def main():
# load trained model
print 'Loading neural machine translator with attention:'
train_set = Corpus(TRAIN_SRC, TRAIN_TGT)
dev_set = Corpus(DEV_SRC, DEV_TGT)
m = load_model(train_set, dev_set, MODEL)
print 'Model loaded!'
# dev_set.target_sentences = dev_set.target_sentences[100:200]
# dev_set.source_sentences = dev_set.source_sentences[100:200]
# translate sentence
print '\nTranslating . . .\n'
sample_output = np.random.choice(len(dev_set.target_sentences), 5, False)
greedy, beam = gen_all(m, dev_set.source_sentences, BSIZE)
for sample in sample_output:
print 'Target: {}'.format(' '.join(dev_set.target_sentences[sample]))
print 'Greedy: {}'.format(' '.join(greedy[sample]))
print 'Beam search: {}'.format(' '.join(beam[sample]))
print '----------'
greedy_score = get_bleu_score(greedy, dev_set.target_sentences)
beam_score = get_bleu_score(beam, dev_set.target_sentences)
print 'Greedy bleu score: ', greedy_score
print 'Beam search bleu score: ', beam_score
def evaluateAll(encoder, decoder, split='dev', use_attention=True):
if use_attention:
model = 'attention'
else:
model = 'simple'
if split == 'train':
eval_pairs = train_pairs
elif split == 'dev':
eval_pairs = dev_pairs
translated_sentences = []
target_sentences = []
f_out = open('%s_%s_out.txt' % (split,model), 'w')
for i in range(len(eval_pairs)):
pair = eval_pairs[i]
f_out.write('> '+pair[0]+'\n')
f_out.write('= '+pair[1]+'\n')
target_sentences.append(pair[1])
#print('>', pair[0])
#print('=', pair[1])
output_words, attentions = evaluate(encoder, decoder, pair[0], use_attention=use_attention)
output_sentence = ' '.join(output_words)
f_out.write('< '+output_sentence+'\n\n')
translated_sentences.append(output_sentence)
#print('<', output_sentence)
#print('')
f_out.close()
bleu_score = get_bleu_score(translated_sentences, target_sentences)
print('BLEU score: ', bleu_score)
def main(train_src_file, train_tgt_file, dev_src_file, dev_tgt_file, model_file, num_epochs, embeddings_init = None, seed = 0):
print('reading train corpus ...')
train_set = Corpus(train_src_file, train_tgt_file)
# assert()
print('reading dev corpus ...')
dev_set = Corpus(dev_src_file, dev_tgt_file)
# test_set = Corpus(test_src_file)
print 'Initializing neural machine translator with attention:'
# src_vocab_size, tgt_vocab_size, tgt_idx2word, word_d, gru_d, gru_layers
encoder_decoder = nmt_dynet_attention(len(train_set.source_word2idx), len(train_set.target_word2idx), train_set.source_word2idx, train_set.source_idx2word, train_set.target_word2idx, train_set.target_idx2word, 50, 50, 2)
trainer = SimpleSGDTrainer(encoder_decoder.model)
sample_output = np.random.choice(len(dev_set.target_sentences), 5, False)
losses = []
best_bleu_score = 0
for epoch in range(num_epochs):
print 'Starting epoch', epoch
# shuffle the training data
combined = list(zip(train_set.source_sentences, train_set.target_sentences))
random.shuffle(combined)
train_set.source_sentences[:], train_set.target_sentences[:] = zip(*combined)
print 'Training . . .'
sentences_processed = 0
for src_sentence, tgt_sentence in zip(train_set.source_sentences, train_set.target_sentences):
loss = encoder_decoder.get_loss(src_sentence, tgt_sentence)
loss_value = loss.value()
loss.backward()
trainer.update()
sentences_processed += 1
if sentences_processed % 4000 == 0:
print 'sentences processed: ', sentences_processed
# Accumulate average losses over training to plot
val_loss = get_val_set_loss(encoder_decoder, dev_set)
print 'Validation loss this epoch', val_loss
losses.append(val_loss)
print 'Translating . . .'
translated_sentences = encoder_decoder.translate_all(dev_set.source_sentences)
print('translating {} source sentences...'.format(len(sample_output)))
for sample in sample_output:
print('Target: {}\nTranslation: {}\n'.format(' '.join(dev_set.target_sentences[sample]),
' '.join(translated_sentences[sample])))
bleu_score = get_bleu_score(translated_sentences, dev_set.target_sentences)
print 'bleu score: ', bleu_score
if bleu_score > best_bleu_score:
best_bleu_score = bleu_score
# save the model
encoder_decoder.save(model_file)
print 'best bleu score: ', best_bleu_score
def evaluate():
# Load and check whether UNK, SOS, EOS appears in the vocabulary.
source_vocab, source_vocab_length = load_and_check_vocab(SOURCE_VOCAB_FILE)
target_vocab, target_vocab_length = load_and_check_vocab(TARGET_VOCAB_FILE)
# Get UNK, SOS, EOS id.
source_unk_id = source_vocab.index(UNK)
source_eos_id = source_vocab.index(EOS)
target_unk_id = target_vocab.index(UNK)
target_sos_id = target_vocab.index(SOS)
target_eos_id = target_vocab.index(EOS)
# Compute the length of effective training data.
source_bool_mask = check_dataset(SOURCE_TRAINING_FILE)
target_bool_mask = check_dataset(TARGET_TRAINING_FILE)
bool_mask = [x and y for x, y in zip(source_bool_mask, target_bool_mask)]
dataset_length = sum(bool_mask)
# Create source and target vocabulary tables.
source_vocab_table = tf.contrib.lookup.index_table_from_file(
DATA_DIR + SOURCE_VOCAB_FILE, default_value=source_unk_id)
target_vocab_table = tf.contrib.lookup.index_table_from_file(
DATA_DIR + TARGET_VOCAB_FILE, default_value=target_unk_id)
# Get dataset iterator tuple(initializer, source_input, target_input, target_output, source_length, target_length).
iterator = get_dataset_iterator(dataset_length, SOURCE_TEST_FILE_1,
TARGET_TEST_FILE_1, source_vocab_table,
target_vocab_table, source_eos_id,
target_sos_id, target_eos_id)
# Load model.
model = Seq2Seq_Model()
model.build_evaluation_model(source_vocab_length, target_vocab_length,
target_sos_id, target_eos_id,
iterator.source_input, iterator.target_output,
iterator.source_length)
iterator_initializer = iterator.initializer
table_initializer = tf.tables_initializer()
with tf.Session() as sess:
# Initialization.
sess.run([iterator_initializer, table_initializer])
# Load parameters.
saver_embedding = tf.train.Saver(model.embedding)
saver_network = tf.train.Saver(model.network_params)
saver_embedding.restore(sess, SAVE_DIR + "embedding")
saver_network.restore(sess, SAVE_DIR + "network_params")
list_translation = []
list_reference = []
total_step = int(np.ceil(dataset_length / BUCKET_BATCH_SIZE))
for step in range(total_step):
translation_ids, reference_ids = sess.run(
[model.translation_ids, model.reference_ids])
# Shorten the sequence by removing the paddings.
translation_ids = shorten_sequence(translation_ids,
target_eos_id,
dtype=np.int32)
reference_ids = shorten_sequence(reference_ids,
target_eos_id,
dtype=np.int32)
list_translation += translation_ids
list_reference += [[reference] for reference in reference_ids]
print("Current progress: ",
step + 1,
"/",
total_step,
sep="",
end="\r",
flush=True)
# Estimate the BLEU score.
score = get_bleu_score(list_translation, list_reference)
print("")
print("BLEU score = ", format(score, ".8f"), sep="")
