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main.py
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main.py
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import tensorflow as tf
import ujson as json
import numpy as np
from tqdm import tqdm
import os
from model import Model
from util import get_record_parser, convert_tokens, evaluate, get_batch_dataset, get_dataset
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, config.use_squad_v2)
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, config.use_squad_v2)
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 evaluate_batch(model, num_batches, eval_file, sess, data_type, handle, str_handle, use_squad_v2):
answer_dict = {}
losses = []
for _ in tqdm(range(1, num_batches + 1)):
qa_id, loss, yp1, yp2, y1, y2, pp1, pp2= sess.run(
[model.qa_id, model.loss, model.yp1, model.yp2, model.y1, model.y2, model.pp1, model.pp2], feed_dict={handle: str_handle})
#print("PREDICTIONS: ", list(zip(pp1, pp2)))
#print("PREDICTIONS: ", list(zip(yp1, yp2)))
#print("ANSWERS : ", list(zip( np.argmax(y1, axis = 1), np.argmax(y2, axis = 1))))
#print("loss: ", loss)
answer_dict_, _ = convert_tokens(
eval_file, qa_id.tolist(), yp1.tolist(), yp2.tolist(), use_squad_v2)
answer_dict.update(answer_dict_)
losses.append(loss)
loss = np.mean(losses)
metrics = evaluate(eval_file, answer_dict, use_squad_v2)
metrics["loss"] = loss
loss_sum = tf.Summary(value=[tf.Summary.Value(
tag="{}/loss".format(data_type), simple_value=metrics["loss"]), ])
f1_sum = tf.Summary(value=[tf.Summary.Value(
tag="{}/f1".format(data_type), simple_value=metrics["f1"]), ])
em_sum = tf.Summary(value=[tf.Summary.Value(
tag="{}/em".format(data_type), simple_value=metrics["exact_match"]), ])
return metrics, [loss_sum, f1_sum, em_sum]
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"]
print("Loading model...")
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)
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))
sess.run(tf.assign(model.is_train, tf.constant(False, dtype=tf.bool)))
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(), config.use_squad_v2)
answer_dict.update(answer_dict_)
remapped_dict.update(remapped_dict_)
losses.append(loss)
loss = np.mean(losses)
metrics = evaluate(eval_file, answer_dict, config.use_squad_v2)
with open(config.answer_file, "w") as fh:
json.dump(remapped_dict, fh)
print("Exact Match: {}, F1: {}, AvNA: {}".format(
metrics['exact_match'], metrics['f1'], metrics['AvNA']))