I want to build a tensor placeholder, features with dimension, say (10, a, a). Such that features[i, :, :] can be an arbitrary square tensor. As an instance, features[0. :, :] may be of dimension 5*5, and features[1, :, :] can be of dimension 8*8 at the same time. How can we do this with tensorflow?
I found ragged tensors for this purpose. But the problem is that for feeding values in the ragged tensor, I will have to use normal lists in python. In my case this feed list is very sparse and there is no way to compress ragged lists.
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Can you provide a example? How are you storing your data out of TensorFlow in the first place? – jdehesa Apr 24 '20 at 10:35
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@jdehesa I have data in the form list of list of list. – Kunal Goyal May 14 '20 at 07:15