Simple RNN (http://nbviewer.jupyter.org/gist/yoavg/d76121dfde2618422139)

rnn.py

#!/usr/bin/env python3

from collections import *
from random import random

def train_char_lm(fname, order=4):
	with open(fname, 'r') as f:
		data = f.read()
	lm = defaultdict(Counter)
	pad = '~' * order
	data = pad + data
	for i in range(len(data)-order):
		history, char = data[i:i+order], data[i+order]
		lm[history][char]+=1
	def normalize(counter):
		s = float(sum(counter.values()))
		return [(c,cnt/s) for c,cnt in counter.items()]
	outlm = {hist:normalize(chars) for hist, chars in lm.items()}
	return outlm

def generate_letter(lm, history, order):
	history = history[-order:]
	dist = lm[history]
	x = random()
	for c,v in dist:
		x = x - v
		if x <= 0: return c
		
def generate_text(lm, order, nletters=1000):
	history = '~' * order
	out = []
	for _ in range(nletters):
		c = generate_letter(lm, history, order)
		history = history[-order:] + c
		out.append(c)
	return ''.join(out)
			
if __name__ == '__main__':
	order = 4
	lm = train_char_lm('input.txt', order=order)
	print(generate_text(lm, order))

Thanks for the code, but this is not RNN. This is char-level n-grams without RNN.