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translate.py
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translate.py
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#!/usr/bin/env python
# -*- coding: utf-8 -*-
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import math
import os
import random
import sys
import time
import tensorflow.python.platform
import numpy as np
from six.moves import xrange
import tensorflow as tf
import data_utils
from tensorflow.models.rnn.translate import seq2seq_model
from tensorflow.python.platform import gfile
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", 4,
"Batch size to use during training.")
tf.app.flags.DEFINE_integer("size", 256, "Size of each model layer.")
tf.app.flags.DEFINE_integer("num_layers", 2, "Number of layers in the model.")
tf.app.flags.DEFINE_integer("in_vocab_size", 500, "input vocabulary size.")
tf.app.flags.DEFINE_integer("out_vocab_size", 500, "output vocabulary size.")
tf.app.flags.DEFINE_string("data_dir", "datas", "Data directory")
tf.app.flags.DEFINE_string("train_dir", "datas", "Training 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", 100,
"How many training steps to do per checkpoint.")
tf.app.flags.DEFINE_boolean("decode", 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
_buckets = [(5, 10), (10, 15), (20, 25), (40, 50)]
def read_data(source_path, target_path, max_size=None):
data_set = [[] for _ in _buckets]
source_file = open(source_path,"r")
target_file = open(target_path,"r")
source, target = source_file.readline(), target_file.readline()
counter = 0
while source and target and (not max_size or counter < max_size):
counter += 1
if counter % 50 == 0:
print(" reading data line %d" % counter)
sys.stdout.flush()
source_ids = [int(x) for x in source.split()]
target_ids = [int(x) for x in target.split()]
target_ids.append(data_utils.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
source, target = source_file.readline(), target_file.readline()
return data_set
def create_model(session, forward_only):
model = seq2seq_model.Seq2SeqModel(
FLAGS.in_vocab_size, FLAGS.out_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)
ckpt = tf.train.get_checkpoint_state(FLAGS.train_dir)
if ckpt and 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.initialize_all_variables())
return model
def train():
print("Preparing data in %s" % FLAGS.data_dir)
in_train, out_train, in_dev, out_dev, _, _ = data_utils.prepare_wmt_data(
FLAGS.data_dir, FLAGS.in_vocab_size, FLAGS.out_vocab_size)
with tf.Session() as sess:
print("Creating %d layers of %d units." % (FLAGS.num_layers, FLAGS.size))
model = create_model(sess, False)
print ("Reading development and training data (limit: %d)."
% FLAGS.max_train_data_size)
dev_set = read_data(in_dev, out_dev)
train_set = read_data(in_train, out_train, 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))
train_buckets_scale = [sum(train_bucket_sizes[:i + 1]) / train_total_size
for i in xrange(len(train_bucket_sizes))]
step_time, loss = 0.0, 0.0
current_step = 0
previous_losses = []
while True:
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])
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)
step_time += (time.time() - start_time) / FLAGS.steps_per_checkpoint
loss += step_loss / FLAGS.steps_per_checkpoint
current_step += 1
if current_step % FLAGS.steps_per_checkpoint == 0:
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))
if len(previous_losses) > 2 and loss > max(previous_losses[-3:]):
sess.run(model.learning_rate_decay_op)
previous_losses.append(loss)
checkpoint_path = os.path.join(FLAGS.train_dir, "translate.ckpt")
model.saver.save(sess, checkpoint_path, global_step=model.global_step)
step_time, loss = 0.0, 0.0
for bucket_id in xrange(len(_buckets)):
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)
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:
print ("Hello!!")
model = create_model(sess, True)
model.batch_size = 1
in_vocab_path = os.path.join(FLAGS.data_dir,
"vocab_in.txt")
out_vocab_path = os.path.join(FLAGS.data_dir,
"vocab_out.txt" )
in_vocab, _ = data_utils.initialize_vocabulary(in_vocab_path)
_, rev_out_vocab = data_utils.initialize_vocabulary(out_vocab_path)
sys.stdout.write("> ")
sys.stdout.flush()
sentence = sys.stdin.readline()
while sentence:
token_ids = data_utils.sentence_to_token_ids(sentence, in_vocab)
bucket_id = min([b for b in xrange(len(_buckets))
if _buckets[b][0] > len(token_ids)])
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)
outputs = [int(np.argmax(logit, axis=1)) for logit in output_logits]
if data_utils.EOS_ID in outputs:
outputs = outputs[:outputs.index(data_utils.EOS_ID)]
print(" ".join([rev_out_vocab[output] for output in outputs]))
print("> ", end="")
sys.stdout.flush()
sentence = sys.stdin.readline()
def self_test():
with tf.Session() as sess:
print("Self-test for neural translation model.")
model = seq2seq_model.Seq2SeqModel(10, 10, [(3, 3), (6, 6)], 32, 2,
5.0, 32, 0.3, 0.99, num_samples=8)
sess.run(tf.initialize_all_variables())
data_set = ([([1, 1], [2, 2]), ([3, 3], [4]), ([5], [6])],
[([1, 1, 1, 1, 1], [2, 2, 2, 2, 2]), ([3, 3, 3], [5, 6])])
for _ in xrange(5):
bucket_id = random.choice([0, 1])
encoder_inputs, decoder_inputs, target_weights = model.get_batch(
data_set, bucket_id)
model.step(sess, encoder_inputs, decoder_inputs, target_weights,
bucket_id, False)
def main(_):
if FLAGS.self_test:
self_test()
elif FLAGS.decode:
decode()
else:
train()
if __name__ == "__main__":
tf.app.run()