] = [11,1] [Op:ConcatV2] name: concat

However when I create an equivalent tensor from scratch I don't get the error.

t2_new = tf.ragged.constant([
                         [[0],[1],[2],[3],[4],[5]],
                         [[0],[1],[2],[3],[4]]

tf.ragged.stack([t1, t2_new]) # no error

The difference between t2 and t2 new is that tensorflow thinks their shapes are different even though they actually represent the same tensor

print(t2.shape) # == (2,None,1)
print(t2_new.shape) # == (2, None, None)
print(tf.math.reduce_all(t2 == t2_new)) # == True i.e actually the same tensor
Solution

  • It's actually not that trivial, as tf.expand_dims adds a new dimension, but not a ragged dimension, which is necessary to stack both tensors afterwards. Generally, it is possible but a bit more complicated than just adding a new dimension. I would recommend using tf.RaggedTensor.from_value_rowids with tf.RaggedTensor.from_row_splits:

    import tensorflow as tf

t1 = tf.ones([2,10,10], tf.int32)
t2 = tf.ragged.constant([[0,1,2,3,4,5],
                         [0,1,2,3,4]])

t2_new = tf.ragged.constant([
                         [[0],[1],[2],[3],[4],[5]],
                         [[0],[1],[2],[3],[4]]])

flattened_ragged_tensor = t2.flat_values
rows = tf.cast(t2.bounding_shape()[0], dtype=tf.int32)
t2 = tf.RaggedTensor.from_value_rowids(
    values=tf.RaggedTensor.from_row_splits(
        values=flattened_ragged_tensor,
        row_splits=tf.range(tf.shape(flattened_ragged_tensor)[0] + 1)),
    value_rowids=tf.concat([tf.tile([i], [t2[i].shape[0]]) for i in tf.range(rows)], axis=0),
    nrows=rows)

print(t2.shape)
print(t2_new.shape)

print(tf.ragged.stack([t1, t2]))
print(tf.ragged.stack([t1, t2_new]))

    (2, None, None)
(2, None, None)
<tf.RaggedTensor [[[[1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1]], [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]], [[[0], [1], [2], [3], [4], [5]], [[0], [1], [2], [3], [4]]]]>
<tf.RaggedTensor [[[[1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1]], [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]], [[[0], [1], [2], [3], [4], [5]], [[0], [1], [2], [3], [4]]]]>