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execute.py
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# Copyright 2015 The TensorFlow Authors. All Rights Reserved.
#
# 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
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
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 numpy as np
from six.moves import xrange # pylint: disable=redefined-builtin
import tensorflow as tf
import data_utils
import seq2seq_model
# python2 and python3 support
try:
reload
except NameError:
# py3k has unicode by default
pass
else:
reload(sys).setdefaultencoding('utf-8')
try:
from ConfigParser import SafeConfigParser
input = raw_input
except:
from configparser import SafeConfigParser # In Python 3, ConfigParser has been renamed to configparser for PEP 8 compliance.
gConfig = {}
def get_config(config_file='seq2seq.ini'):
parser = SafeConfigParser()
parser.read(config_file)
# get the ints, floats and strings
_conf_ints = [(key, int(value)) for key, value in parser.items('ints')]
_conf_floats = [(key, float(value)) for key, value in parser.items('floats')]
_conf_strings = [(key, str(value)) for key, value in parser.items('strings')]
return dict(_conf_ints + _conf_floats + _conf_strings)
# 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_data(source_path, target_path, max_size=None):
"""Read data from source and target files and put into buckets.
Args:
source_path: path to the files with token-ids for the source language.
target_path: path to the file with token-ids for the target language;
it must be aligned with the source file: n-th line contains the desired
output for n-th line from the source_path.
max_size: maximum number of lines to read, all other will be ignored;
if 0 or None, data files will be read completely (no limit).
Returns:
data_set: a list of length len(_buckets); data_set[n] contains a list of
(source, target) pairs read from the provided data files that fit
into the n-th bucket, i.e., such that len(source) < _buckets[n][0] and
len(target) < _buckets[n][1]; source and target are lists of token-ids.
"""
data_set = [[] for _ in _buckets]
with tf.gfile.GFile(source_path, mode="r") as source_file:
with tf.gfile.GFile(target_path, mode="r") as target_file:
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 % 100000 == 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):
"""Create model and initialize or load parameters"""
model = seq2seq_model.Seq2SeqModel(gConfig['enc_vocab_size'], gConfig['dec_vocab_size'], _buckets,
gConfig['layer_size'], gConfig['num_layers'], gConfig['max_gradient_norm'],
gConfig['batch_size'], gConfig['learning_rate'],
gConfig['learning_rate_decay_factor'], forward_only=forward_only)
if 'pretrained_model' in gConfig:
model.saver.restore(session, gConfig['pretrained_model'])
return model
ckpt = tf.train.get_checkpoint_state(gConfig['working_directory'])
# the checkpoint filename has changed in recent versions of tensorflow
checkpoint_suffix = ""
if tf.__version__ > "0.12":
checkpoint_suffix = ".index"
if ckpt and tf.gfile.Exists(ckpt.model_checkpoint_path + checkpoint_suffix):
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():
# prepare dataset
print("Preparing data in %s" % gConfig['working_directory'])
enc_train, dec_train, enc_dev, dec_dev, _, _ = data_utils.prepare_custom_data(gConfig['working_directory'],
gConfig['train_enc'],
gConfig['train_dec'],
gConfig['test_enc'],
gConfig['test_dec'],
gConfig['enc_vocab_size'],
gConfig['dec_vocab_size'])
# Only allocate 2/3 of the gpu memory to allow for running gpu-based predictions while training:
gpu_options = tf.GPUOptions(per_process_gpu_memory_fraction=0.666)
config = tf.ConfigProto(gpu_options=gpu_options)
config.gpu_options.allocator_type = 'BFC'
with tf.Session(config=config) as sess:
# Create model.
print("Creating %d layers of %d units." % (gConfig['num_layers'], gConfig['layer_size']))
model = create_model(sess, False)
# Read data into buckets and compute their sizes.
print("Reading development and training data (limit: %d)."
% gConfig['max_train_data_size'])
dev_set = read_data(enc_dev, dec_dev)
train_set = read_data(enc_train, dec_train, gConfig['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.
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)
step_time += (time.time() - start_time) / gConfig['steps_per_checkpoint']
loss += step_loss / gConfig['steps_per_checkpoint']
current_step += 1
# Once in a while, we save checkpoint, print statistics, and run evals.
if current_step % gConfig['steps_per_checkpoint'] == 0:
# Print statistics for the previous epoch.
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(gConfig['working_directory'], "seq2seq.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)
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():
# Only allocate part of the gpu memory when predicting.
gpu_options = tf.GPUOptions(per_process_gpu_memory_fraction=0.2)
config = tf.ConfigProto(gpu_options=gpu_options)
with tf.Session(config=config) as sess:
# Create model and load parameters.
model = create_model(sess, True)
model.batch_size = 1 # We decode one sentence at a time.
# Load vocabularies.
enc_vocab_path = os.path.join(gConfig['working_directory'], "vocab%d.enc" % gConfig['enc_vocab_size'])
dec_vocab_path = os.path.join(gConfig['working_directory'], "vocab%d.dec" % gConfig['dec_vocab_size'])
enc_vocab, _ = data_utils.initialize_vocabulary(enc_vocab_path)
_, rev_dec_vocab = data_utils.initialize_vocabulary(dec_vocab_path)
# Decode from standard input.
sentence = input('> ').strip()
while sentence:
# Get token-ids for the input sentence.
token_ids = data_utils.sentence_to_token_ids(sentence, enc_vocab)
print(token_ids)
if not token_ids:
raise Exception('no ids!')
# Which bucket does it belong to?
bucket_id = None
sorted_buckets = sorted(_buckets) # [(start, end), ...] Will sort by start value, small to large
for index, (start, end) in enumerate(sorted_buckets):
if start > len(token_ids):
bucket_id = index
break
# 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.
_, _, output_logits = model.step(sess,
encoder_inputs,
decoder_inputs,
target_weights,
bucket_id,
forward_only=True)
# This is a greedy decoder - outputs are just argmaxes of output_logits.
outputs = []
for logit in output_logits:
argmax_result = int(np.argmax(logit)) #, axis=1))
outputs.append(argmax_result)
# If there is an EOS symbol in outputs, cut them at that point.
if data_utils.EOS_ID in outputs:
outputs = outputs[:outputs.index(data_utils.EOS_ID)]
# Print out corresponding to outputs.
print(" ".join([rev_dec_vocab[output] for output in outputs]))
sentence = input('> ').strip()
def self_test():
"""Test the translation model."""
with tf.Session() as sess:
print("Self-test for neural translation model.")
# Create model with vocabularies of 10, 2 small buckets, 2 layers of 32.
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())
# Fake data set for both the (3, 3) and (6, 6) bucket.
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): # Train the fake model for 5 steps.
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 init_session(sess, conf='seq2seq.ini'):
global gConfig
gConfig = get_config(conf)
# Create model and load parameters.
model = create_model(sess, True)
model.batch_size = 1 # We decode one sentence at a time.
# Load vocabularies.
enc_vocab_path = os.path.join(gConfig['working_directory'], "vocab%d.enc" % gConfig['enc_vocab_size'])
dec_vocab_path = os.path.join(gConfig['working_directory'], "vocab%d.dec" % gConfig['dec_vocab_size'])
enc_vocab, _ = data_utils.initialize_vocabulary(enc_vocab_path)
_, rev_dec_vocab = data_utils.initialize_vocabulary(dec_vocab_path)
return sess, model, enc_vocab, rev_dec_vocab
def decode_line(sess, model, enc_vocab, rev_dec_vocab, sentence):
# Get token-ids for the input sentence.
token_ids = data_utils.sentence_to_token_ids(tf.compat.as_bytes(sentence), enc_vocab)
# 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.
_, _, output_logits = model.step(sess, encoder_inputs, decoder_inputs, target_weights, bucket_id, True)
# 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 data_utils.EOS_ID in outputs:
outputs = outputs[:outputs.index(data_utils.EOS_ID)]
return " ".join([tf.compat.as_str(rev_dec_vocab[output]) for output in outputs])
if __name__ == '__main__':
import argparse
# get configuration from seq2seq.ini
gConfig = get_config()
parser = argparse.ArgumentParser()
ACTION_CHOICES = ('train', 'test')
parser.add_argument('-a', '--action',
choices=ACTION_CHOICES,
default=gConfig['mode'],
help='Action to perform {}'.format(ACTION_CHOICES))
args = parser.parse_args()
print('\n>> Mode : %s\n' % (args.action))
if args.action == 'train':
# start training
train()
elif args.action == 'test':
# interactive decode
decode()