# Using TF Dataset API to process sequences for stateful RNN

I am trying to use the TensorFlow (v1.13) Dataset API to save and load long sequences for a stateful RNN.

Basically, lets say I have n_seq sequences, each fixed to a length of 120.

I want to load batches to train my stateful RNN according to this post, basically:

           batch 1         batch 2         batch 3         batch 4

element0    s21             s22              s23             s24   ...
element1    s11             s12              s13             s14


where sij denotes j-th window of the i-th sequence. This is done based on how a stateful RNN works in TF, as when stateful=True, "the last state for each sample at index i in a batch will be used as initial state for the sample of index i in the following batch." (source)

### Initial Solution

I am saving each sequence to its own *.tfrecord file. Then I am loading them using:

tf_dataset = tf.data.Dataset.list_files(path, shuffle=False, seed=1)
tf_dataset = tf_dataset.interleave(
lambda filename: tf.data.TFRecordDataset(filename).map(parse_func),
cycle_length=BATCH_SIZE,
block_length=1
)
tf_dataset = tf_dataset.batch(batchsize)


This actually mostly works EXCEPT for the final files. Namely if n_files % BATCH_SIZE != 0, the finale files will be together in the same batch.

Is there a better way to handle this?

NOTE: This question was previously asked on stackoverflow.coom, but deleted due to lack of response. I think this will be a better home for it.