dest='verbose')
parser.add_argument('--optim',
help='Optimizer algorithm',
default='adagrad',
choices=['adagrad', 'adadelta', 'adam'])
args = parser.parse_args()
utils.config_logger(args.verbose)
logger = utils.get_logger('train')
logger.info('Training with following options: %s' % ' '.join(sys.argv))
#train_pairs = ioutils.read_corpus(args.train, args.lower, args.lang)
#valid_pairs = ioutils.read_corpus(args.validation, args.lower, args.lang)
# whether to generate embeddings for unknown, padding, null
word_dict, embeddings = ioutils.load_embeddings(args.embeddings,
args.vocab,
True,
normalize=True)
# test
#print(word_dict)
print(embeddings)
logger.info('Converting words to indices')
# find out which labels are there in the data
# (more flexible to different datasets)
"""
label_dict = utils.create_label_dict(train_pairs)
train_data = utils.create_dataset(train_pairs, word_dict, label_dict)
valid_data = utils.create_dataset(valid_pairs, word_dict, label_dict)
"""
label_dict = utils.create_label_dict_SSQA()
train_data, labels, sents1, sents2 = utils.create_dataset_SSQA(
args.train, word_dict, label_dict)
for i in vars(args):
f.write(str(i) + "\t" + str(vars(args)[i]) + "\n")
f.close()
else:
args.save = args.load
utils.config_logger(args.verbose)
logger = utils.get_logger('train')
logger.info('Reading training data')
train_pairs, train_max = ioutils.read_corpus(args.train, args.lower, args.lang, args.ratio)
logger.info('Reading validation data')
valid_pairs, valid_max = ioutils.read_corpus(args.validation, args.lower, args.lang)
logger.info('Reading test data')
test_pairs, test_max = ioutils.read_corpus(args.test, args.lower, args.lang)
logger.info('Reading word embeddings')
word_dict, embeddings = ioutils.load_embeddings(args.embeddings, args.vocab)
max_len = None
#print(train_pairs)
#embeddings = utils.normalize_embeddings(embeddings)
logger.debug('Embeddings have shape {} (including unknown, padding and null)'
.format(embeddings.shape))
logger.info('Converting words to indices')
# find out which labels are there in the data (more flexible to different datasets)
label_dict = utils.create_label_dict(train_pairs)
train_data = utils.create_dataset(train_pairs, word_dict, label_dict, max_len, max_len)
valid_data = utils.create_dataset(valid_pairs, word_dict, label_dict, max_len, max_len)
test_data = utils.create_dataset(test_pairs, word_dict, label_dict, max_len, max_len)
#print(train_data.sizes1)
ioutils.write_extra_embeddings(embeddings, args.save)
train_pairs += ioutils.read_corpus(args.additional_training,
args.lower, args.lang)
assert (not args.cont) # Not implemented yet.
# whether to generate embeddings for unknown, padding, null
is_really_cont = args.warm != None or (args.cont and os.path.exists(
os.path.join(args.save, "model.meta")))
warmup_model = args.warm
if is_really_cont:
logger.info('Found a model. Fine-tuning...')
word_dict, embeddings = ioutils.load_embeddings(
args.embeddings,
args.vocab,
generate=False,
normalize=True,
load_extra_from=warmup_model)
params = ioutils.load_params(warmup_model)
else:
word_dict, embeddings = ioutils.load_embeddings(args.embeddings,
args.vocab,
generate=True,
normalize=True)
ioutils.write_params(args.save,
lowercase=args.lower,
language=args.lang,
model=args.model)
ioutils.write_extra_embeddings(embeddings, args.save)
dest='verbose')
parser.add_argument('-e',
help='Print pairs and labels that got a wrong answer',
action='store_true',
dest='errors')
args = parser.parse_args()
utils.config_logger(verbose=args.verbose)
params = ioutils.load_params(args.model)
sess = tf.InteractiveSession()
model_class = utils.get_model_class(params)
model = model_class.load(args.model, sess)
word_dict, embeddings = ioutils.load_embeddings(args.embeddings,
args.vocabulary,
generate=False,
load_extra_from=args.model,
normalize=True)
model.initialize_embeddings(sess, embeddings)
label_dict = ioutils.load_label_dict(args.model)
print('Label dict[Y] : ', label_dict['Y'])
# pairs = ioutils.read_corpus(args.dataset, params['lowercase'],
# params['language'])
#dataset = utils.create_dataset(pairs, word_dict, label_dict)
dataset, labels, sents1, sents2 = utils.create_dataset_SSQA(
args.dataset, word_dict, label_dict)
#for pair in pairs:
#print(pair[0].encode('utf-8'))
#print(pair[1].encode('utf-8'))
#print(pair[2].encode('utf-8'))
loss, acc, answers = model.evaluate(sess, dataset, True)
action='store_true',
dest='verbose',
default=1)
parser.add_argument('--optim',
help='Optimizer algorithm',
default='adagrad',
choices=['adagrad', 'adadelta', 'adam'])
args = parser.parse_args()
utils.config_logger(args.verbose)
logger = utils.get_logger('train')
logger.debug('Training with following options: %s' % ' '.join(sys.argv))
# hwwang change normalize to false
word_dict, embeddings = ioutils.load_embeddings(args.embeddings,
args.vocab,
False,
normalize=False)
gpu_options = tf.GPUOptions(per_process_gpu_memory_fraction=0.6)
sess = tf.Session(config=tf.ConfigProto(gpu_options=gpu_options))
logger.info('Creating model')
vocab_size = embeddings.shape[0]
embedding_size = embeddings.shape[1]
#wordweightdict = ioutils.load_wordnetweight(args.wordnetweight)
#wordweightdict = {}
# 这里 lemma 之前的没有词对
train_pairs, _ = ioutils.read_corpus(args.train, args.lower, args.lang)
valid_pairs, _ = ioutils.read_corpus(args.validation, args.lower,
args.lang)
test_pairs, _ = ioutils.read_corpus(args.test, args.lower, args.lang)