def main(_):
print('Configurations:')
print(FLAGS)
log_dir = FLAGS.model_dir
if not os.path.exists(log_dir):
os.makedirs(log_dir)
path_prefix = log_dir + "/NP2P.{}".format(FLAGS.suffix)
log_file_path = path_prefix + ".log"
print('Log file path: {}'.format(log_file_path))
log_file = open(log_file_path, 'wt')
log_file.write("{}\n".format(FLAGS))
log_file.flush()
# save configuration
namespace_utils.save_namespace(FLAGS, path_prefix + ".config.json")
print('Loading training set.')
trainset, train_ans_len = NP2P_data_stream.read_all_GenerationDatasets(
FLAGS.train_path, isLower=FLAGS.isLower)
print('Number of training samples: {}'.format(len(trainset)))
print('Loading dev set.')
devset, dev_ans_len = NP2P_data_stream.read_all_GenerationDatasets(
FLAGS.test_path, isLower=FLAGS.isLower)
print('Number of dev samples: {}'.format(len(devset)))
if FLAGS.finetune_path != "":
print('Loading finetune set.')
ftset, ft_ans_len = NP2P_data_stream.read_all_GenerationDatasets(
FLAGS.ft_path, isLower=FLAGS.isLower)
print('Number of finetune samples: {}'.format(len(ftset)))
else:
ftset, ft_ans_len = (None, 0)
max_actual_len = max(train_ans_len, ft_ans_len, dev_ans_len)
print('Max answer length: {}, truncated to {}'.format(
max_actual_len, FLAGS.max_answer_len))
enc_word_vocab = None
dec_word_vocab = None
char_vocab = None
has_pretrained_model = False
best_path = path_prefix + ".best.model"
if os.path.exists(best_path + ".index"):
has_pretrained_model = True
print('!!Existing pretrained model. Loading vocabs.')
if FLAGS.with_word:
enc_word_vocab = Vocab(FLAGS.enc_word_vec_path, fileformat='txt2')
dec_word_vocab = Vocab(FLAGS.dec_word_vec_path, fileformat='txt2')
print('Encoder word vocab: {}'.format(
enc_word_vocab.word_vecs.shape))
print('Decoder word vocab: {}'.format(
dec_word_vocab.word_vecs.shape))
if FLAGS.with_char:
char_vocab = Vocab(path_prefix + ".char_vocab", fileformat='txt2')
print('char_vocab: {}'.format(char_vocab.word_vecs.shape))
else:
print('Collecting vocabs.')
(allWords, allChars) = NP2P_data_stream.collect_vocabs(trainset)
print('Number of words: {}'.format(len(allWords)))
print('Number of allChars: {}'.format(len(allChars)))
if FLAGS.with_word:
enc_word_vocab = Vocab(FLAGS.enc_word_vec_path, fileformat='txt2')
dec_word_vocab = Vocab(FLAGS.dec_word_vec_path, fileformat='txt2')
if FLAGS.with_char:
char_vocab = Vocab(voc=allChars,
dim=FLAGS.char_dim,
fileformat='build')
char_vocab.dump_to_txt2(path_prefix + ".char_vocab")
print('Encoder word vocab size {}'.format(enc_word_vocab.vocab_size))
print('Decoder word vocab size {}'.format(dec_word_vocab.vocab_size))
sys.stdout.flush()
print('Build DataStream ... ')
trainDataStream = NP2P_data_stream.DataStream(trainset,
enc_word_vocab,
dec_word_vocab,
char_vocab,
options=FLAGS,
isShuffle=True,
isLoop=True,
isSort=True)
devDataStream = NP2P_data_stream.DataStream(devset,
enc_word_vocab,
dec_word_vocab,
char_vocab,
options=FLAGS,
isShuffle=False,
isLoop=False,
isSort=True)
print('Number of instances in trainDataStream: {}'.format(
trainDataStream.get_num_instance()))
print('Number of instances in devDataStream: {}'.format(
devDataStream.get_num_instance()))
print('Number of batches in trainDataStream: {}'.format(
trainDataStream.get_num_batch()))
print('Number of batches in devDataStream: {}'.format(
devDataStream.get_num_batch()))
if ftset != None:
ftDataStream = NP2P_data_stream.DataStream(ftset,
enc_word_vocab,
dec_word_vocab,
char_vocab,
options=FLAGS,
isShuffle=True,
isLoop=True,
isSort=True)
print('Number of instances in ftDataStream: {}'.format(
ftDataStream.get_num_instance()))
print('Number of batches in ftDataStream: {}'.format(
ftDataStream.get_num_batch()))
sys.stdout.flush()
init_scale = 0.01
# initialize the best bleu and accu scores for current training session
best_accu = FLAGS.best_accu if FLAGS.__dict__.has_key('best_accu') else 0.0
best_bleu = FLAGS.best_bleu if FLAGS.__dict__.has_key('best_bleu') else 0.0
if best_accu > 0.0:
print('With initial dev accuracy {}'.format(best_accu))
if best_bleu > 0.0:
print('With initial dev BLEU score {}'.format(best_bleu))
with tf.Graph().as_default():
initializer = tf.random_uniform_initializer(-init_scale, init_scale)
with tf.name_scope("Train"):
with tf.variable_scope("Model",
reuse=None,
initializer=initializer):
train_graph = ModelGraph(enc_word_vocab=enc_word_vocab,
dec_word_vocab=dec_word_vocab,
char_vocab=char_vocab,
POS_vocab=None,
NER_vocab=None,
options=FLAGS,
mode=FLAGS.mode)
assert FLAGS.mode in (
'ce_train',
'rl_train',
)
valid_mode = 'evaluate' if FLAGS.mode == 'ce_train' else 'evaluate_bleu'
with tf.name_scope("Valid"):
with tf.variable_scope("Model",
reuse=True,
initializer=initializer):
valid_graph = ModelGraph(enc_word_vocab=enc_word_vocab,
dec_word_vocab=dec_word_vocab,
char_vocab=char_vocab,
POS_vocab=None,
NER_vocab=None,
options=FLAGS,
mode=valid_mode)
initializer = tf.global_variables_initializer()
vars_ = {}
for var in tf.all_variables():
if FLAGS.fix_word_vec and "word_embedding" in var.name: continue
if not var.name.startswith("Model"): continue
print(var)
vars_[var.name.split(":")[0]] = var
saver = tf.train.Saver(vars_)
sess = tf.Session()
sess.run(initializer)
if has_pretrained_model:
print("Restoring model from " + best_path)
saver.restore(sess, best_path)
print("DONE!")
if FLAGS.mode == 'rl_train' and abs(best_bleu) < 0.00001:
print("Getting BLEU score for the model")
best_bleu = evaluate(sess,
valid_graph,
devDataStream,
options=FLAGS)['dev_bleu']
FLAGS.best_bleu = best_bleu
namespace_utils.save_namespace(FLAGS,
path_prefix + ".config.json")
print('BLEU = %.4f' % best_bleu)
log_file.write('BLEU = %.4f\n' % best_bleu)
if FLAGS.mode == 'ce_train' and abs(best_accu) < 0.00001:
print("Getting ACCU score for the model")
best_accu = evaluate(sess,
valid_graph,
devDataStream,
options=FLAGS)['dev_accu']
FLAGS.best_accu = best_accu
namespace_utils.save_namespace(FLAGS,
path_prefix + ".config.json")
print('ACCU = %.4f' % best_accu)
log_file.write('ACCU = %.4f\n' % best_accu)
print('Start the training loop.')
train_size = trainDataStream.get_num_batch()
max_steps = train_size * FLAGS.max_epochs
total_loss = 0.0
start_time = time.time()
for step in xrange(max_steps):
cur_batch = trainDataStream.nextBatch()
if FLAGS.mode == 'rl_train':
loss_value = train_graph.run_rl_training_2(
sess, cur_batch, FLAGS)
elif FLAGS.mode == 'ce_train':
loss_value = train_graph.run_ce_training(
sess, cur_batch, FLAGS)
total_loss += loss_value
if step % 100 == 0:
print('{} '.format(step), end="")
sys.stdout.flush()
# Save a checkpoint and evaluate the model periodically.
if (step + 1) % trainDataStream.get_num_batch() == 0 or (step + 1) == max_steps or \
(trainDataStream.get_num_batch() > 10000 and (step + 1) % 2000 == 0):
print()
duration = time.time() - start_time
print('Step %d: loss = %.2f (%.3f sec)' %
(step, total_loss, duration))
log_file.write('Step %d: loss = %.2f (%.3f sec)\n' %
(step, total_loss, duration))
log_file.flush()
sys.stdout.flush()
total_loss = 0.0
if ftset != None:
best_accu, best_bleu = fine_tune(sess, saver, FLAGS,
log_file, ftDataStream,
devDataStream,
train_graph, valid_graph,
path_prefix, best_accu,
best_bleu)
else:
best_accu, best_bleu = validate_and_save(
sess, saver, FLAGS, log_file, devDataStream,
valid_graph, path_prefix, best_accu, best_bleu)
start_time = time.time()
log_file.close()
def main(_):
print('Configurations:')
print(FLAGS)
log_dir = FLAGS.model_dir
if not os.path.exists(log_dir):
os.makedirs(log_dir)
path_prefix = log_dir + "/NP2P.{}".format(FLAGS.suffix)
log_file_path = path_prefix + ".log"
print('Log file path: {}'.format(log_file_path))
log_file = open(log_file_path, 'wt')
log_file.write("{}\n".format(FLAGS))
log_file.flush()
# save configuration
namespace_utils.save_namespace(FLAGS, path_prefix + ".config.json")
print('Loading train set.')
if FLAGS.infile_format == 'fof':
trainset, train_ans_len = NP2P_data_stream.read_generation_datasets_from_fof(
FLAGS.train_path, isLower=FLAGS.isLower)
elif FLAGS.infile_format == 'plain':
trainset, train_ans_len = NP2P_data_stream.read_all_GenerationDatasets(
FLAGS.train_path, isLower=FLAGS.isLower)
else:
trainset, train_ans_len = NP2P_data_stream.read_all_GQA_questions(
FLAGS.train_path, isLower=FLAGS.isLower, switch=FLAGS.switch_qa)
print('Number of training samples: {}'.format(len(trainset)))
print('Loading test set.')
if FLAGS.infile_format == 'fof':
testset, test_ans_len = NP2P_data_stream.read_generation_datasets_from_fof(
FLAGS.test_path, isLower=FLAGS.isLower)
elif FLAGS.infile_format == 'plain':
testset, test_ans_len = NP2P_data_stream.read_all_GenerationDatasets(
FLAGS.test_path, isLower=FLAGS.isLower)
else:
testset, test_ans_len = NP2P_data_stream.read_all_GQA_questions(
FLAGS.test_path, isLower=FLAGS.isLower, switch=FLAGS.switch_qa)
print('Number of test samples: {}'.format(len(testset)))
max_actual_len = max(train_ans_len, test_ans_len)
print('Max answer length: {}, truncated to {}'.format(
max_actual_len, FLAGS.max_answer_len))
word_vocab = None
POS_vocab = None
NER_vocab = None
char_vocab = None
has_pretrained_model = False
best_path = path_prefix + ".best.model"
if os.path.exists(best_path + ".index"):
has_pretrained_model = True
print('!!Existing pretrained model. Loading vocabs.')
if FLAGS.with_word:
word_vocab = Vocab(FLAGS.word_vec_path, fileformat='txt2')
print('word_vocab: {}'.format(word_vocab.word_vecs.shape))
if FLAGS.with_char:
char_vocab = Vocab(path_prefix + ".char_vocab", fileformat='txt2')
print('char_vocab: {}'.format(char_vocab.word_vecs.shape))
if FLAGS.with_POS:
POS_vocab = Vocab(path_prefix + ".POS_vocab", fileformat='txt2')
print('POS_vocab: {}'.format(POS_vocab.word_vecs.shape))
if FLAGS.with_NER:
NER_vocab = Vocab(path_prefix + ".NER_vocab", fileformat='txt2')
print('NER_vocab: {}'.format(NER_vocab.word_vecs.shape))
else:
print('Collecting vocabs.')
(allWords, allChars, allPOSs,
allNERs) = NP2P_data_stream.collect_vocabs(trainset)
print('Number of words: {}'.format(len(allWords)))
print('Number of allChars: {}'.format(len(allChars)))
print('Number of allPOSs: {}'.format(len(allPOSs)))
print('Number of allNERs: {}'.format(len(allNERs)))
if FLAGS.with_word:
word_vocab = Vocab(FLAGS.word_vec_path, fileformat='txt2')
if FLAGS.with_char:
char_vocab = Vocab(voc=allChars,
dim=FLAGS.char_dim,
fileformat='build')
char_vocab.dump_to_txt2(path_prefix + ".char_vocab")
if FLAGS.with_POS:
POS_vocab = Vocab(voc=allPOSs,
dim=FLAGS.POS_dim,
fileformat='build')
POS_vocab.dump_to_txt2(path_prefix + ".POS_vocab")
if FLAGS.with_NER:
NER_vocab = Vocab(voc=allNERs,
dim=FLAGS.NER_dim,
fileformat='build')
NER_vocab.dump_to_txt2(path_prefix + ".NER_vocab")
print('word vocab size {}'.format(word_vocab.vocab_size))
sys.stdout.flush()
print('Build DataStream ... ')
trainDataStream = NP2P_data_stream.QADataStream(trainset,
word_vocab,
char_vocab,
POS_vocab,
NER_vocab,
options=FLAGS,
isShuffle=True,
isLoop=True,
isSort=True)
devDataStream = NP2P_data_stream.QADataStream(testset,
word_vocab,
char_vocab,
POS_vocab,
NER_vocab,
options=FLAGS,
isShuffle=False,
isLoop=False,
isSort=True)
print('Number of instances in trainDataStream: {}'.format(
trainDataStream.get_num_instance()))
print('Number of instances in devDataStream: {}'.format(
devDataStream.get_num_instance()))
print('Number of batches in trainDataStream: {}'.format(
trainDataStream.get_num_batch()))
print('Number of batches in devDataStream: {}'.format(
devDataStream.get_num_batch()))
sys.stdout.flush()
init_scale = 0.01
# initialize the best bleu and accu scores for current training session
best_accu = FLAGS.best_accu if FLAGS.__dict__.has_key('best_accu') else 0.0
best_bleu = FLAGS.best_bleu if FLAGS.__dict__.has_key('best_bleu') else 0.0
if best_accu > 0.0:
print('With initial dev accuracy {}'.format(best_accu))
if best_bleu > 0.0:
print('With initial dev BLEU score {}'.format(best_bleu))
with tf.Graph().as_default():
initializer = tf.random_uniform_initializer(-init_scale, init_scale)
with tf.name_scope("Train"):
with tf.variable_scope("Model",
reuse=None,
initializer=initializer):
train_graph = ModelGraph(word_vocab=word_vocab,
char_vocab=char_vocab,
POS_vocab=POS_vocab,
NER_vocab=NER_vocab,
options=FLAGS,
mode=FLAGS.mode)
assert FLAGS.mode in (
'ce_train',
'rl_train',
)
valid_mode = 'evaluate' if FLAGS.mode == 'ce_train' else 'evaluate_bleu'
with tf.name_scope("Valid"):
with tf.variable_scope("Model",
reuse=True,
initializer=initializer):
valid_graph = ModelGraph(word_vocab=word_vocab,
char_vocab=char_vocab,
POS_vocab=POS_vocab,
NER_vocab=NER_vocab,
options=FLAGS,
mode=valid_mode)
initializer = tf.global_variables_initializer()
vars_ = {}
for var in tf.all_variables():
if "word_embedding" in var.name: continue
if not var.name.startswith("Model"): continue
vars_[var.name.split(":")[0]] = var
saver = tf.train.Saver(vars_)
sess = tf.Session()
sess.run(initializer)
if has_pretrained_model:
print("Restoring model from " + best_path)
saver.restore(sess, best_path)
print("DONE!")
if FLAGS.mode == 'rl_train' and abs(best_bleu) < 0.00001:
print("Getting BLEU score for the model")
best_bleu = evaluate(sess,
valid_graph,
devDataStream,
options=FLAGS)['dev_bleu']
FLAGS.best_bleu = best_bleu
namespace_utils.save_namespace(FLAGS,
path_prefix + ".config.json")
print('BLEU = %.4f' % best_bleu)
log_file.write('BLEU = %.4f\n' % best_bleu)
if FLAGS.mode == 'ce_train' and abs(best_accu) < 0.00001:
print("Getting ACCU score for the model")
best_accu = evaluate(sess,
valid_graph,
devDataStream,
options=FLAGS)['dev_accu']
FLAGS.best_accu = best_accu
namespace_utils.save_namespace(FLAGS,
path_prefix + ".config.json")
print('ACCU = %.4f' % best_accu)
log_file.write('ACCU = %.4f\n' % best_accu)
print('Start the training loop.')
train_size = trainDataStream.get_num_batch()
max_steps = train_size * FLAGS.max_epochs
total_loss = 0.0
start_time = time.time()
for step in xrange(max_steps):
cur_batch = trainDataStream.nextBatch()
if FLAGS.mode == 'rl_train':
loss_value = train_graph.run_rl_training_2(
sess, cur_batch, FLAGS)
elif FLAGS.mode == 'ce_train':
loss_value = train_graph.run_ce_training(
sess, cur_batch, FLAGS)
total_loss += loss_value
if step % 100 == 0:
print('{} '.format(step), end="")
sys.stdout.flush()
# Save a checkpoint and evaluate the model periodically.
if (step + 1) % trainDataStream.get_num_batch() == 0 or (
step + 1) == max_steps:
print()
duration = time.time() - start_time
print('Step %d: loss = %.2f (%.3f sec)' %
(step, total_loss, duration))
log_file.write('Step %d: loss = %.2f (%.3f sec)\n' %
(step, total_loss, duration))
log_file.flush()
sys.stdout.flush()
total_loss = 0.0
# Evaluate against the validation set.
start_time = time.time()
print('Validation Data Eval:')
res_dict = evaluate(sess,
valid_graph,
devDataStream,
options=FLAGS,
suffix=str(step))
if valid_graph.mode == 'evaluate':
dev_loss = res_dict['dev_loss']
dev_accu = res_dict['dev_accu']
dev_right = int(res_dict['dev_right'])
dev_total = int(res_dict['dev_total'])
print('Dev loss = %.4f' % dev_loss)
log_file.write('Dev loss = %.4f\n' % dev_loss)
print('Dev accu = %.4f %d/%d' %
(dev_accu, dev_right, dev_total))
log_file.write('Dev accu = %.4f %d/%d\n' %
(dev_accu, dev_right, dev_total))
log_file.flush()
if best_accu < dev_accu:
print('Saving weights, ACCU {} (prev_best) < {} (cur)'.
format(best_accu, dev_accu))
saver.save(sess, best_path)
best_accu = dev_accu
FLAGS.best_accu = dev_accu
namespace_utils.save_namespace(
FLAGS, path_prefix + ".config.json")
else:
dev_bleu = res_dict['dev_bleu']
print('Dev bleu = %.4f' % dev_bleu)
log_file.write('Dev bleu = %.4f\n' % dev_bleu)
log_file.flush()
if best_bleu < dev_bleu:
print('Saving weights, BLEU {} (prev_best) < {} (cur)'.
format(best_bleu, dev_bleu))
saver.save(sess, best_path)
best_bleu = dev_bleu
FLAGS.best_bleu = dev_bleu
namespace_utils.save_namespace(
FLAGS, path_prefix + ".config.json")
duration = time.time() - start_time
print('Duration %.3f sec' % (duration))
sys.stdout.flush()
log_file.write('Duration %.3f sec\n' % (duration))
log_file.flush()
log_file.close()
def main(_):
print('Configurations:')
print(FLAGS)
log_dir = FLAGS.model_dir
if not os.path.exists(log_dir):
os.makedirs(log_dir)
path_prefix = log_dir + "/NP2P.{}".format(FLAGS.suffix)
init_model_prefix = FLAGS.init_model # "/u/zhigwang/zhigwang1/sentence_generation/mscoco/logs/NP2P.phrase_ce_train"
log_file_path = path_prefix + ".log"
print('Log file path: {}'.format(log_file_path))
log_file = open(log_file_path, 'wt')
log_file.write("{}\n".format(FLAGS))
log_file.flush()
# save configuration
namespace_utils.save_namespace(FLAGS, path_prefix + ".config.json")
print('Loading train set.')
if FLAGS.infile_format == 'fof':
trainset, train_ans_len = NP2P_data_stream.read_generation_datasets_from_fof(
FLAGS.train_path, isLower=FLAGS.isLower)
if FLAGS.max_answer_len > train_ans_len:
FLAGS.max_answer_len = train_ans_len
else:
trainset, train_ans_len = NP2P_data_stream.read_all_GQA_questions(
FLAGS.train_path, isLower=FLAGS.isLower)
print('Number of training samples: {}'.format(len(trainset)))
print('Loading test set.')
if FLAGS.infile_format == 'fof':
testset, test_ans_len = NP2P_data_stream.read_generation_datasets_from_fof(
FLAGS.test_path, isLower=FLAGS.isLower)
else:
testset, test_ans_len = NP2P_data_stream.read_all_GQA_questions(
FLAGS.test_path, isLower=FLAGS.isLower)
print('Number of test samples: {}'.format(len(testset)))
max_actual_len = max(train_ans_len, test_ans_len)
print('Max answer length: {}, truncated to {}'.format(
max_actual_len, FLAGS.max_answer_len))
word_vocab = None
POS_vocab = None
NER_vocab = None
char_vocab = None
has_pretrained_model = False
best_path = path_prefix + ".best.model"
if os.path.exists(init_model_prefix + ".best.model.index"):
has_pretrained_model = True
print('!!Existing pretrained model. Loading vocabs.')
if FLAGS.with_word:
word_vocab = Vocab(FLAGS.word_vec_path, fileformat='txt2')
print('word_vocab: {}'.format(word_vocab.word_vecs.shape))
if FLAGS.with_char:
char_vocab = Vocab(init_model_prefix + ".char_vocab",
fileformat='txt2')
print('char_vocab: {}'.format(char_vocab.word_vecs.shape))
if FLAGS.with_POS:
POS_vocab = Vocab(init_model_prefix + ".POS_vocab",
fileformat='txt2')
print('POS_vocab: {}'.format(POS_vocab.word_vecs.shape))
if FLAGS.with_NER:
NER_vocab = Vocab(init_model_prefix + ".NER_vocab",
fileformat='txt2')
print('NER_vocab: {}'.format(NER_vocab.word_vecs.shape))
else:
print('Collecting vocabs.')
(allWords, allChars, allPOSs,
allNERs) = NP2P_data_stream.collect_vocabs(trainset)
print('Number of words: {}'.format(len(allWords)))
print('Number of allChars: {}'.format(len(allChars)))
print('Number of allPOSs: {}'.format(len(allPOSs)))
print('Number of allNERs: {}'.format(len(allNERs)))
if FLAGS.with_word:
word_vocab = Vocab(FLAGS.word_vec_path, fileformat='txt2')
if FLAGS.with_char:
char_vocab = Vocab(voc=allChars,
dim=FLAGS.char_dim,
fileformat='build')
char_vocab.dump_to_txt2(path_prefix + ".char_vocab")
if FLAGS.with_POS:
POS_vocab = Vocab(voc=allPOSs,
dim=FLAGS.POS_dim,
fileformat='build')
POS_vocab.dump_to_txt2(path_prefix + ".POS_vocab")
if FLAGS.with_NER:
NER_vocab = Vocab(voc=allNERs,
dim=FLAGS.NER_dim,
fileformat='build')
NER_vocab.dump_to_txt2(path_prefix + ".NER_vocab")
print('word vocab size {}'.format(word_vocab.vocab_size))
sys.stdout.flush()
print('Build DataStream ... ')
trainDataStream = NP2P_data_stream.QADataStream(trainset,
word_vocab,
char_vocab,
POS_vocab,
NER_vocab,
options=FLAGS,
isShuffle=True,
isLoop=True,
isSort=True)
devDataStream = NP2P_data_stream.QADataStream(testset,
word_vocab,
char_vocab,
POS_vocab,
NER_vocab,
options=FLAGS,
isShuffle=False,
isLoop=False,
isSort=True)
print('Number of instances in trainDataStream: {}'.format(
trainDataStream.get_num_instance()))
print('Number of instances in devDataStream: {}'.format(
devDataStream.get_num_instance()))
print('Number of batches in trainDataStream: {}'.format(
trainDataStream.get_num_batch()))
print('Number of batches in devDataStream: {}'.format(
devDataStream.get_num_batch()))
sys.stdout.flush()
init_scale = 0.01
with tf.Graph().as_default():
initializer = tf.random_uniform_initializer(-init_scale, init_scale)
with tf.name_scope("Train"):
with tf.variable_scope("Model",
reuse=None,
initializer=initializer):
train_graph = ModelGraph(word_vocab=word_vocab,
char_vocab=char_vocab,
POS_vocab=POS_vocab,
NER_vocab=NER_vocab,
options=FLAGS,
mode="rl_train_for_phrase")
with tf.name_scope("Valid"):
with tf.variable_scope("Model",
reuse=True,
initializer=initializer):
valid_graph = ModelGraph(word_vocab=word_vocab,
char_vocab=char_vocab,
POS_vocab=POS_vocab,
NER_vocab=NER_vocab,
options=FLAGS,
mode="decode")
initializer = tf.global_variables_initializer()
vars_ = {}
for var in tf.all_variables():
if "word_embedding" in var.name: continue
if not var.name.startswith("Model"): continue
vars_[var.name.split(":")[0]] = var
saver = tf.train.Saver(vars_)
sess = tf.Session()
sess.run(initializer)
if has_pretrained_model:
print("Restoring model from " + init_model_prefix + ".best.model")
saver.restore(sess, init_model_prefix + ".best.model")
print("DONE!")
sys.stdout.flush()
# for first-time rl training, we get the current BLEU score
print("First-time rl training, get the current BLEU score on dev")
sys.stdout.flush()
best_bleu = evaluate(sess,
valid_graph,
devDataStream,
word_vocab,
options=FLAGS)
print('First-time bleu = %.4f' % best_bleu)
log_file.write('First-time bleu = %.4f\n' % best_bleu)
print('Start the training loop.')
sys.stdout.flush()
train_size = trainDataStream.get_num_batch()
max_steps = train_size * FLAGS.max_epochs
total_loss = 0.0
start_time = time.time()
for step in xrange(max_steps):
cur_batch = trainDataStream.nextBatch()
if FLAGS.with_baseline:
# greedy search
(greedy_sentences, _, _,
_) = NP2P_beam_decoder.search(sess,
valid_graph,
word_vocab,
cur_batch,
FLAGS,
decode_mode="greedy")
if FLAGS.with_target_lattice:
(sampled_sentences, sampled_prediction_lengths,
sampled_generator_input_idx, sampled_generator_output_idx
) = cur_batch.sample_a_partition()
else:
# multinomial sampling
(sampled_sentences, sampled_prediction_lengths,
sampled_generator_input_idx,
sampled_generator_output_idx) = NP2P_beam_decoder.search(
sess,
valid_graph,
word_vocab,
cur_batch,
FLAGS,
decode_mode="multinomial")
# calculate rewards
rewards = []
for i in xrange(cur_batch.batch_size):
# print(sampled_sentences[i])
# print(sampled_generator_input_idx[i])
# print(sampled_generator_output_idx[i])
cur_toks = cur_batch.instances[i][1].tokText.split()
# r = sentence_bleu([cur_toks], sampled_sentences[i].split(), smoothing_function=cc.method3)
r = 1.0
b = 0.0
if FLAGS.with_baseline:
b = sentence_bleu([cur_toks],
greedy_sentences[i].split(),
smoothing_function=cc.method3)
# r = metric_utils.evaluate_captions([cur_toks],[sampled_sentences[i]])
# b = metric_utils.evaluate_captions([cur_toks],[greedy_sentences[i]])
rewards.append(1.0 * (r - b))
rewards = np.array(rewards, dtype=np.float32)
# sys.exit(-1)
# update parameters
feed_dict = train_graph.run_encoder(sess,
cur_batch,
FLAGS,
only_feed_dict=True)
feed_dict[train_graph.reward] = rewards
feed_dict[
train_graph.gen_input_words] = sampled_generator_input_idx
feed_dict[
train_graph.in_answer_words] = sampled_generator_output_idx
feed_dict[train_graph.answer_lengths] = sampled_prediction_lengths
(_,
loss_value) = sess.run([train_graph.train_op, train_graph.loss],
feed_dict)
total_loss += loss_value
if step % 100 == 0:
print('{} '.format(step), end="")
sys.stdout.flush()
# Save a checkpoint and evaluate the model periodically.
if (step + 1) % trainDataStream.get_num_batch() == 0 or (
step + 1) == max_steps:
print()
duration = time.time() - start_time
print('Step %d: loss = %.2f (%.3f sec)' %
(step, total_loss, duration))
log_file.write('Step %d: loss = %.2f (%.3f sec)\n' %
(step, total_loss, duration))
log_file.flush()
sys.stdout.flush()
total_loss = 0.0
# Evaluate against the validation set.
start_time = time.time()
print('Validation Data Eval:')
dev_bleu = evaluate(sess,
valid_graph,
devDataStream,
word_vocab,
options=FLAGS)
print('Dev bleu = %.4f' % dev_bleu)
log_file.write('Dev bleu = %.4f\n' % dev_bleu)
log_file.flush()
if best_bleu < dev_bleu:
print('Saving weights, BLEU {} (prev_best) < {} (cur)'.
format(best_bleu, dev_bleu))
best_bleu = dev_bleu
saver.save(sess, best_path) # TODO: save model
duration = time.time() - start_time
print('Duration %.3f sec' % (duration))
sys.stdout.flush()
log_file.write('Duration %.3f sec\n' % (duration))
log_file.flush()
log_file.close()