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main.py
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main.py
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# coding: utf-8
import argparse
import logging
import os
import tensorflow as tf
from preprocessing import load_json, preprocess
from embedding import get_glove
from mrcModel import *
logging.basicConfig(level=logging.INFO)
os.environ["CUDA_VISIBLE_DEVICES"] = "0"
def modelSetup(sess, model, trainDir):
checkpoint = tf.train.get_checkpoint_state(trainDir)
path = ""
if checkpoint:
path = checkpoint.model_checkpoint_path + ".index"
# Model previously exists
if checkpoint and (tf.gfile.Exists(checkpoint.model_checkpoint_path) or tf.gfile.Exists(path)):
model.saver.restore(sess, checkpoint.model_checkpoint_path)
else: # No saved checkpoints
sess.run(tf.global_variables_initializer())
if __name__ == "__main__":
## Static variables
data_dir = "./dataset/"
train_dir = "./train/"
# Hyperparameters
learning_rate = 0.001
batch_size = 60
# Read data
dev_data = load_json(os.path.join(data_dir,"dev-v1.1.json"))
train_data = load_json(os.path.join(data_dir,"train-v1.1.json"))
print("Loading devset:")
preprocess(dev_data, "dev", data_dir)
print("Loading trainset:")
preprocess(train_data, "train", data_dir)
## Getting train and dev data
train_context = os.path.join(data_dir, "train.context")
train_questions = os.path.join(data_dir, "train.question")
train_ans_span = os.path.join(data_dir, "train.span")
dev_context = os.path.join(data_dir, "dev.context")
dev_questions = os.path.join(data_dir, "dev.question")
dev_ans_span = os.path.join(data_dir, "dev.span")
## Create Glove Vector
id2word, word2id, embed_matrix = get_glove(os.path.join(data_dir,"glove.6B.100d.txt"), 100)
# Configuration
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
parser = argparse.ArgumentParser()
parser.add_argument("--mode")
parser.add_argument("--spanMode")
parser.add_argument("--CharCNN")
parser.add_argument("--Highway")
parser.add_argument("--Bidaf")
args = parser.parse_args()
spanMode = False
CharCNN = False
Highway = False
Bidaf = False
if args.spanMode == 'True':
spanMode = True
if args.CharCNN == 'True':
CharCNN = True
if args.Highway == 'True':
Highway = True
if args.Bidaf == 'True':
Bidaf = True
print('Mode Running:')
print('SpanMode: ', spanMode)
print('CharCNN: ', CharCNN)
print('Highway: ', Highway)
print('Bidaf: ', Bidaf)
# Initialize model
mrcModel = mrcModel(id2word, word2id, embed_matrix, CharCNN = CharCNN, Highway = Highway, Bidaf = Bidaf)
# Train Mode
if args.mode == 'train':
print("Training Network")
logFile = logging.FileHandler(os.path.join(train_dir, "logFile.txt"))
logging.getLogger().addHandler(logFile)
with tf.Session(config = config) as sess:
modelSetup(sess, mrcModel, train_dir)
mrcModel.train(sess, train_context, train_questions, train_ans_span, dev_questions, dev_context, dev_ans_span, spanMode= spanMode, CharCNN = CharCNN)
print ("Training Network Finished")
# Test Mode
elif args.mode == 'test':
print("Testing Network")
with tf.Session(config = config) as sess:
modelSetup(sess, mrcModel, train_dir)