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model.py
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import os
import math
import random
import numpy as np
import tensorflow as tf
from past.builtins import xrange
class MemN2N(object):
def __init__(self, config, sess):
self.nwords = config.nwords
self.nwords = config.nwords
self.init_hid = config.init_hid
self.init_std = config.init_std
self.batch_size = config.batch_size
self.nepoch = config.nepoch
self.nhop = config.nhop
self.edim = config.edim
self.mem_size = config.mem_size
self.lindim = config.lindim
self.max_grad_norm = config.max_grad_norm
self.show = config.show
self.is_test = config.is_test
self.checkpoint_dir = config.checkpoint_dir
if not os.path.isdir(self.checkpoint_dir):
raise Exception(" [!] Directory %s not found" % self.checkpoint_dir)
self.input = tf.placeholder(tf.float32, [None, self.edim], name="input")
self.time = tf.placeholder(tf.int32, [None, self.mem_size], name="time")
self.target = tf.placeholder(tf.float32, [self.batch_size, self.nwords], name="target")
self.context = tf.placeholder(tf.int32, [self.batch_size, self.mem_size], name="context")
self.hid = []
self.hid.append(self.input)
self.share_list = []
self.share_list.append([])
self.lr = None
self.current_lr = config.init_lr
self.loss = None
self.step = None
self.optim = None
self.sess = sess
self.log_loss = []
self.log_perp = []
def build_memory(self):
self.global_step = tf.Variable(0, name="global_step")
self.A = tf.Variable(tf.random_normal([self.nwords, self.edim], stddev=self.init_std))
self.B = tf.Variable(tf.random_normal([self.nwords, self.edim], stddev=self.init_std))
self.C = tf.Variable(tf.random_normal([self.edim, self.edim], stddev=self.init_std))
# Temporal Encoding
self.T_A = tf.Variable(tf.random_normal([self.mem_size, self.edim], stddev=self.init_std))
self.T_B = tf.Variable(tf.random_normal([self.mem_size, self.edim], stddev=self.init_std))
# m_i = sum A_ij * x_ij + T_A_i
Ain_c = tf.nn.embedding_lookup(self.A, self.context)
Ain_t = tf.nn.embedding_lookup(self.T_A, self.time)
Ain = tf.add(Ain_c, Ain_t)
# c_i = sum B_ij * u + T_B_i
Bin_c = tf.nn.embedding_lookup(self.B, self.context)
Bin_t = tf.nn.embedding_lookup(self.T_B, self.time)
Bin = tf.add(Bin_c, Bin_t)
for h in xrange(self.nhop):
self.hid3dim = tf.reshape(self.hid[-1], [-1, 1, self.edim])
Aout = tf.batch_matmul(self.hid3dim, Ain, adj_y=True)
Aout2dim = tf.reshape(Aout, [-1, self.mem_size])
P = tf.nn.softmax(Aout2dim)
probs3dim = tf.reshape(P, [-1, 1, self.mem_size])
Bout = tf.batch_matmul(probs3dim, Bin)
Bout2dim = tf.reshape(Bout, [-1, self.edim])
Cout = tf.matmul(self.hid[-1], self.C)
Dout = tf.add(Cout, Bout2dim)
self.share_list[0].append(Cout)
if self.lindim == self.edim:
self.hid.append(Dout)
elif self.lindim == 0:
self.hid.append(tf.nn.relu(Dout))
else:
F = tf.slice(Dout, [0, 0], [self.batch_size, self.lindim])
G = tf.slice(Dout, [0, self.lindim], [self.batch_size, self.edim-self.lindim])
K = tf.nn.relu(G)
self.hid.append(tf.concat(1, [F, K]))
def build_model(self):
self.build_memory()
self.W = tf.Variable(tf.random_normal([self.edim, self.nwords], stddev=self.init_std))
z = tf.matmul(self.hid[-1], self.W)
self.loss = tf.nn.softmax_cross_entropy_with_logits(z, self.target)
self.lr = tf.Variable(self.current_lr)
self.opt = tf.train.GradientDescentOptimizer(self.lr)
params = [self.A, self.B, self.C, self.T_A, self.T_B, self.W]
grads_and_vars = self.opt.compute_gradients(self.loss,params)
clipped_grads_and_vars = [(tf.clip_by_norm(gv[0], self.max_grad_norm), gv[1]) \
for gv in grads_and_vars]
inc = self.global_step.assign_add(1)
with tf.control_dependencies([inc]):
self.optim = self.opt.apply_gradients(clipped_grads_and_vars)
tf.initialize_all_variables().run()
self.saver = tf.train.Saver()
def train(self, data):
N = int(math.ceil(len(data) / self.batch_size))
cost = 0
x = np.ndarray([self.batch_size, self.edim], dtype=np.float32)
time = np.ndarray([self.batch_size, self.mem_size], dtype=np.int32)
target = np.zeros([self.batch_size, self.nwords]) # one-hot-encoded
context = np.ndarray([self.batch_size, self.mem_size])
x.fill(self.init_hid)
for t in xrange(self.mem_size):
time[:,t].fill(t)
if self.show:
from utils import ProgressBar
bar = ProgressBar('Train', max=N)
for idx in xrange(N):
if self.show: bar.next()
target.fill(0)
for b in xrange(self.batch_size):
m = random.randrange(self.mem_size, len(data))
target[b][data[m]] = 1
context[b] = data[m - self.mem_size:m]
_, loss, self.step = self.sess.run([self.optim,
self.loss,
self.global_step],
feed_dict={
self.input: x,
self.time: time,
self.target: target,
self.context: context})
cost += np.sum(loss)
if self.show: bar.finish()
return cost/N/self.batch_size
def test(self, data, label='Test'):
N = int(math.ceil(len(data) / self.batch_size))
cost = 0
x = np.ndarray([self.batch_size, self.edim], dtype=np.float32)
time = np.ndarray([self.batch_size, self.mem_size], dtype=np.int32)
target = np.zeros([self.batch_size, self.nwords]) # one-hot-encoded
context = np.ndarray([self.batch_size, self.mem_size])
x.fill(self.init_hid)
for t in xrange(self.mem_size):
time[:,t].fill(t)
if self.show:
from utils import ProgressBar
bar = ProgressBar(label, max=N)
m = self.mem_size
for idx in xrange(N):
if self.show: bar.next()
target.fill(0)
for b in xrange(self.batch_size):
target[b][data[m]] = 1
context[b] = data[m - self.mem_size:m]
m += 1
if m >= len(data):
m = self.mem_size
loss = self.sess.run([self.loss], feed_dict={self.input: x,
self.time: time,
self.target: target,
self.context: context})
cost += np.sum(loss)
if self.show: bar.finish()
return cost/N/self.batch_size
def run(self, train_data, test_data):
if not self.is_test:
for idx in xrange(self.nepoch):
train_loss = np.sum(self.train(train_data))
test_loss = np.sum(self.test(test_data, label='Validation'))
# Logging
self.log_loss.append([train_loss, test_loss])
self.log_perp.append([math.exp(train_loss), math.exp(test_loss)])
state = {
'perplexity': math.exp(train_loss),
'epoch': idx,
'learning_rate': self.current_lr,
'valid_perplexity': math.exp(test_loss)
}
print(state)
# Learning rate annealing
if len(self.log_loss) > 1 and self.log_loss[idx][1] > self.log_loss[idx-1][1] * 0.9999:
self.current_lr = self.current_lr / 1.5
self.lr.assign(self.current_lr).eval()
if self.current_lr < 1e-5: break
if idx % 10 == 0:
self.saver.save(self.sess,
os.path.join(self.checkpoint_dir, "MemN2N.model"),
global_step = self.step.astype(int))
else:
self.load()
valid_loss = np.sum(self.test(train_data, label='Validation'))
test_loss = np.sum(self.test(test_data, label='Test'))
state = {
'valid_perplexity': math.exp(valid_loss),
'test_perplexity': math.exp(test_loss)
}
print(state)
def load(self):
print(" [*] Reading checkpoints...")
ckpt = tf.train.get_checkpoint_state(self.checkpoint_dir)
if ckpt and ckpt.model_checkpoint_path:
self.saver.restore(self.sess, ckpt.model_checkpoint_path)
else:
raise Exception(" [!] Trest mode but no checkpoint found")