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I tried to implement snippets of your code for a simple word-reversal problem, where I have 3 words in a sentence but when I compute the cross-entropy it gives me an error like this: InvalidArgumentError (see above for traceback): logits and labels must be broadcastable: logits_size=[832,28] labels_size=[960,28].
I believe it is because the labels in my code have been padded whereas the logits, which are outputs of the dynamic_decode function, have variable sequence lengths. How did you manage to get the cross-entropy to work with variable sequence length for the logits?
Hi,
I tried to implement snippets of your code for a simple word-reversal problem, where I have 3 words in a sentence but when I compute the cross-entropy it gives me an error like this: InvalidArgumentError (see above for traceback): logits and labels must be broadcastable: logits_size=[832,28] labels_size=[960,28].
I believe it is because the labels in my code have been padded whereas the logits, which are outputs of the dynamic_decode function, have variable sequence lengths. How did you manage to get the cross-entropy to work with variable sequence length for the logits?
https://github.com/kcang2/Udacity-Deep-Learning-Assignment/blob/master/Assignment_6_3.ipynb
Best regards.
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