Motoki Wu tokestermw

Key-Value Memory Networks for Directly Reading Documents

Knowledge Bases (KBs) are effective tools for Question Answering (QA) but are often too restrictive (due to fixed schema) and too sparse (due to limitations of Information Extraction (IE) systems).
The paper proposes Key-Value Memory Networks, a neural network architecture based on Memory Networks that can leverage both KBs and raw data for QA.
The paper also introduces MOVIEQA, a new QA dataset that can be answered by a perfect KB, by Wikipedia pages and by an imperfect KB obtained using IE techniques thereby allowing a comparison between systems using any of the three sources.
Link to the paper.

	""" Trains an agent with (stochastic) Policy Gradients on Pong. Uses OpenAI Gym. """
	import numpy as np
	import cPickle as pickle
	import gym

	# hyperparameters
	H = 200 # number of hidden layer neurons
	batch_size = 10 # every how many episodes to do a param update?
	learning_rate = 1e-4
	gamma = 0.99 # discount factor for reward

	vocab=400000
	tokens=42
	dim=300
	filename="glove.${tokens}B.${dim}d"
	n="_unnorm"
	txt=".txt"
	bin=".bin"
	echo "${vocab} ${dim}" > $filename$n$txt
	cat $filename$txt >> $filename$n$txt

	"""
	from here: https://github.com/petered/plato/pull/56/files
	adapted by Motoki
	"""

	import hashlib
	from collections import OrderedDict
	import numpy as np
	import cPickle as pickle
	import os

	import array
	import numpy as np
	import scipy.sparse as sp


	class IncrementalCOOMatrix(object):

	def __init__(self, shape, dtype):

	if dtype is np.int32:

	from __future__ import division


	def average(x):
	return sum(x) / len(x)


	def wilson_lower_bound(count, total, z_score=1.96):
	""" Implementation of Wilson Scores

	import random


	class TicTacToe:
	def __init__(self, playerX, playerO):
	self.board = [' ']*9
	self.playerX, self.playerO = playerX, playerO
	self.playerX_turn = random.choice([True, False])

	def play_game(self):

	from __future__ import print_function

	from keras import backend as K
	from keras.engine import Input, Model, InputSpec
	from keras.layers import Dense, Activation, Dropout, Lambda
	from keras.layers import Embedding, LSTM
	from keras.optimizers import Adam
	from keras.preprocessing import sequence
	from keras.utils.data_utils import get_file
	from keras.datasets import imdb

	"""
	Play with saving .

	Closest:
	https://github.com/tensorflow/tensorflow/issues/616#issuecomment-205620223
	"""
	import numpy as np

	import tensorflow as tf
	from tensorflow.python.platform import gfile