Suppose I have three numpy arrays with shape (1,3) and stack them into groups with shape (2,3) and (1,3). Then I stack them with tf.ragged.stack to get a ragged tensor:
x1 = np.asarray([1,0,0])
x2 = np.asarray([0,1,0])
x3 = np.asarray([0,0,1])
group_a = np.stack([x1,x2])
group_b = np.stack([x3])
ac = tf.ragged.stack([group_a,group_b], axis=0)
I expect its shape to be (2, None, 3) but instead it's (2, None, None). How do I get the desired shape? I'm using tensorflow 2.5.2
This is happening because tf.ragged.stack is creating a ragged_rank which equals to 2. Check the docs for more information. You could explicitly define how to partition a ragged tensor like this:
import tensorflow as tf
import numpy as np
x1 = np.asarray([1,0,0])
x2 = np.asarray([0,1,0])
x3 = np.asarray([0,0,1])
ac = tf.RaggedTensor.from_row_splits(
values=[x1, x2, x3],
row_splits=[0, 2, 3])
print(ac.shape)
print(ac)
(2, None, 3)
<tf.RaggedTensor [[[1, 0, 0], [0, 1, 0]], [[0, 0, 1]]]>
This question is actually answered by the official help: https://www.tensorflow.org/api_docs/python/tf/RaggedTensor#get_shape
x1=[[0, 1, 2]]
x2=[[1, 2, 3], [3, 4, 6]]
tf.ragged.constant([x1, x2], ragged_rank=1).shape
Out[51]: TensorShape([2, None, 3])