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neural_conversation_model.py
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neural_conversation_model.py
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"""Most of the code comes from seq2seq tutorial. Binary for training conversation models and decoding from them.
Running this program without --decode will tokenize it in a very basic way,
and then start training a model saving checkpoints to --train_dir.
Running with --decode starts an interactive loop so you can see how
the current checkpoint performs
See the following papers for more information on neural translation models.
* http://arxiv.org/abs/1409.3215
* http://arxiv.org/abs/1409.0473
* http://arxiv.org/abs/1412.2007
"""
import math
import os
import random
import sys
import time
import numpy as np
from six.moves import xrange # pylint: disable=redefined-builtin
import tensorflow as tf
from data_utils import *
from seq2seq_model import *
import codecs
train_dir_in = './train_{}/'.format(int(time.time()))
tf.app.flags.DEFINE_float("learning_rate", 0.5, "Learning rate.")
tf.app.flags.DEFINE_float("learning_rate_decay_factor", 0.99,
"Learning rate decays by this much.")
tf.app.flags.DEFINE_float("max_gradient_norm", 5.0,
"Clip gradients to this norm.")
tf.app.flags.DEFINE_integer("batch_size", 64,
"Batch size to use during training.")
tf.app.flags.DEFINE_integer("size", 512, "Size of each model layer.")
tf.app.flags.DEFINE_integer("num_layers", 3, "Number of layers in the model.")
tf.app.flags.DEFINE_integer("en_vocab_size", 40000, "English vocabulary size.")
tf.app.flags.DEFINE_string("train_dir", train_dir_in, "Training directory.") #"./tmp/"
tf.app.flags.DEFINE_string("vocab_path", "./tmp/", "Data directory")
tf.app.flags.DEFINE_string("data_path", "./tmp/", "Training directory.")
tf.app.flags.DEFINE_string("dev_data", "./tmp/", "Data directory")
tf.app.flags.DEFINE_integer("max_train_data_size", 0,
"Limit on the size of training data (0: no limit).")
tf.app.flags.DEFINE_integer("steps_per_checkpoint", 400,
"How many training steps to do per checkpoint.")
tf.app.flags.DEFINE_integer("beam_size", 100,
"How many training steps to do per checkpoint.")
tf.app.flags.DEFINE_boolean("beam_search", False,
"Set to True for beam_search.")
tf.app.flags.DEFINE_boolean("decode", False,
"Set to True for interactive decoding.")
tf.app.flags.DEFINE_boolean("attention", False,
"Set to True for interactive decoding.")
tf.app.flags.DEFINE_boolean("self_test", False,
"Run a self-test if this is set to True.")
FLAGS = tf.app.flags.FLAGS
# We use a number of buckets and pad to the closest one for efficiency.
# See seq2seq_model.Seq2SeqModel for details of how they work.
_buckets = [(5, 10), (10, 15), (20, 25), (40, 50)]
def read_chat_data(data_path,vocabulary_path, max_size=None):
counter = 0
vocab, _ = initialize_vocabulary(vocabulary_path)
print("vocab number: {}".format(len(vocab)))
data_set = [[] for _ in _buckets]
# http://stackoverflow.com/questions/33054527/python-3-5-typeerror-a-bytes-like-object-is-required-not-str-when-writing-t
with codecs.open(data_path, "rb") as fi:
for line in fi.readlines():
line = line.decode('utf8').strip()
counter += 1
if max_size!=0 and counter > max_size:
break
if counter % 10000 == 0:
print(" reading data line %d" % counter)
sys.stdout.flush()
entities = line.lower().split("|")
# print entities
if len(entities) == 2:
source = entities[0]
target = entities[1]
source_ids = [int(x) for x in sentence_to_token_ids(source,vocab)]
target_ids = [int(x) for x in sentence_to_token_ids(target,vocab)]
target_ids.append(EOS_ID)
for bucket_id, (source_size, target_size) in enumerate(_buckets):
if len(source_ids) < source_size and len(target_ids) < target_size:
data_set[bucket_id].append([source_ids, target_ids])
break
return data_set
def create_model(session, forward_only, beam_search, beam_size = 10, attention = True):
"""Create translation model and initialize or load parameters in session."""
model = Seq2SeqModel(
FLAGS.en_vocab_size, FLAGS.en_vocab_size, _buckets,
FLAGS.size, FLAGS.num_layers, FLAGS.max_gradient_norm, FLAGS.batch_size,
FLAGS.learning_rate, FLAGS.learning_rate_decay_factor,
forward_only=forward_only, beam_search=beam_search, beam_size=beam_size, attention=attention)
print(FLAGS.train_dir)
ckpt = tf.train.get_checkpoint_state(FLAGS.train_dir)
# ckpt.model_checkpoint_path ="./big_models/chat_bot.ckpt-183600"
# print ckpt.model_checkpoint_path
if ckpt and tf.gfile.Exists(ckpt.model_checkpoint_path):
print("Reading model parameters from %s" % ckpt.model_checkpoint_path)
model.saver.restore(session, ckpt.model_checkpoint_path)
else:
print("Created model with fresh parameters.")
session.run(tf.global_variables_initializer())
return model
def create_models(path, en_vocab_size, session, forward_only, beam_search, beam_size = 10, attention = True):
"""Create translation model and initialize or load parameters in session."""
model = Seq2SeqModel(
en_vocab_size, en_vocab_size, _buckets,
FLAGS.size, FLAGS.num_layers, FLAGS.max_gradient_norm, FLAGS.batch_size,
FLAGS.learning_rate, FLAGS.learning_rate_decay_factor,
forward_only=forward_only, beam_search=beam_search, beam_size=beam_size, attention=attention)
print(FLAGS.train_dir)
ckpt = tf.train.get_checkpoint_state(path)
# ckpt.model_checkpoint_path ="./big_models/chat_bot.ckpt-183600"
# print ckpt.model_checkpoint_path
if ckpt and tf.gfile.Exists(ckpt.model_checkpoint_path):
print("Reading model parameters from %s" % ckpt.model_checkpoint_path)
model.saver.restore(session, ckpt.model_checkpoint_path)
else:
print("Created model with fresh parameters.")
session.run(tf.global_variables_initializer())
return model
def train():
data_path =FLAGS.data_path
dev_data = FLAGS.dev_data
vocab_path =FLAGS.vocab_path
# Beam search is false during training operation and usedat inference .
beam_search = False
beam_size =10
attention = FLAGS.attention
normalize_digits=True
create_vocabulary(vocab_path, data_path, FLAGS.en_vocab_size )
with tf.Session() as sess:
# Create model.
print("Creating %d layers of %d units." % (FLAGS.num_layers, FLAGS.size))
model = create_model(sess, False,beam_search=beam_search, beam_size=beam_size, attention=attention)
# Read data into buckets and compute their sizes.
print("Reading development and training data (limit: %d)."
% FLAGS.max_train_data_size)
train_set =read_chat_data(data_path,vocab_path, FLAGS.max_train_data_size)
dev_set =read_chat_data(dev_data,vocab_path, FLAGS.max_train_data_size)
train_bucket_sizes = [len(train_set[b]) for b in xrange(len(_buckets))]
train_total_size = float(sum(train_bucket_sizes))
# A bucket scale is a list of increasing numbers from 0 to 1 that we'll use
# to select a bucket. Length of [scale[i], scale[i+1]] is proportional to
# the size if i-th training bucket, as used later.
train_buckets_scale = [sum(train_bucket_sizes[:i + 1]) / train_total_size
for i in xrange(len(train_bucket_sizes))]
# This is the training loop.
step_time, loss = 0.0, 0.0
current_step = 0
previous_losses = []
while True:
# Choose a bucket according to data distribution. We pick a random number
# in [0, 1] and use the corresponding interval in train_buckets_scale.
# print "Started"
random_number_01 = np.random.random_sample()
bucket_id = min([i for i in xrange(len(train_buckets_scale))
if train_buckets_scale[i] > random_number_01])
# Get a batch and make a step.
start_time = time.time()
encoder_inputs, decoder_inputs, target_weights = model.get_batch(
train_set, bucket_id)
_, step_loss, _ = model.step(sess, encoder_inputs, decoder_inputs,
target_weights, bucket_id, False, beam_search)
step_time += (time.time() - start_time) / FLAGS.steps_per_checkpoint
loss += step_loss / FLAGS.steps_per_checkpoint
current_step += 1
# Once in a while, we save checkpoint, print statistics, and run evals.
if current_step % FLAGS.steps_per_checkpoint == 0:
# Print statistics for the previous epoch.
print("Running epochs")
perplexity = math.exp(loss) if loss < 300 else float('inf')
print ("global step %d learning rate %.4f step-time %.2f perplexity "
"%.2f" % (model.global_step.eval(), model.learning_rate.eval(),
step_time, perplexity))
# # Decrease learning rate if no improvement was seen over last 3 times.
if len(previous_losses) > 2 and loss > max(previous_losses[-3:]):
sess.run(model.learning_rate_decay_op)
previous_losses.append(loss)
# # Save checkpoint and zero timer and loss.
checkpoint_path = os.path.join(FLAGS.train_dir, "chat_bot.ckpt")
model.saver.save(sess, checkpoint_path, global_step=model.global_step)
step_time, loss = 0.0, 0.0
# # Run evals on development set and print their perplexity.
for bucket_id in xrange(len(_buckets)):
if len(dev_set[bucket_id]) == 0:
print(" eval: empty bucket %d" % (bucket_id))
continue
encoder_inputs, decoder_inputs, target_weights = model.get_batch(
dev_set, bucket_id)
_, eval_loss, _ = model.step(sess, encoder_inputs, decoder_inputs,
target_weights, bucket_id, True, beam_search)
eval_ppx = math.exp(eval_loss) if eval_loss < 300 else float('inf')
print(" eval: bucket %d perplexity %.2f" % (bucket_id, eval_ppx))
sys.stdout.flush()
def decode():
with tf.Session() as sess:
# Create model and load parameters.
beam_size = FLAGS.beam_size
beam_search = FLAGS.beam_search
attention = FLAGS.attention
model = create_model(sess, True, beam_search=beam_search, beam_size=beam_size, attention=attention)
model.batch_size = 1 # We decode one sentence at a time.
# Load vocabularies.
vocab_path = FLAGS.vocab_path
vocab, rev_vocab = initialize_vocabulary(vocab_path)
# Decode from standard input.
if beam_search:
sys.stdout.write("> ")
sys.stdout.flush()
sentence = sys.stdin.readline()
while sentence:
# Get token-ids for the input sentence.
token_ids = sentence_to_token_ids(sentence, vocab)#tf.compat.as_bytes(sentence)
# Which bucket does it belong to?
bucket_id = min([b for b in xrange(len(_buckets))
if _buckets[b][0] > len(token_ids)])
# Get a 1-element batch to feed the sentence to the model.
encoder_inputs, decoder_inputs, target_weights = model.get_batch(
{bucket_id: [(token_ids, [])]}, bucket_id)
# Get output logits for the sentence.
# print bucket_id
path, symbol , output_logits = model.step(sess, encoder_inputs, decoder_inputs,
target_weights, bucket_id, True,beam_search )
k = output_logits[0]
paths = []
for kk in range(beam_size):
paths.append([])
curr = list(range(beam_size))
num_steps = len(path)
for i in range(num_steps-1, -1, -1):
for kk in range(beam_size):
paths[kk].append(symbol[i][curr[kk]])
curr[kk] = path[i][curr[kk]]
recos = set()
print("Replies --------------------------------------->")
for kk in range(beam_size):
foutputs = [int(logit) for logit in paths[kk][::-1]]
# If there is an EOS symbol in outputs, cut them at that point.
if EOS_ID in foutputs:
# # print outputs
foutputs = foutputs[:foutputs.index(EOS_ID)]
rec = " ".join([tf.compat.as_str(rev_vocab[output]) for output in foutputs])
if rec not in recos:
recos.add(rec)
print(rec)
print("> ", "")
sys.stdout.flush()
sentence = sys.stdin.readline()
else:
sys.stdout.write("> ")
sys.stdout.flush()
sentence = sys.stdin.readline()
while sentence:
# Get token-ids for the input sentence.
token_ids = sentence_to_token_ids(tf.compat.as_bytes(sentence), vocab)
# Which bucket does it belong to?
bucket_id = min([b for b in xrange(len(_buckets))
if _buckets[b][0] > len(token_ids)])
# for loc in locs:
# Get a 1-element batch to feed the sentence to the model.
encoder_inputs, decoder_inputs, target_weights = model.get_batch(
{bucket_id: [(token_ids, [],)]}, bucket_id)
_, _, output_logits = model.step(sess, encoder_inputs, decoder_inputs,
target_weights, bucket_id, True,beam_search)
# This is a greedy decoder - outputs are just argmaxes of output_logits.
outputs = [int(np.argmax(logit, axis=1)) for logit in output_logits]
# If there is an EOS symbol in outputs, cut them at that point.
if EOS_ID in outputs:
# print outputs
outputs = outputs[:outputs.index(EOS_ID)]
print(" ".join([tf.compat.as_str(rev_vocab[output]) for output in outputs]))
print("> ", "")
sys.stdout.flush()
sentence = sys.stdin.readline()
def main(_):
if FLAGS.decode:
decode()
else:
train()
if __name__ == "__main__":
tf.app.run()