sequence-to-sequence

I have coded a sequence to sequence learning LSTM in keras myself using the knowledge gained from the web tutorials and my own intuitions. I converted my sample text to sequences and then padded using pad_sequence function in keras. from keras.preprocessing.text import Tokenizer,base_filter from keras.preprocessing.sequence import pad_sequences def shift(seq, n): n = n % len(seq) return seq[n:] + seq[:n] txt="abcdefghijklmn"*100 tk = Tokenizer(nb_words=2000, filters=base_filter(), lower=True, split=" ") tk.fit_on_texts(txt) x = tk.texts_to_sequences(txt) #shifing to left y = shift(x,1)

I'm having trouble using buckets in my Tensorflow model. When I run it with buckets = [(100, 100)] , it works fine. When I run it with buckets = [(100, 100), (200, 200)] it doesn't work at all (stacktrace at bottom). Interestingly, running Tensorflow's Seq2Seq tutorial gives the same kind of issue with a nearly identical stacktrace. For testing purposes, the link to the repository is here . I'm not sure what the issue is, but having more than one bucket always seems to trigger it. This code won't work as a standalone, but this is the function where it is crashing - remember that changing