def train(args):
"""
trains the reading comprehension model
"""
logger = logging.getLogger("brc")
logger.info('Loading vocab...')
with open(os.path.join(args.vocab_dir, 'vocab.pkl'), 'rb') as fin:
vocab = pickle.load(fin)
fin.close()
pad_id = vocab.get_id(vocab.pad_token)
brc_data = BRCDataset(args.max_p_num, args.max_p_len, args.max_q_len,
args.prepared_dir, args.train_files, args.dev_files,
args.test_files)
logger.info('Converting text into ids...')
brc_data.convert_to_ids(vocab)
g = tf.Graph()
with g.as_default():
rc_model = RCModel(vocab.embeddings, pad_id, args)
del vocab
# Train
with tf.name_scope("Train"):
logger.info('Training the model...')
rc_model.train(brc_data,
args.epochs,
args.batch_size,
save_dir=args.result_dir,
save_prefix='test.predicted',
dropout_keep_prob=args.dropout_keep_prob)
tf.summary.FileWriter(args.summary_dir, g).close()
with tf.name_scope('Valid'):
assert len(args.dev_files) > 0, 'No dev files are provided.'
logger.info('Evaluating the model on dev set...')
dev_batches = brc_data.gen_mini_batches('dev',
args.batch_size,
pad_id=pad_id,
shuffle=False)
dev_loss, dev_bleu_rouge = rc_model.evaluate(
dev_batches,
result_dir=args.result_dir,
result_prefix='dev.predicted')
logger.info('Loss on dev set: {}'.format(dev_loss))
logger.info('Result on dev set: {}'.format(dev_bleu_rouge))
logger.info('Predicted answers are saved to {}'.format(
os.path.join(args.result_dir)))
with tf.name_scope('Test'):
assert len(args.test_files) > 0, 'No test files are provided.'
logger.info('Predicting answers for test set...')
test_batches = brc_data.gen_mini_batches('test',
args.batch_size,
pad_id=pad_id,
shuffle=False)
rc_model.evaluate(test_batches,
result_dir=args.result_dir,
result_prefix='test.predicted')
def predict(args):
"""
predicts answers for test files
"""
logger = logging.getLogger("brc")
logger.info('Load data_set and vocab...')
# with open(os.path.join(args.vocab_dir, 'vocab.data'), 'rb') as fin:
with open(args.vocab_path, 'rb') as fin:
vocab = pickle.load(fin)
assert len(args.test_files) > 0, 'No test files are provided.'
brc_data = BRCDataset(args.max_p_num,
args.max_p_len,
args.max_q_len,
test_files=args.test_files)
logger.info('Converting text into ids...')
brc_data.convert_to_ids(vocab)
logger.info('Restoring the model...')
rc_model = RCModel(vocab, args)
rc_model.restore(model_dir=args.model_dir, model_prefix=args.algo)
logger.info('Predicting answers for test set...')
test_batches = brc_data.gen_mini_batches('test',
args.batch_size,
pad_id=vocab.get_id(
vocab.pad_token),
shuffle=False)
rc_model.evaluate(test_batches,
result_dir=args.result_dir,
result_prefix='test.predicted')
def evaluate(args):
"""
evaluate the trained model on dev files
"""
logger = logging.getLogger("brc")
logger.info('Load data_set and vocab...')
# with open(os.path.join(args.vocab_dir, 'vocab.data'), 'rb') as fin:
with open(args.vocab_path, 'rb') as fin:
vocab = pickle.load(fin)
assert len(args.dev_files) > 0, 'No dev files are provided.'
brc_data = BRCDataset(args.max_p_num,
args.max_p_len,
args.max_q_len,
dev_files=args.dev_files)
logger.info('Converting text into ids...')
brc_data.convert_to_ids(vocab)
logger.info('Restoring the model...')
rc_model = RCModel(vocab, args)
rc_model.restore(model_dir=args.model_dir, model_prefix=args.algo)
logger.info('Evaluating the model on dev set...')
dev_batches = brc_data.gen_mini_batches('dev',
args.batch_size,
pad_id=vocab.get_id(
vocab.pad_token),
shuffle=False)
dev_loss, dev_bleu_rouge = rc_model.evaluate(dev_batches,
result_dir=args.result_dir,
result_prefix='dev.predicted')
logger.info('Loss on dev set: {}'.format(dev_loss))
logger.info('Result on dev set: {}'.format(dev_bleu_rouge))
logger.info('Predicted answers are saved to {}'.format(
os.path.join(args.result_dir)))
def evaluate(args):
logger = logging.getLogger("QAPointNet")
logger.info("====== evaluating ======")
logger.info('Load data_set and vocab...')
with open(os.path.join(args.vocab_dir, 'vocab.data'), 'rb') as fin:
vocab = pickle.load(fin)
assert len(args.dev_files) > 0, 'No dev files are provided.'
dataloader = BRCDataset(args.max_p_num,args.max_p_len, args.max_q_len,args.save_dir, dev_files=args.dev_files)
num_train_steps = int(
len(dataloader.train_set) / args.batch_size * args.epochs)
num_warmup_steps = int(num_train_steps * args.warmup_proportion)
logger.info('Converting text into ids...')
dataloader.convert_to_ids(vocab)
logger.info('Restoring the model...')
model = RCModel(vocab, num_train_steps,num_warmup_steps,args)
model.restore(args.model_dir, 'BIDAF_42000')
logger.info('Evaluating the model on dev set...')
dev_batches = dataloader.gen_mini_batches('dev', 64, vocab.get_word_id(vocab.pad_token),shuffle=False)
dev_loss, dev_bleu_rouge = model.evaluate(
dev_batches, result_dir=args.result_dir, result_prefix='dev.predicted')
logger.info('Loss on dev set: {}'.format(dev_loss))
logger.info('Result on dev set: {}'.format(dev_bleu_rouge))
logger.info('Predicted answers are saved to {}'.format(os.path.join(args.result_dir)))
def evaluate(args):
"""
对训练好的模型进行验证
"""
logger = logging.getLogger("brc")
logger.info('加wudi...')
logger.info('加载数据集和词汇表...')
with open(os.path.join(args.vocab_dir, 'vocab.data'), 'rb') as fin:
vocab = pickle.load(fin)
assert len(args.dev_files) > 0, '找不到验证文件.'
brc_data = BRCDataset(args.max_p_num,
args.max_p_len,
args.max_q_len,
dev_files=args.dev_files)
logger.info('把文本转化为id序列...')
brc_data.convert_to_ids(vocab)
logger.info('重载模型...')
rc_model = RCModel(vocab, args)
rc_model.restore(model_dir=args.model_dir, model_prefix=args.algo)
logger.info('验证模型...')
dev_batches = brc_data.gen_mini_batches('dev',
args.batch_size,
pad_id=vocab.get_id(
vocab.pad_token),
shuffle=False)
dev_loss, dev_bleu_rouge = rc_model.evaluate(dev_batches,
result_dir=args.result_dir,
result_prefix='dev.predicted')
logger.info('验证集上的损失为: {}'.format(dev_loss))
logger.info('验证集的结果: {}'.format(dev_bleu_rouge))
logger.info('预测的答案证保存到 {}'.format(os.path.join(args.result_dir)))
def predict(args):
"""
预测测试文件的答案
"""
logger = logging.getLogger("brc")
logger.info('加载数据集和词汇表...')
with open(os.path.join(args.vocab_dir, 'vocab.data'), 'rb') as fin:
vocab = pickle.load(fin)
assert len(args.test_files) > 0, '找不到测试文件.'
brc_data = BRCDataset(args.max_p_num,
args.max_p_len,
args.max_q_len,
test_files=args.test_files)
logger.info('把文本转化为id序列...')
brc_data.convert_to_ids(vocab)
logger.info('重载模型...')
rc_model = RCModel(vocab, args)
rc_model.restore(model_dir=args.model_dir, model_prefix=args.algo)
logger.info('预测测试集的答案...')
test_batches = brc_data.gen_mini_batches('test',
args.batch_size,
pad_id=vocab.get_id(
vocab.pad_token),
shuffle=False)
rc_model.evaluate(test_batches,
result_dir=args.result_dir,
result_prefix='test.predicted')
def predict(args):
logger = logging.getLogger("QAPointNet")
logger.info('Load data_set and vocab...')
with open(os.path.join(args.vocab_dir, 'vocab.data'), 'rb') as fin:
vocab = pickle.load(fin)
assert len(args.test_files) > 0, 'No test files are provided.'
dataloader = BRCDataset(args.max_p_num,
args.max_p_len,
args.max_q_len,
args.save_dir,
test_files=args.test_files)
num_train_steps = int(
len(dataloader.train_set) / args.batch_size * args.epochs)
num_warmup_steps = int(num_train_steps * args.warmup_proportion)
logger.info('Converting text into ids...')
dataloader.convert_to_ids(vocab)
logger.info('Restoring the model...')
model = RCModel(vocab, num_train_steps, num_warmup_steps, args)
model.restore(args.model_dir, 'BIDAF_18000')
logger.info('Predicting answers for test set...')
test_batches = dataloader.gen_mini_batches('test',
64,
vocab.get_word_id(
vocab.pad_token),
shuffle=False)
model.evaluate(test_batches,
result_dir=args.result_dir,
result_prefix='test.predicted')
def evaluate(args):
"""
predicts answers for test files
"""
logger = logging.getLogger("brc")
logger.info('Load data_set and vocab...')
with open(os.path.join(args.vocab_dir, 'vocab.data'), 'rb') as fin:
vocab = pickle.load(fin)
# assert len(args.test_files) > 0, 'No test files are provided.'
# brc_data = BRCDataset(args.max_p_num, args.max_p_len, args.max_q_len, args.max_train_sample_num,args.test_files, use_type="test")
brc_data = BRCDataset(args.max_p_num,
args.max_p_len,
args.max_q_len,
args.max_train_sample_num,
args.dev_files,
use_type="dev")
logger.info('Converting text into ids...')
brc_data.convert_to_ids(vocab)
rc_model = S_netModel(vocab, args)
logger.info('Restoring the model...')
rc_model.restore(model_dir=args.model_dir, model_prefix=args.algo)
logger.info('evaluate answers for dev set...')
test_batches = brc_data.gen_mini_batches('dev',
args.batch_size,
pad_id=vocab.get_id(
vocab.pad_token),
shuffle=False)
#rc_model.predict(test_batches,result_dir=args.result_dir, result_prefix=args.result_prefix)
rc_model.evaluate(test_batches,
result_dir=args.result_dir,
result_prefix=args.result_prefix)
def evaluate(args):
"""
evaluate the trained model on dev files
"""
with open(os.path.join(args.vocab_dir, 'vocab.data'), 'rb') as fin:
vocab = pickle.load(fin)
assert len(args.dev_files) > 0, 'No dev files are provided.'
brc_data = BRCDataset(args.max_p_num,
args.max_p_len,
args.max_q_len,
dev_files=args.dev_files)
brc_data.convert_to_ids(vocab)
rc_model = RCModel(vocab, args)
rc_model.restore(model_dir=args.model_dir, model_prefix=args.algo + '_7')
dev_batches = brc_data.gen_mini_batches('dev',
args.batch_size,
pad_id=vocab.get_id(
vocab.pad_token),
shuffle=False)
bleu_rouge = rc_model.evaluate(dev_batches)
def predict(args):
"""
predicts answers for test files
"""
logger = logging.getLogger("brc")
logger.info('Load data_set and vocab...')
with open(os.path.join(args.vocab_dir, 'vocab.data'), 'rb') as fin:
vocab = pickle.load(fin)
assert len(args.test_files) > 0, 'No test files are provided.'
brc_data = BRCDataset(args.max_p_num, args.max_p_len, args.max_q_len,
test_files=args.test_files)
logger.info('Converting text into ids...')
brc_data.convert_to_ids(vocab)
logger.info('Restoring the model...')
rc_model = RCModel(vocab, args)
rc_model.restore(model_dir=args.model_dir, model_prefix=args.algo)
logger.info('Predicting answers for test set...')
test_batches = brc_data.gen_mini_batches('test', args.batch_size,
pad_id=vocab.get_id(vocab.pad_token), shuffle=False)
rc_model.evaluate(test_batches,
result_dir=args.result_dir, result_prefix='test.predicted')
def predict(args):
"""
predicts answers for test files
"""
with open(os.path.join(args.vocab_dir, 'vocab.data'), 'rb') as fin:
vocab = pickle.load(fin)
assert len(args.test_files) > 0, 'No test files are provided.'
brc_data = BRCDataset(args.max_p_num,
args.max_p_len,
args.max_q_len,
test_files=args.test_files)
brc_data.convert_to_ids(vocab)
rc_model = RCModel(vocab, args)
rc_model.restore(model_dir=args.model_dir, model_prefix=args.algo)
test_batches = brc_data.gen_mini_batches('test',
args.batch_size,
pad_id=vocab.get_id(
vocab.pad_token),
shuffle=False)
rc_model.evaluate(test_batches,
result_dir=args.result_dir,
result_prefix='test.predicted')
def evaluate(args):
"""
evaluate the trained model on dev files
"""
logger = logging.getLogger("brc")
logger.info('Load data_set and vocab...')
with open(os.path.join(args.vocab_dir, 'vocab.data'), 'rb') as fin:
vocab = pickle.load(fin)
assert len(args.dev_files) > 0, 'No dev files are provided.'
brc_data = BRCDataset(args.max_p_num, args.max_p_len, args.max_q_len, dev_files=args.dev_files)
logger.info('Converting text into ids...')
brc_data.convert_to_ids(vocab)
logger.info('Restoring the model...')
rc_model = RCModel(vocab, args)
rc_model.restore(model_dir=args.model_dir, model_prefix=args.algo)
logger.info('Evaluating the model on dev set...')
dev_batches = brc_data.gen_mini_batches('dev', args.batch_size,
pad_id=vocab.get_id(vocab.pad_token), shuffle=False)
dev_loss, dev_bleu_rouge = rc_model.evaluate(
dev_batches, result_dir=args.result_dir, result_prefix='dev.predicted')
logger.info('Loss on dev set: {}'.format(dev_loss))
logger.info('Result on dev set: {}'.format(dev_bleu_rouge))
logger.info('Predicted answers are saved to {}'.format(os.path.join(args.result_dir)))
def predict(args):
"""
predicts answers for test files
"""
logger = logging.getLogger("brc")
logger.info('Load data_set and vocab...')
with open(os.path.join(args.vocab_dir, 'vocab.data'), 'rb') as fin:
vocab = pickle.load(fin)
assert len(args.test_files) > 0, 'No test files are provided.'
brc_data = BRCDataset(args.max_p_num,
args.max_p_len,
args.max_q_len,
args.max_word_len,
test_files=args.test_files)
logger.info('Converting text into ids...')
brc_data.convert_to_ids(vocab)
logger.info('Restoring the model...')
rc_model = RCModel(vocab, args)
rc_model.restore(model_dir=args.model_dir, model_prefix=args.algo)
rc_model.finalize()
# 增加完所有操作后采用sess.graph.finalize()
# 来使得整个graph变为只读的
# 注意：tf.train.Saver()
# 也算是往graph中添加node， 所以也必须放在finilize前
# 但是，，tf.train.Saver()
# 只会存储
# 在该Saver声明时已经存在的变量！！！
logger.info('Predicting answers for test set...')
test_batches = brc_data.gen_mini_batches('test',
args.batch_size,
pad_id=vocab.get_id(
vocab.pad_token),
shuffle=False)
rc_model.evaluate(test_batches,
result_dir=args.result_dir,
result_prefix='test.predicted')
def predict(args):
"""
predicts answers for test files
"""
logger = logging.getLogger("brc")
logger.info('Load data_set and vocab...')
with open(args.vocab_path, 'rb') as fin:
vocab = pickle.load(fin)
assert len(args.test_files) > 0, 'No test files are provided.'
brc_data = BRCDataset(args.algo,
args.max_p_num,
args.max_p_len,
args.max_q_len,
args.max_a_len,
test_files=args.test_files)
logger.info('Converting text into ids...')
brc_data.convert_to_ids(vocab)
logger.info('Restoring the model...')
rc_model = RCModel(vocab, args)
rc_model.restore(model_dir=args.model_dir, model_prefix=args.algo)
logger.info('Predicting answers for test set...')
test_batches = brc_data.gen_mini_batches('test',
args.batch_size,
pad_id=vocab.get_id(
vocab.pad_token),
shuffle=False)
if args.algo == 'YESNO':
qa_resultPath = args.test_files[0] #只会有一个文件！
(filepath, tempfilename) = os.path.split(qa_resultPath)
(qarst_filename, extension) = os.path.splitext(tempfilename)
result_prefix = qarst_filename
else:
result_prefix = 'test.predicted.qa'
rc_model.evaluate(test_batches,
result_dir=args.result_dir,
result_prefix=result_prefix)
if args.algo == 'YESNO': #将YESNO结果合并入QA结果
qa_resultPath = args.test_files[0] #只会有一个文件！
yesno_resultPath = args.result_dir + '/' + result_prefix + '.YESNO.json'
out_file_path = args.result_dir + '/' + result_prefix + '.134.class.' + str(
args.run_id) + '.json'
#首先载入YESNO部分的预测结果
yesno_records = {}
with open(yesno_resultPath, 'r') as f_in:
for line in f_in:
sample = json.loads(line)
yesno_records[sample['question_id']] = line
total_rst_num = 0
with open(qa_resultPath, 'r') as f_in:
with open(out_file_path, 'w') as f_out:
for line in f_in:
total_rst_num += 1
sample = json.loads(line)
if sample['question_id'] in yesno_records:
line = yesno_records[sample['question_id']]
f_out.write(line)
print('total rst num : ', total_rst_num)
print('yes no label combining done!')
def train(logger, args):
"""train a model"""
logger.info('Load data_set and vocab...')
with open(os.path.join(args.vocab_dir, 'vocab.data'), 'rb') as fin:
if six.PY2:
vocab = pickle.load(fin)
else:
vocab = pickle.load(fin, encoding='bytes')
logger.info('vocab size is {} and embed dim is {}'.format(
vocab.size(), vocab.embed_dim))
brc_data = BRCDataset(args.max_p_num, args.max_p_len, args.max_q_len,
args.trainset, args.devset)
logger.info('Converting text into ids...')
brc_data.convert_to_ids(vocab)
logger.info('Initialize the model...')
if not args.use_gpu:
place = fluid.CPUPlace()
dev_count = int(os.environ.get('CPU_NUM', multiprocessing.cpu_count()))
else:
place = fluid.CUDAPlace(0)
dev_count = fluid.core.get_cuda_device_count()
# build model
main_program = fluid.Program()
startup_prog = fluid.Program()
if args.enable_ce:
main_program.random_seed = args.random_seed
startup_prog.random_seed = args.random_seed
with fluid.program_guard(main_program, startup_prog):
with fluid.unique_name.guard():
avg_cost, s_probs, e_probs, match, feed_order = rc_model.rc_model(
args.hidden_size, vocab, args)
# clone from default main program and use it as the validation program
inference_program = main_program.clone(for_test=True)
# build optimizer
if args.optim == 'sgd':
optimizer = fluid.optimizer.SGD(
learning_rate=args.learning_rate)
elif args.optim == 'adam':
optimizer = fluid.optimizer.Adam(
learning_rate=args.learning_rate)
elif args.optim == 'rprop':
optimizer = fluid.optimizer.RMSPropOptimizer(
learning_rate=args.learning_rate)
else:
logger.error('Unsupported optimizer: {}'.format(args.optim))
exit(-1)
if args.weight_decay > 0.0:
obj_func = avg_cost + args.weight_decay * l2_loss(main_program)
optimizer.minimize(obj_func)
else:
obj_func = avg_cost
optimizer.minimize(obj_func)
# initialize parameters
place = fluid.CUDAPlace(0) if args.use_gpu else fluid.CPUPlace()
exe = Executor(place)
if args.load_dir:
logger.info('load from {}'.format(args.load_dir))
fluid.io.load_persistables(exe,
args.load_dir,
main_program=main_program)
else:
exe.run(startup_prog)
embedding_para = fluid.global_scope().find_var(
'embedding_para').get_tensor()
embedding_para.set(vocab.embeddings.astype(np.float32), place)
# prepare data
feed_list = [
main_program.global_block().var(var_name)
for var_name in feed_order
]
feeder = fluid.DataFeeder(feed_list, place)
logger.info('Training the model...')
parallel_executor = fluid.ParallelExecutor(
main_program=main_program,
use_cuda=bool(args.use_gpu),
loss_name=avg_cost.name)
print_para(main_program, parallel_executor, logger, args)
for pass_id in range(1, args.pass_num + 1):
pass_start_time = time.time()
pad_id = vocab.get_id(vocab.pad_token)
if args.enable_ce:
train_reader = lambda: brc_data.gen_mini_batches(
'train', args.batch_size, pad_id, shuffle=False)
else:
train_reader = lambda: brc_data.gen_mini_batches(
'train', args.batch_size, pad_id, shuffle=True)
train_reader = read_multiple(train_reader, dev_count)
log_every_n_batch, n_batch_loss = args.log_interval, 0
total_num, total_loss = 0, 0
for batch_id, batch_list in enumerate(train_reader(), 1):
feed_data = batch_reader(batch_list, args)
fetch_outs = parallel_executor.run(
feed=list(feeder.feed_parallel(feed_data, dev_count)),
fetch_list=[obj_func.name],
return_numpy=False)
cost_train = np.array(fetch_outs[0]).mean()
total_num += args.batch_size * dev_count
n_batch_loss += cost_train
total_loss += cost_train * args.batch_size * dev_count
if args.enable_ce and batch_id >= 100:
break
if log_every_n_batch > 0 and batch_id % log_every_n_batch == 0:
print_para(main_program, parallel_executor, logger,
args)
logger.info(
'Average loss from batch {} to {} is {}'.format(
batch_id - log_every_n_batch + 1, batch_id,
"%.10f" % (n_batch_loss / log_every_n_batch)))
n_batch_loss = 0
if args.dev_interval > 0 and batch_id % args.dev_interval == 0:
if brc_data.dev_set is not None:
eval_loss, bleu_rouge = validation(
inference_program, avg_cost, s_probs, e_probs,
match, feed_order, place, dev_count, vocab,
brc_data, logger, args)
logger.info('Dev eval loss {}'.format(eval_loss))
logger.info(
'Dev eval result: {}'.format(bleu_rouge))
pass_end_time = time.time()
time_consumed = pass_end_time - pass_start_time
logger.info('epoch: {0}, epoch_time_cost: {1:.2f}'.format(
pass_id, time_consumed))
logger.info(
'Evaluating the model after epoch {}'.format(pass_id))
if brc_data.dev_set is not None:
eval_loss, bleu_rouge = validation(inference_program,
avg_cost, s_probs,
e_probs, match,
feed_order, place,
dev_count, vocab,
brc_data, logger, args)
logger.info('Dev eval loss {}'.format(eval_loss))
logger.info('Dev eval result: {}'.format(bleu_rouge))
else:
logger.warning(
'No dev set is loaded for evaluation in the dataset!')
logger.info('Average train loss for epoch {} is {}'.format(
pass_id, "%.10f" % (1.0 * total_loss / total_num)))
if pass_id % args.save_interval == 0:
model_path = os.path.join(args.save_dir, str(pass_id))
if not os.path.isdir(model_path):
os.makedirs(model_path)
fluid.io.save_persistables(executor=exe,
dirname=model_path,
main_program=main_program)
if args.enable_ce: # For CE
print("kpis\ttrain_cost_card%d\t%f" %
(dev_count, total_loss / total_num))
if brc_data.dev_set is not None:
print("kpis\ttest_cost_card%d\t%f" %
(dev_count, eval_loss))
print("kpis\ttrain_duration_card%d\t%f" %
(dev_count, time_consumed))
def train(args):
"""
checks data, creates the directories, prepare the vocabulary and embeddings
"""
logger = logging.getLogger("brc")
logger.info('Checking the data files...')
for data_path in args.train_files + args.dev_files + args.test_files:
assert os.path.exists(data_path), '{} file does not exist.'.format(
data_path)
logger.info('Preparing the directories...')
for dir_path in [args.vocab_dir, args.model_dir, args.result_dir]:
if not os.path.exists(dir_path):
os.makedirs(dir_path)
logger.info('Building vocabulary...')
brc_data = BRCDataset(args.max_p_num, args.max_p_len, args.max_q_len,
args.max_train_sample_num, args.train_files)
vocab = Vocab(lower=True)
for word in brc_data.word_iter('train'):
vocab.add(word)
unfiltered_vocab_size = vocab.size()
vocab.filter_tokens_by_cnt(min_cnt=2)
filtered_num = unfiltered_vocab_size - vocab.size()
logger.info('After filter {} tokens, the final vocab size is {}'.format(
filtered_num, vocab.size()))
logger.info('Assigning embeddings...')
vocab.load_pretrained_embeddings(args.word_embedding_path)
#vocab.randomly_init_embeddings(300)
#vocab1.randomly_init_embeddings(300)
logger.info('Saving vocab...')
brc_data.convert_to_ids(vocab)
logger.info('Initialize the model...')
with open(os.path.join(args.vocab_dir, 'vocab.data'), 'wb') as fout:
pickle.dump(vocab, fout)
rc_model = S_netModel(vocab, args)
logger.info('Training the model...')
#rc_model.restore(model_dir=args.model_dir, model_prefix=args.algo +'sys')
#if args.train_as:
# rc_model.restore(model_dir=args.model_dir, model_prefix=args.algo + 'syst')
rc_model.train(brc_data,
args.epochs,
args.batch_size,
save_dir=args.model_dir,
save_prefix=args.algo,
dropout_keep_prob=args.dropout_keep_prob)
logger.info('Done with model training!')
logger.info('evaluate the trained model!')
test_batches = brc_data.gen_mini_batches('test',
args.batch_size,
pad_id=vocab.get_id(
vocab.pad_token),
shuffle=False)
rc_model.evaluate(test_batches,
result_dir=args.result_dir,
result_prefix='test.predicted')
logger.info('Done with model evaluating !')