resetting c_t and h_t in LSTM every couple of sequences in RNN
I don't think you would want to do that since each example in the batch should be independent.  If they are not you should just have a batch size of 1 and a sequence of length 100 * batch_size.  Often you might want to save the state in between batches.  In this case you would need to save the RNN state to a variable or as I like to do allow the user to feed it in with a placeholder.
I am trying to implement RNN in Tensorflow for text prediction. I am using BasicLSTMCell for this purpose, with sequence lengths of 100. 
If I understand correctly, the output activation h_t and the activation c_t of the LSTM are reset each time we enter a new sequence (that is, they are updated 100 times along the sequence, but once we move to the next sequence in the batch, they are reset to 0).
Is there a way to prevent this from happening using Tensorflow? That is, to continue using the updated c_t and h_t along all the sequences in the batch? (and then to reset them when moving to the next batch).