def test(config):
with open(config.word_emb_file, "r") as fh:
word_mat = np.array(json.load(fh), dtype=np.float32)
with open(config.char_emb_file, "r") as fh:
char_mat = np.array(json.load(fh), dtype=np.float32)
with open(config.test_eval_file, "r") as fh:
eval_file = json.load(fh)
with open(config.test_meta, "r") as fh:
meta = json.load(fh)
total = meta["total"]
graph = tf.Graph()
print("Loading model...")
with graph.as_default() as g:
test_batch = get_dataset(config.test_record_file,
get_record_parser(config, is_test=True),
config).make_one_shot_iterator()
model = Model(config,
test_batch,
word_mat,
char_mat,
trainable=False,
graph=g)
sess_config = tf.ConfigProto(allow_soft_placement=True)
sess_config.gpu_options.allow_growth = True
with tf.Session(config=sess_config) as sess:
sess.run(tf.global_variables_initializer())
saver = tf.train.Saver()
saver.restore(sess, tf.train.latest_checkpoint(config.save_dir))
if config.decay < 1.0:
sess.run(model.assign_vars)
losses = []
answer_dict = {}
remapped_dict = {}
for step in tqdm(range(total // config.batch_size + 1)):
qa_id, loss, yp1, yp2 = sess.run(
[model.qa_id, model.loss, model.yp1, model.yp2])
answer_dict_, remapped_dict_ = convert_tokens(
eval_file, qa_id.tolist(), yp1.tolist(), yp2.tolist())
answer_dict.update(answer_dict_)
remapped_dict.update(remapped_dict_)
losses.append(loss)
loss = np.mean(losses)
metrics = evaluate(eval_file, answer_dict)
with open(config.answer_file, "w") as fh:
json.dump(remapped_dict, fh)
print("Exact Match: {}, F1: {}".format(metrics['exact_match'],
metrics['f1']))
def train(config):
with open(config.word_emb_file, "r") as fh:
word_mat = np.array(json.load(fh), dtype=np.float32)
with open(config.char_emb_file, "r") as fh:
char_mat = np.array(json.load(fh), dtype=np.float32)
with open(config.train_eval_file, "r") as fh:
train_eval_file = json.load(fh)
with open(config.dev_eval_file, "r") as fh:
dev_eval_file = json.load(fh)
with open(config.dev_meta, "r") as fh:
meta = json.load(fh)
dev_total = meta["total"]
print("Building model...")
parser = get_record_parser(config)
train_dataset = get_batch_dataset(config.train_record_file, parser, config)
dev_dataset = get_dataset(config.dev_record_file, parser, config)
handle = tf.placeholder(tf.string, shape=[])
iterator = tf.data.Iterator.from_string_handle(handle,
train_dataset.output_types,
train_dataset.output_shapes)
train_iterator = train_dataset.make_one_shot_iterator()
dev_iterator = dev_dataset.make_one_shot_iterator()
model = Model(config, iterator, word_mat, char_mat)
sess_config = tf.ConfigProto(allow_soft_placement=True)
sess_config.gpu_options.allow_growth = True
loss_save = 100.0
patience = 0
lr = config.init_lr
with tf.Session(config=sess_config) as sess:
writer = tf.summary.FileWriter(config.log_dir)
sess.run(tf.global_variables_initializer())
saver = tf.train.Saver()
train_handle = sess.run(train_iterator.string_handle())
dev_handle = sess.run(dev_iterator.string_handle())
sess.run(tf.assign(model.is_train, tf.constant(True, dtype=tf.bool)))
sess.run(tf.assign(model.lr, tf.constant(lr, dtype=tf.float32)))
for _ in tqdm(range(1, config.num_steps + 1)):
global_step = sess.run(model.global_step) + 1
loss, train_op = sess.run([model.loss, model.train_op],
feed_dict={handle: train_handle})
if global_step % config.period == 0:
loss_sum = tf.Summary(value=[
tf.Summary.Value(tag="model/loss", simple_value=loss),
])
writer.add_summary(loss_sum, global_step)
if global_step % config.checkpoint == 0:
sess.run(
tf.assign(model.is_train, tf.constant(False,
dtype=tf.bool)))
_, summ = evaluate_batch(model, config.val_num_batches,
train_eval_file, sess, "train",
handle, train_handle)
for s in summ:
writer.add_summary(s, global_step)
metrics, summ = evaluate_batch(
model, dev_total // config.batch_size + 1, dev_eval_file,
sess, "dev", handle, dev_handle)
sess.run(
tf.assign(model.is_train, tf.constant(True,
dtype=tf.bool)))
dev_loss = metrics["loss"]
if dev_loss < loss_save:
loss_save = dev_loss
patience = 0
else:
patience += 1
if patience >= config.patience:
lr /= 2.0
loss_save = dev_loss
patience = 0
sess.run(tf.assign(model.lr, tf.constant(lr,
dtype=tf.float32)))
for s in summ:
writer.add_summary(s, global_step)
writer.flush()
filename = os.path.join(config.save_dir,
"model_{}.ckpt".format(global_step))
saver.save(sess, filename)
def train(config):
with open(config.word_emb_file, "r") as fh:
word_mat = np.array(json.load(fh), dtype=np.float32)
with open(config.char_emb_file, "r") as fh:
char_mat = np.array(json.load(fh), dtype=np.float32)
with open(config.train_eval_file, "r") as fh:
train_eval_file = json.load(fh)
with open(config.dev_eval_file, "r") as fh:
dev_eval_file = json.load(fh)
with open(config.dev_meta, "r") as fh:
meta = json.load(fh)
dev_total = meta["total"]
print("Building model...")
parser = get_record_parser(config)
graph = tf.Graph()
with graph.as_default() as g:
train_dataset = get_batch_dataset(config.train_record_file, parser,
config)
dev_dataset = get_dataset(config.dev_record_file, parser, config)
handle = tf.placeholder(tf.string, shape=[])
iterator = tf.data.Iterator.from_string_handle(
handle, train_dataset.output_types, train_dataset.output_shapes)
train_iterator = train_dataset.make_one_shot_iterator()
dev_iterator = dev_dataset.make_one_shot_iterator()
model = Model(config, iterator, word_mat, char_mat, graph=g)
# 如果你指定的设备不存在，允许TF自动分配设备
sess_config = tf.ConfigProto(allow_soft_placement=True)
sess_config.gpu_options.allow_growth = True
loss_save = 100.0
patience = 0
best_f1 = 0.
best_em = 0.
with tf.Session(config=sess_config) as sess:
writer = tf.summary.FileWriter(config.log_dir)
sess.run(tf.global_variables_initializer())
saver = tf.train.Saver()
train_handle = sess.run(train_iterator.string_handle())
dev_handle = sess.run(dev_iterator.string_handle())
if os.path.exists(os.path.join(config.save_dir, "checkpoint")):
saver.restore(sess,
tf.train.latest_checkpoint(config.save_dir))
global_step = max(sess.run(model.global_step), 1)
for _ in tqdm(range(global_step, config.num_steps + 1)):
global_step = sess.run(model.global_step) + 1
loss, train_op = sess.run([model.loss, model.train_op],
feed_dict={
handle: train_handle,
model.dropout: config.dropout
})
if global_step % config.period == 0:
loss_sum = tf.Summary(value=[
tf.Summary.Value(tag="model/loss", simple_value=loss),
])
writer.add_summary(loss_sum, global_step)
if global_step % config.checkpoint == 0:
_, summ = evaluate_batch(model, config.val_num_batches,
train_eval_file, sess, "train",
handle, train_handle)
for s in summ:
writer.add_summary(s, global_step)
metrics, summ = evaluate_batch(
model, dev_total // config.batch_size + 1,
dev_eval_file, sess, "dev", handle, dev_handle)
dev_f1 = metrics["f1"]
dev_em = metrics["exact_match"]
if dev_f1 < best_f1 and dev_em < best_em:
patience += 1
if patience > config.early_stop:
break
else:
patience = 0
best_em = max(best_em, dev_em)
best_f1 = max(best_f1, dev_f1)
for s in summ:
writer.add_summary(s, global_step)
writer.flush()
filename = os.path.join(
config.save_dir, "model_{}.ckpt".format(global_step))
saver.save(sess, filename)
def train(config):
# shape [91589, 300]
with open(config.word_emb_file, "r") as fh:
word_mat = np.array(json.load(fh), dtype=np.float32)
# shape [1427,64]
with open(config.char_emb_file, "r") as fh:
char_mat = np.array(json.load(fh), dtype=np.float32)
# {'context': 'Architecturally, the school has a Catholic character. Atop the Main Building\'s gold dome is a
# golden statue of the Virgin Mary. Immediately in front of the Main Building and facing it, is a copper statue
# of Christ with arms upraised with the legend "Venite Ad Me Omnes". Next to the Main Building is the Basilica of
# the Sacred Heart. Immediately behind the basilica is the Grotto, a Marian place of prayer and reflection. It
# is a replica of the grotto at Lourdes, France where the Virgin Mary reputedly appeared to Saint Bernadette
# Soubirous in 1858. At the end of the main drive (and in a direct line that connects through 3 statues and the
# Gold Dome), is a simple, modern stone statue of Mary.', 'spans': [[0, 15], [15, 16], [17, 20], [21, 27], [28,
# 31], [32, 33], [34, 42], [43, 52], [52, 53], [54, 58], [59, 62], [63, 67], [68, 76], [76, 78], [79, 83], [84,
# 88], [89, 91], [92, 93], [94, 100], [101, 107], [108, 110], [111, 114], [115, 121], [122, 126], [126, 127],
# [128, 139], [140, 142], [143, 148], [149, 151], [152, 155], [156, 160], [161, 169], [170, 173], [174, 180],
# [181, 183], [183, 184], [185, 187], [188, 189], [190, 196], [197, 203], [204, 206], [207, 213], [214, 218],
# [219, 223], [224, 232], [233, 237], [238, 241], [242, 248], [249, 250], [250, 256], [257, 259], [260, 262],
# [263, 268], [268, 269], [269, 270], [271, 275], [276, 278], [279, 282], [283, 287], [288, 296], [297, 299],
# [300, 303], [304, 312], [313, 315], [316, 319], [320, 326], [327, 332], [332, 333], [334, 345], [346, 352],
# [353, 356], [357, 365], [366, 368], [369, 372], [373, 379], [379, 380], [381, 382], [383, 389], [390, 395],
# [396, 398], [399, 405], [406, 409], [410, 420], [420, 421], [422, 424], [425, 427], [428, 429], [430, 437],
# [438, 440], [441, 444], [445, 451], [452, 454], [455, 462], [462, 463], [464, 470], [471, 476], [477, 480],
# [481, 487], [488, 492], [493, 502], [503, 511], [512, 514], [515, 520], [521, 531], [532, 541], [542, 544],
# [545, 549], [549, 550], [551, 553], [554, 557], [558, 561], [562, 564], [565, 568], [569, 573], [574, 579],
# [580, 581], [581, 584], [585, 587], [588, 589], [590, 596], [597, 601], [602, 606], [607, 615], [616, 623],
# [624, 625], [626, 633], [634, 637], [638, 641], [642, 646], [647, 651], [651, 652], [652, 653], [654, 656],
# [657, 658], [659, 665], [665, 666], [667, 673], [674, 679], [680, 686], [687, 689], [690, 694], [694, 695]],
# 'answers': ['Saint Bernadette Soubirous'], 'uuid': '5733be284776f41900661182'} 一共87599 个
with open(config.train_eval_file, "r") as fh:
train_eval_file = json.load(fh)
# dev_eval_file与train_eval_file 格式一样
# 一共10570 个
with open(config.dev_eval_file, "r") as fh:
dev_eval_file = json.load(fh)
# meta 10482
with open(config.dev_meta, "r") as fh:
meta = json.load(fh)
dev_total = meta["total"]
print("BIDAF Building model...")
parser = get_record_parser(config)
graph = tf.Graph()
with graph.as_default() as g:
# context_idxs, ques_idxs, context_char_idxs, ques_char_idxs, y1, y2, qa_id是parse返回的数据具体的shape和数据类型为
# <BatchDataset shapes: ((?, 400), (?, 50), (?, 400, 16), (?, 50, 16), (?, 400), (?, 400), (?,)),
# types: (tf.int32, tf.int32, tf.int32, tf.int32, tf.float32, tf.float32, tf.int64)>
train_data_set = get_batch_dataset(config.train_record_file, parser, config)
dev_data_set = get_dataset(config.dev_record_file, parser, config)
# feed able iterator https://www.bilibili.com/read/cv647026
handle = tf.placeholder(tf.string, shape=[])
iterator = tf.data.Iterator.from_string_handle(handle, train_data_set.output_types, train_data_set.output_shapes)
train_iterator = train_data_set.make_one_shot_iterator()
dev_iterator = dev_data_set.make_one_shot_iterator()
model = Model(config, iterator, word_mat, char_mat, graph=g)
sess_config = tf.ConfigProto(allow_soft_placement=True)
sess_config.gpu_options.allow_growth = True
patience = 0
best_f1 = 0.
best_em = 0.
with tf.Session(config=sess_config) as sess:
writer = tf.summary.FileWriter(config.log_dir)
sess.run(tf.global_variables_initializer())
saver = tf.train.Saver()
train_handle = sess.run(train_iterator.string_handle())
dev_handle = sess.run(dev_iterator.string_handle())
if os.path.exists(os.path.join(config.save_dir, "checkpoint")):
saver.restore(sess, tf.train.latest_checkpoint(config.save_dir))
global_step = max(sess.run(model.global_step))
for _ in tqdm(range(global_step, config.num_steps + 1)):
global_step = sess.run(model.global_step) + 1
loss, train_op = sess.run([model.loss, model.train_op], feed_dict={handle: train_handle,
model.dropout: config.dropout})
if global_step % config.period == 0:
loss_sum = tf.Summary(value=[tf.Summary.Value(tag="model/loss", simple_value=loss), ])
writer.add_summary(loss_sum, global_step)
if global_step % config.checkpoint == 0:
_, summ = evaluate_batch(
model, config.val_num_batches, train_eval_file, sess, "train", handle, train_handle)
for s in summ:
writer.add_summary(s, global_step)
metrics, summ = evaluate_batch(
model, dev_total // config.batch_size + 1, dev_eval_file, sess, "dev", handle, dev_handle)
dev_f1 = metrics["f1"]
dev_em = metrics["exact_match"]
if dev_f1 < best_f1 and dev_em < best_em:
patience +=1
if patience > config.early_stop:
break
else:
patience = 0
best_em = max(best_em, dev_em)
best_f1 = max(best_f1, dev_f1)
for s in summ:
writer.add_summary(s, global_step)
writer.flush()
filename = os.path.join(config.save_dir, "model_{}.ckpt".format(global_step))
saver.save(sess, filename)