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# encoding: utf-8
# Copyright 2016 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 cPickle
import itertools
import os
import random
import numpy as np
import tensorflow as tf
from tensorflow.contrib import learn
# Get training data
# This dataset can be downloaded from http://www.statmt.org/europarl/v6/fr-en.tgz
ENGLISH_CORPUS = "europarl-v6.fr-en.en"
FRENCH_CORPUS = "europarl-v6.fr-en.fr"
def read_iterator(filename, reporting=True):
f = open(filename)
line_count = 0
for line in f:
line_count += 1
if reporting and line_count % 100000 == 0:
print("%d lines read from %s" % (line_count, filename))
yield line.strip()
def repeated_read_iterator(filename):
while True:
f = open(filename)
for line in f:
yield line.strip()
def split_train_test(data, partition=0.2, random_seed=42):
rnd = np.random.RandomState(random_seed)
for item in data:
if rnd.uniform() > partition:
yield (0, item)
else:
yield (1, item)
def save_partitions(data, filenames):
files = [open(filename, 'w') for filename in filenames]
for partition, item in data:
files[partition].write(item + '\n')
def loop_iterator(data):
while True:
for item in data:
yield item
if not (os.path.exists('train.data') and os.path.exists('test.data')):
english_data = read_iterator(ENGLISH_CORPUS)
french_data = read_iterator(FRENCH_CORPUS)
parallel_data = ('%s;;;%s' % (eng, fr) for eng, fr in itertools.izip(english_data, french_data))
save_partitions(split_train_test(parallel_data), ['train.data', 'test.data'])
def Xy(data):
def split_lines(data):
for item in data:
yield item.split(';;;')
X, y = itertools.tee(split_lines(data))
return (item[0] for item in X), (item[1] for item in y)
X_train, y_train = Xy(repeated_read_iterator('train.data'))
X_test, y_test = Xy(read_iterator('test.data'))
# Preprocessing
MAX_DOCUMENT_LENGTH = 10
if not (os.path.exists('en.vocab') and os.path.exists('fr.vocab')):
X_vocab_processor = learn.preprocessing.VocabularyProcessor(MAX_DOCUMENT_LENGTH,
min_frequency=5)
y_vocab_processor = learn.preprocessing.VocabularyProcessor(MAX_DOCUMENT_LENGTH,
min_frequency=5)
Xtrainff, ytrainff = Xy(read_iterator('train.data'))
print('Fitting dictionary for English...')
X_vocab_processor.fit(Xtrainff)
print('Fitting dictionary for French...')
y_vocab_processor.fit(ytrainff)
open('en.vocab', 'w').write(cPickle.dumps(X_vocab_processor))
open('fr.vocab', 'w').write(cPickle.dumps(y_vocab_processor))
else:
X_vocab_processor = cPickle.loads(open('en.vocab').read())
y_vocab_processor = cPickle.loads(open('fr.vocab').read())
print('Transforming...')
X_train = X_vocab_processor.transform(X_train)
y_train = y_vocab_processor.transform(y_train)
X_test = X_vocab_processor.transform(X_test)
# TODO: Expand this to use the whole test set.
X_test = np.array([X_test.next() for _ in range(1000)])
y_test = [y_test.next() for _ in range(1000)]
n_en_words = len(X_vocab_processor.vocabulary_)
n_fr_words = len(y_vocab_processor.vocabulary_)
print('Total words, en: %d, fr: %d' % (n_en_words, n_fr_words))
# Translation model
HIDDEN_SIZE = 20
EMBEDDING_SIZE = 20
def translate_model(X, y):
word_vectors = learn.ops.categorical_variable(X, n_classes=n_en_words,
embedding_size=EMBEDDING_SIZE, name='words')
in_X, in_y, out_y = learn.ops.seq2seq_inputs(
word_vectors, y, MAX_DOCUMENT_LENGTH, MAX_DOCUMENT_LENGTH)
encoder_cell = tf.nn.rnn_cell.GRUCell(HIDDEN_SIZE)
decoder_cell = tf.nn.rnn_cell.OutputProjectionWrapper(
tf.nn.rnn_cell.GRUCell(HIDDEN_SIZE), n_fr_words)
decoding, _, sampling_decoding, _ = learn.ops.rnn_seq2seq(in_X, in_y,
encoder_cell, decoder_cell=decoder_cell)
return learn.ops.sequence_classifier(decoding, out_y, sampling_decoding)
PATH = '/tmp/tf_examples/ntm_words/'
if os.path.exists(os.path.join(PATH, 'graph.pbtxt')):
translator = learn.TensorFlowEstimator.restore(PATH)
else:
translator = learn.TensorFlowEstimator(model_fn=translate_model,
n_classes=n_fr_words,
optimizer='Adam', learning_rate=0.01, batch_size=128,
continue_training=True, steps=100)
while True:
translator.fit(X_train, y_train, logdir=PATH)
translator.save(PATH)
xpred, ygold = [], []
for _ in range(10):
idx = random.randint(0, len(X_test) - 1)
xpred.append(X_test[idx])
ygold.append(y_test[idx])
xpred = np.array(xpred)
predictions = translator.predict(xpred, axis=2)
xpred_inp = X_vocab_processor.reverse(xpred)
text_outputs = y_vocab_processor.reverse(predictions)
for inp_data, input_text, pred, output_text, gold in zip(xpred, xpred_inp,
predictions, text_outputs, ygold):
print('English: %s. French (pred): %s, French (gold): %s' %
(input_text, output_text, gold))
print(inp_data, pred)
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