forked from crossderek/chatbot-MemN2N-tensorflow
/
single_dialog.py
232 lines (215 loc) · 10.2 KB
/
single_dialog.py
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from __future__ import absolute_import
from __future__ import print_function
from data_utils import load_dialog_task, vectorize_data, load_candidates, vectorize_candidates, vectorize_candidates_sparse, tokenize
from sklearn import metrics
from memn2n import MemN2NDialog
from itertools import chain
from six.moves import range, reduce
import sys
import tensorflow as tf
import numpy as np
import os
tf.flags.DEFINE_float("learning_rate", 0.001,
"Learning rate for Adam Optimizer.")
tf.flags.DEFINE_float("epsilon", 1e-8, "Epsilon value for Adam Optimizer.")
tf.flags.DEFINE_float("max_grad_norm", 40.0, "Clip gradients to this norm.")
tf.flags.DEFINE_integer("evaluation_interval", 10,
"Evaluate and print results every x epochs")
tf.flags.DEFINE_integer("batch_size", 32, "Batch size for training.")
tf.flags.DEFINE_integer("hops", 3, "Number of hops in the Memory Network.")
tf.flags.DEFINE_integer("epochs", 200, "Number of epochs to train for.")
tf.flags.DEFINE_integer("embedding_size", 20,
"Embedding size for embedding matrices.")
tf.flags.DEFINE_integer("memory_size", 50, "Maximum size of memory.")
tf.flags.DEFINE_integer("task_id", 6, "bAbI task id, 1 <= id <= 6")
tf.flags.DEFINE_integer("random_state", None, "Random state.")
tf.flags.DEFINE_string("data_dir", "data/dialog-bAbI-tasks/",
"Directory containing bAbI tasks")
tf.flags.DEFINE_string("model_dir", "model/",
"Directory containing memn2n model checkpoints")
tf.flags.DEFINE_boolean('train', True, 'if True, begin to train')
tf.flags.DEFINE_boolean('interactive', False, 'if True, interactive')
tf.flags.DEFINE_boolean('OOV', False, 'if True, use OOV test set')
FLAGS = tf.flags.FLAGS
print("Started Task:", FLAGS.task_id)
class chatBot(object):
def __init__(self, data_dir, model_dir, task_id, isInteractive=True, OOV=False, memory_size=50, random_state=None, batch_size=32, learning_rate=0.001, epsilon=1e-8, max_grad_norm=40.0, evaluation_interval=10, hops=3, epochs=200, embedding_size=20):
self.data_dir = data_dir
self.task_id = task_id
self.model_dir = model_dir
# self.isTrain=isTrain
self.isInteractive = isInteractive
self.OOV = OOV
self.memory_size = memory_size
self.random_state = random_state
self.batch_size = batch_size
self.learning_rate = learning_rate
self.epsilon = epsilon
self.max_grad_norm = max_grad_norm
self.evaluation_interval = evaluation_interval
self.hops = hops
self.epochs = epochs
self.embedding_size = embedding_size
candidates, self.candid2indx = load_candidates(
self.data_dir, self.task_id)
self.n_cand = len(candidates)
print("Candidate Size", self.n_cand)
self.indx2candid = dict(
(self.candid2indx[key], key) for key in self.candid2indx)
# task data
self.trainData, self.testData, self.valData = load_dialog_task(
self.data_dir, self.task_id, self.candid2indx, self.OOV)
data = self.trainData + self.testData + self.valData
self.build_vocab(data, candidates)
# self.candidates_vec=vectorize_candidates_sparse(candidates,self.word_idx)
self.candidates_vec = vectorize_candidates(
candidates, self.word_idx, self.candidate_sentence_size)
optimizer = tf.train.AdamOptimizer(
learning_rate=self.learning_rate, epsilon=self.epsilon)
self.sess = tf.Session()
self.model = MemN2NDialog(self.batch_size, self.vocab_size, self.n_cand, self.sentence_size, self.embedding_size, self.candidates_vec, session=self.sess,
hops=self.hops, max_grad_norm=self.max_grad_norm, optimizer=optimizer, task_id=task_id)
self.saver = tf.train.Saver(max_to_keep=50)
self.summary_writer = tf.summary.FileWriter(
self.model.root_dir, self.model.graph_output.graph)
def build_vocab(self, data, candidates):
vocab = reduce(lambda x, y: x | y, (set(
list(chain.from_iterable(s)) + q) for s, q, a in data))
vocab |= reduce(lambda x, y: x | y, (set(candidate)
for candidate in candidates))
vocab = sorted(vocab)
self.word_idx = dict((c, i + 1) for i, c in enumerate(vocab))
max_story_size = max(map(len, (s for s, _, _ in data)))
mean_story_size = int(np.mean([len(s) for s, _, _ in data]))
self.sentence_size = max(
map(len, chain.from_iterable(s for s, _, _ in data)))
self.candidate_sentence_size = max(map(len, candidates))
query_size = max(map(len, (q for _, q, _ in data)))
self.memory_size = min(self.memory_size, max_story_size)
self.vocab_size = len(self.word_idx) + 1 # +1 for nil word
self.sentence_size = max(
query_size, self.sentence_size) # for the position
# params
print("vocab size:", self.vocab_size)
print("Longest sentence length", self.sentence_size)
print("Longest candidate sentence length",
self.candidate_sentence_size)
print("Longest story length", max_story_size)
print("Average story length", mean_story_size)
def interactive(self):
context = []
u = None
r = None
nid = 1
while True:
line = raw_input('--> ').strip().lower()
if line == 'exit':
break
if line == 'restart':
context = []
nid = 1
print("clear memory")
continue
u = tokenize(line)
data = [(context, u, -1)]
s, q, a = vectorize_data(
data, self.word_idx, self.sentence_size, self.batch_size, self.n_cand, self.memory_size)
preds = self.model.predict(s, q)
r = self.indx2candid[preds[0]]
print(r)
r = tokenize(r)
u.append('$u')
u.append('#' + str(nid))
r.append('$r')
r.append('#' + str(nid))
context.append(u)
context.append(r)
nid += 1
def train(self):
trainS, trainQ, trainA = vectorize_data(
self.trainData, self.word_idx, self.sentence_size, self.batch_size, self.n_cand, self.memory_size)
valS, valQ, valA = vectorize_data(
self.valData, self.word_idx, self.sentence_size, self.batch_size, self.n_cand, self.memory_size)
n_train = len(trainS)
n_val = len(valS)
print("Training Size", n_train)
print("Validation Size", n_val)
tf.set_random_seed(self.random_state)
batches = zip(range(0, n_train - self.batch_size, self.batch_size),
range(self.batch_size, n_train, self.batch_size))
batches = [(start, end) for start, end in batches]
best_validation_accuracy = 0
for t in range(1, self.epochs + 1):
np.random.shuffle(batches)
total_cost = 0.0
for start, end in batches:
s = trainS[start:end]
q = trainQ[start:end]
a = trainA[start:end]
cost_t = self.model.batch_fit(s, q, a)
total_cost += cost_t
if t % self.evaluation_interval == 0:
train_preds = self.batch_predict(trainS, trainQ, n_train)
val_preds = self.batch_predict(valS, valQ, n_val)
train_acc = metrics.accuracy_score(
np.array(train_preds), trainA)
val_acc = metrics.accuracy_score(val_preds, valA)
print('-----------------------')
print('Epoch', t)
print('Total Cost:', total_cost)
print('Training Accuracy:', train_acc)
print('Validation Accuracy:', val_acc)
print('-----------------------')
# write summary
train_acc_summary = tf.summary.scalar(
'task_' + str(self.task_id) + '/' + 'train_acc', tf.constant((train_acc), dtype=tf.float32))
val_acc_summary = tf.summary.scalar(
'task_' + str(self.task_id) + '/' + 'val_acc', tf.constant((val_acc), dtype=tf.float32))
merged_summary = tf.summary.merge(
[train_acc_summary, val_acc_summary])
summary_str = self.sess.run(merged_summary)
self.summary_writer.add_summary(summary_str, t)
self.summary_writer.flush()
if val_acc > best_validation_accuracy:
best_validation_accuracy = val_acc
self.saver.save(self.sess, self.model_dir +
'model.ckpt', global_step=t)
def test(self):
ckpt = tf.train.get_checkpoint_state(self.model_dir)
if ckpt and ckpt.model_checkpoint_path:
self.saver.restore(self.sess, ckpt.model_checkpoint_path)
else:
print("...no checkpoint found...")
if self.isInteractive:
self.interactive()
else:
testS, testQ, testA = vectorize_data(
self.testData, self.word_idx, self.sentence_size, self.batch_size, self.n_cand, self.memory_size)
n_test = len(testS)
print("Testing Size", n_test)
test_preds = self.batch_predict(testS, testQ, n_test)
test_acc = metrics.accuracy_score(test_preds, testA)
print("Testing Accuracy:", test_acc)
def batch_predict(self, S, Q, n):
preds = []
for start in range(0, n, self.batch_size):
end = start + self.batch_size
s = S[start:end]
q = Q[start:end]
pred = self.model.predict(s, q)
preds += list(pred)
return preds
def close_session(self):
self.sess.close()
if __name__ == '__main__':
model_dir = "task" + str(FLAGS.task_id) + "_" + FLAGS.model_dir
if not os.path.exists(model_dir):
os.makedirs(model_dir)
chatbot = chatBot(FLAGS.data_dir, model_dir, FLAGS.task_id, OOV=FLAGS.OOV,
isInteractive=FLAGS.interactive, batch_size=FLAGS.batch_size)
# chatbot.run()
if FLAGS.train:
chatbot.train()
else:
chatbot.test()
chatbot.close_session()