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main.py
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main.py
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import os, pdb
import random
import numpy as np
import tensorflow as tf
from model import BIDAF
from train import Trainer
from config import get_args
from evaluator import *
from preprocess import Squad_Dataset
def main():
config = get_args()
dataset = Squad_Dataset(config)
model = BIDAF(config, dataset.word_idx2vec)
config.mode = 'train'
# make run_config
run_config = tf.ConfigProto(log_device_placement=False)
run_config.gpu_options.allow_growth = True
exp_name = '%s_%s_%s' % (config.train_file, config.glove_file, config.lr)
# save train file
if not (os.path.exists(os.path.join(config.save_dir, exp_name))):
os.makedirs(os.path.join(config.save_dir, exp_name))
sess = tf.Session(config=run_config)
init = tf.group(tf.global_variables_initializer(), tf.local_variables_initializer())
sess.run(init)
# model.model_summary()
writer = tf.summary.FileWriter('./logs/', sess.graph)
writer.add_graph(sess.graph)
loader = tf.train.Saver(max_to_keep=5)
if config.mode == 'train':
trainer = Trainer(config, dataset, model, loader, sess, exp_name, writer)
trainer.train()
elif config.mode == 'test':
# load best trainer for testing
print ("restore latest evaluation model\n")
loader.restore(sess, tf.train.latest_checkpoint(os.path.join(config.save_dir, exp_name)))
trainer = Trainer(config, dataset, model, loader, sess, exp_name, writer)
trainer.train()
if __name__ == "__main__":
main()