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memnn_theano_v3.py
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memnn_theano_v3.py
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import numpy as np
import theano
import theano.tensor as T
import sys, random, pprint
from theano_util import *
from keras.activations import tanh, hard_sigmoid
from keras.initializations import glorot_uniform, orthogonal
from keras.utils.theano_utils import shared_zeros, alloc_zeros_matrix
def inspect_inputs(i, node, fn):
print i, node, "inputs:", [input[0] for input in fn.inputs],
def inspect_outputs(i, node, fn):
print i, node, "outputs:", [output[0] for output in fn.outputs]
class MemNN:
def __init__(self, n_words=1000, n_embedding=100, lr=0.01, margin=0.1, momentum=0.9, word_to_id=None):
self.n_embedding = n_embedding
self.n_lstm_embed = n_embedding
self.word_embed = n_embedding
self.lr = lr
self.momentum = momentum
self.margin = margin
self.n_words = n_words
self.n_D = 3 * self.n_words + 3
self.word_to_id = word_to_id
self.id_to_word = dict((v, k) for k, v in word_to_id.iteritems())
# Question
x = T.vector('x')
phi_x = T.vector('phi_x')
# True statements
phi_f1_1 = T.vector('phi_f1_1')
phi_f2_1 = T.vector('phi_f2_1')
# False statements
phi_f1_2 = T.vector('phi_f1_2')
phi_f2_2 = T.vector('phi_f2_2')
# Supporting memories
m0 = T.vector('m0')
m1 = T.vector('m1')
phi_m0 = T.vector('phi_m0')
phi_m1 = T.vector('phi_m1')
# True word
r = T.vector('r')
# Word sequence
words = T.ivector('words')
# Scoring function
self.U_O = init_shared_normal(n_embedding, self.n_D, 0.01)
# Word embedding
self.L = glorot_uniform((self.n_words, self.word_embed))
self.Lprime = glorot_uniform((self.n_words, self.n_lstm_embed))
# LSTM
self.W_i = glorot_uniform((self.word_embed, self.n_lstm_embed))
self.U_i = orthogonal((self.n_lstm_embed, self.n_lstm_embed))
self.b_i = shared_zeros((self.n_lstm_embed))
self.W_f = glorot_uniform((self.word_embed, self.n_lstm_embed))
self.U_f = orthogonal((self.n_lstm_embed, self.n_lstm_embed))
self.b_f = shared_zeros((self.n_lstm_embed))
self.W_c = glorot_uniform((self.word_embed, self.n_lstm_embed))
self.U_c = orthogonal((self.n_lstm_embed, self.n_lstm_embed))
self.b_c = shared_zeros((self.n_lstm_embed))
self.W_o = glorot_uniform((self.word_embed, self.n_lstm_embed))
self.U_o = orthogonal((self.n_lstm_embed, self.n_lstm_embed))
self.b_o = shared_zeros((self.n_lstm_embed))
mem_cost = self.calc_cost(phi_x, phi_f1_1, phi_f1_2, phi_f2_1, phi_f2_2, phi_m0)
lstm_output = self.lstm_cost(words)
self.predict_function_r = theano.function(inputs = [words], outputs = lstm_output, allow_input_downcast=True)
lstm_cost = -T.sum(T.mul(r, T.log(lstm_output)))
cost = mem_cost + lstm_cost
params = [
self.U_O,
self.W_i, self.U_i, self.b_i,
self.W_f, self.U_f, self.b_f,
self.W_c, self.U_c, self.b_c,
self.W_o, self.U_o, self.b_o,
self.L, self.Lprime
]
grads = T.grad(cost, params)
# Parameter updates
updates = self.get_updates(params, grads, method='adagrad')
l_rate = T.scalar('l_rate')
# Theano functions
self.train_function = theano.function(
inputs = [phi_x, phi_f1_1, phi_f1_2, phi_f2_1, phi_f2_2,
phi_m0, r, words,
theano.Param(l_rate, default=self.lr)],
outputs = cost,
updates = updates,
on_unused_input='warn',
allow_input_downcast=True,
)
#mode='FAST_COMPILE')
#mode='DebugMode')
#mode=theano.compile.MonitorMode(pre_func=inspect_inputs,post_func=inspect_outputs))
# Candidate statement for prediction
phi_f = T.vector('phi_f')
score_o = self.calc_score_o(phi_x, phi_f)
self.predict_function_o = theano.function(inputs = [phi_x, phi_f], outputs = score_o)
def get_updates(self, params, grads, method=None, **kwargs):
self.rho = 0.95
self.epsilon = 1e-6
accumulators = [shared_zeros(p.get_value().shape) for p in params]
updates=[]
if method == 'adadelta':
print "Using ADADELTA"
delta_accumulators = [shared_zeros(p.get_value().shape) for p in params]
for p, g, a, d_a in zip(params, grads, accumulators, delta_accumulators):
new_a = self.rho * a + (1 - self.rho) * g ** 2 # update accumulator
updates.append((a, new_a))
# use the new accumulator and the *old* delta_accumulator
update = g * T.sqrt(d_a + self.epsilon) / T.sqrt(new_a + self.epsilon)
new_p = p - self.lr * update
updates.append((p, new_p)) # apply constraints
# update delta_accumulator
new_d_a = self.rho * d_a + (1 - self.rho) * update ** 2
updates.append((d_a, new_d_a))
elif method == 'adam':
# unimplemented
print "Using ADAM"
elif method == 'adagrad':
print "Using ADAGRAD"
for p, g, a in zip(params, grads, accumulators):
new_a = a + g ** 2 # update accumulator
updates.append((a, new_a))
new_p = p - self.lr * g / T.sqrt(new_a + self.epsilon)
updates.append((p, new_p)) # apply constraints
else: # Default
print "Using MOMENTUM"
l_rate = kwargs['l_rate']
for param, gparam in zip(params, gradient):
param_update = theano.shared(param.get_value()*0., broadcastable=param.broadcastable)
updates.append((param, param - param_update * l_rate))
updates.append((param_update, self.momentum*param_update + (1. - self.momentum)*gparam))
return updates
def _step(self,
xi_t, xf_t, xc_t, xo_t,
h_tm1, c_tm1,
u_i, u_f, u_o, u_c):
i_t = hard_sigmoid(xi_t + T.dot(h_tm1, u_i))
f_t = hard_sigmoid(xf_t + T.dot(h_tm1, u_f))
c_t = f_t * c_tm1 + i_t * tanh(xc_t + T.dot(h_tm1, u_c))
o_t = hard_sigmoid(xo_t + T.dot(h_tm1, u_o))
h_t = o_t * tanh(c_t)
return h_t, c_t
# words: word index in n_words
def lstm_cost(self, words):
x = self.L[words]
# Each element of x is (word_embed,) shape
xi = T.dot(x, self.W_i) + self.b_i
xf = T.dot(x, self.W_f) + self.b_f
xc = T.dot(x, self.W_c) + self.b_c
xo = T.dot(x, self.W_o) + self.b_o
[outputs, memories], updates = theano.scan(
self._step,
sequences=[xi, xf, xc, xo],
outputs_info=[
alloc_zeros_matrix(self.n_lstm_embed),
alloc_zeros_matrix(self.n_lstm_embed),
],
non_sequences=[
self.U_i, self.U_f, self.U_o, self.U_c,
],
truncate_gradient=-1
)
r = T.dot(self.Lprime, outputs[-1])
return T.nnet.softmax(r)
def calc_score_o(self, phi_x, phi_y_yp_t):
return T.dot(self.U_O.dot(phi_x), self.U_O.dot(phi_y_yp_t))
# phi_f1_1 = phi_f1 - phi_f1bar + phi_t1_1
# phi_f1_2 = phi_f1bar - phi_f1 + phi_t1_2
def calc_cost(self, phi_x, phi_f1_1, phi_f1_2, phi_f2_1, phi_f2_2, phi_m0):
score1_1 = self.calc_score_o(phi_x, phi_f1_1)
score1_2 = self.calc_score_o(phi_x, phi_f1_2)
score2_1 = self.calc_score_o(phi_x + phi_m0, phi_f2_1)
score2_2 = self.calc_score_o(phi_x + phi_m0, phi_f2_2)
s_o_cost = (
T.maximum(0, self.margin - score1_1) + T.maximum(0, self.margin + score1_2) +
T.maximum(0, self.margin - score2_1) + T.maximum(0, self.margin + score2_2)
)
return s_o_cost
def construct_phi(self, phi_type, bow=None, word_id=None, ids=None):
# type 0: question (phi_x)
# type 1: supporting memory (phi_m*)
# type 2: candidate memory (phi_y)
# type 3: word vector
# type 4: write-time features
# type 5: 0s
assert(phi_type >= 0 and phi_type < 6)
phi = np.zeros((3*self.n_words + 3,))
if phi_type < 3:
assert(bow is not None)
phi[phi_type*self.n_words:(phi_type+1)*self.n_words] = bow
elif phi_type == 3:
assert(word_id != None and word_id < self.n_words)
phi[2*self.n_words + word_id] = 1
elif phi_type == 5:
pass
else:
assert(ids != None and len(ids) == 3)
if ids[0] > ids[1]: phi[3*self.n_words] = 1
if ids[0] > ids[2]: phi[3*self.n_words+1] = 1
if ids[1] > ids[2]: phi[3*self.n_words+2] = 1
return phi
def make_one_hot(self, index):
v = np.zeros((self.n_words))
v[index] = 1.0
return v
# returns (phi_y - phi_yp + phi_t)
def construct_wt_phi(self, index_x, index_y, index_yp, y, yp):
phi_y = self.construct_phi(2, bow=y)
phi_yp = self.construct_phi(2, bow=yp)
phi_t = self.construct_phi(4, ids=[index_x, index_y, index_yp])
return phi_y - phi_yp + phi_t
def neg_sample(self, c, num):
assert(c < num)
assert(num > 1)
f = random.randint(0, num-2)
if f == c:
f = num-1
return f
def find_m0(self, index_x, phi_x, statements, ignore=None):
max_score = float("-inf")
index_m0 = 0
m0 = statements[0]
for i in xrange(1,len(statements)):
if ignore and i == ignore:
continue
s = statements[i]
phi_s = self.construct_wt_phi(index_x, i, index_m0, s, m0)
if self.predict_function_o(phi_x, phi_s) >= 0:
index_m0 = i
m0 = s
return index_m0, m0
def train(self, dataset_seq, dataset_bow, questions, n_epochs=100, lr_schedule=None):
l_rate = self.lr
for epoch in xrange(n_epochs):
costs = []
if lr_schedule != None and epoch in lr_schedule:
l_rate = lr_schedule[epoch]
random.shuffle(questions)
for i, question in enumerate(questions):
article_no = question[0]
article = dataset_bow[article_no]
line_no = question[1]
question_phi = question[2]
correct_stmts = question[4].split(' ')
correct_stmt1 = int(correct_stmts[0])
is_single_statement = len(correct_stmts) == 1
correct_stmt2 = None
if not is_single_statement:
correct_stmt2 = int(correct_stmts[1])
question_seq = question[-1]
if line_no <= 1:
continue
# The question
phi_x = self.construct_phi(0, bow=question_phi)
# Find m0
index_m0, m0 = self.find_m0(line_no, phi_x, article[:line_no])
phi_m0 = self.construct_phi(1, bow=m0)
# Find m1
index_m1, m1 = self.find_m0(index_m0, phi_x + phi_m0, article[:line_no], ignore=index_m0)
phi_m1 = self.construct_phi(1, bow=m1)
# False statement 1
false_stmt1 = index_m0
if false_stmt1 == correct_stmt1:
false_stmt1 = self.neg_sample(correct_stmt1, line_no)
phi_f1_1 = self.construct_wt_phi(line_no, correct_stmt1, false_stmt1, article[correct_stmt1], article[false_stmt1])
phi_f1_2 = self.construct_wt_phi(line_no, false_stmt1, correct_stmt1, article[false_stmt1], article[correct_stmt1])
# False statement 2
phi_f2_1 = None
phi_f2_2 = None
if not is_single_statement:
false_stmt2 = index_m1
if false_stmt2 == correct_stmt2:
false_stmt2 = self.neg_sample(correct_stmt2, line_no)
phi_f2_1 = self.construct_wt_phi(line_no, correct_stmt2, false_stmt2, article[correct_stmt2], article[false_stmt2])
phi_f2_2 = self.construct_wt_phi(line_no, false_stmt2, correct_stmt2, article[false_stmt2], article[correct_stmt2])
else:
phi_f2_1 = self.construct_phi(5)
phi_f2_2 = self.construct_phi(5)
# Correct word
correct_word = question[3]
r = self.make_one_hot(correct_word)
words = np.asarray(dataset_seq[article_no][index_m0] + dataset_seq[article_no][index_m1] + question_seq)
cost = self.train_function(phi_x, phi_f1_1, phi_f1_2, phi_f2_1, phi_f2_2,
phi_m0, r, words)
#print "%d: %f" % (i, cost)
costs.append(cost)
print "Epoch %d: %f" % (epoch, np.mean(costs))
def find_word(self, words):
probs = self.predict_function_r(words)
return np.argmax(probs)
def predict(self, dataset_seq, dataset_bow, questions):
correct_answers = 0
wrong_answers = 0
fake_correct_answers = 0
for i, question in enumerate(questions):
article_no = question[0]
line_no = question[1]
question_phi = question[2]
correct = question[3]
question_seq = question[-1]
x = question_phi
phi_x = self.construct_phi(0, bow=question_phi)
statements = dataset_bow[article_no]
phi_m0 = None
phi_m1 = None
if len(statements) == 0:
print "Stupid question"
continue
elif len(statements) == 1:
print "Stupid question?"
phi_m0 = self.construct_phi(1, statements[0])
phi_m1 = self.construct_phi(1, statements[0])
else:
index_m0, m0 = self.find_m0(line_no, phi_x, statements[:line_no])
phi_m0 = self.construct_phi(1, m0)
index_m1, m1 = self.find_m0(index_m0, phi_x + phi_m0, statements[:line_no], ignore=index_m0)
correct_stmts = question[4].split(' ')
is_single_statement = len(correct_stmts) == 1
c1 = int(correct_stmts[0])
c2 = int(question[4].split(' ')[1]) if not is_single_statement else None
if (index_m0 == c1 or index_m0 == c2) and (index_m1 == c1 or index_m1 == c2):
fake_correct_answers += 1
predicted = self.find_word(
np.asarray(dataset_seq[article_no][index_m0] + dataset_seq[article_no][index_m1] + question_seq)
)
# print 'Correct: %s (%d), Guess: %s (%d)' % (self.id_to_word[correct], correct, self.id_to_word[predicted], predicted)
if predicted == correct:
correct_answers += 1
else:
wrong_answers += 1
print '%d correct, %d wrong, %d fake_correct' % (correct_answers, wrong_answers, fake_correct_answers)
def train_weak(self, dataset, questions, n_epochs=100, lr_schedule=None):
l_rate = self.lr
for epoch in xrange(n_epochs):
costs = []
if lr_schedule != None and epoch in lr_schedule:
l_rate = lr_schedule[epoch]
random.shuffle(questions)
for i, question in enumerate(questions):
article_no = question[0]
article = dataset[article_no]
line_no = question[1]
statements_seq = question[2][:-1]
question_seq = question[2][-1]
if line_no <= 1:
continue
# Correct word
correct_word = question[3]
cost = self.train_function(statements_seq, question_seq, correct_word)
#print "%d: %f" % (i, cost)
costs.append(cost)
print "Epoch %d: %f" % (epoch, np.mean(costs))
def predict_weak(self, dataset, questions):
correct_answers = 0
wrong_answers = 0
for i, question in enumerate(questions):
article_no = question[0]
article = dataset[article_no]
line_no = question[1]
statements_seq = question[2][:-1]
question_seq = question[2][-1]
correct = question[3]
predicted = self.predict_function(
np.asarray(statements_seq), np.asarray(question_seq)
)
# print 'Correct: %s (%d), Guess: %s (%d)' % (self.id_to_word[correct], correct, self.id_to_word[predicted], predicted)
if predicted == correct:
correct_answers += 1
else:
wrong_answers += 1
print '%d correct, %d wrong' % (correct_answers, wrong_answers)
if __name__ == "__main__":
train_file = sys.argv[1]
test_file = train_file.replace('train', 'test')
train_dataset_seq, train_dataset_bow, train_questions, word_to_id, num_words = parse_dataset(train_file)
test_dataset_seq, test_dataset_bow, test_questions, _, _ = parse_dataset(test_file, word_id=num_words, word_to_id=word_to_id, update_word_ids=False)
if len(sys.argv) > 2:
n_epochs = int(sys.argv[2])
else:
n_epochs = 10
memNN = MemNN(n_words=num_words, n_embedding=100, lr=0.01, margin=0.1, word_to_id=word_to_id)
#memNN.train(train_dataset_seq, train_dataset_bow, train_questions, n_epochs=n_epochs, lr_schedule=dict([(0, 0.02), (20, 0.01), (50, 0.005), (80, 0.002)]))
#memNN.train(train_dataset_seq, train_dataset_bow, train_questions, lr_schedule=dict([(0, 0.01), (15, 0.009), (30, 0.007), (50, 0.005), (60, 0.003), (85, 0.001)]))
#memNN.train(train_dataset_seq, train_dataset_bow, train_questions)
#memNN.predict(train_dataset, train_questions)
#memNN.predict(test_dataset_seq, test_dataset_bow, test_questions)
for i in xrange(20):
memNN.train(train_dataset_seq, train_dataset_bow, train_questions, n_epochs=5)
memNN.predict(test_dataset_seq, test_dataset_bow, test_questions)