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LT;DR: length calculation is wrong, padded zeros are never ignored. Note that `vocab_encode` encodes the each char an index in `1`..`vocab_len`: that's what is stored in `seq` before it goes through one-hot encodding. It is expected that `tf.one_hot` will encode only valid indices and return zeros for paddings (which is `0`), but it's not what it does. Instead, it will encode every index in `0`..`vocab_len-1` and ignore `vocab_len`. This means that `}` char will always end the seq, while padded zeros are processed as normal chars. Doing `seq - 1` fixes both the padding `0` (should be invalid) and `vocab_len` (should be valid) indices.
Since one-hot encoding shifts the index down by 1, the generator must account for that, otherwise the sample sequence will collapse to zeros
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LT;DR: length calculation is wrong, padded zeros are never ignored.
Note that
vocab_encodeencodes each char as an index in1..vocab_len: that's what is stored inseqbefore it goes through one-hot encodding. It is expected thattf.one_hotwill encode only valid indices and return zeros for paddings (which is0), but it's not what it does. Instead, it will encode every index in0..vocab_len-1and ignorevocab_len. This means that}char will always end the seq, while padded zeros are processed as normal chars.Doing
seq - 1fixes both the padding0(should be invalid) andvocab_len(should be valid) indices.By the way, length calculation can also be simplified to
tf.reduce_sum(tf.reduce_max(seq, 2), 1)