def main(_):
print('loading data...')
tr_revs, tr_revs_content = read_data_file(FLAGS.train_file_path)
word_idx_map, w2v = creat_word_embedding(tr_revs_content,
FLAGS.embedding_dim)
tr_data = data_parse(tr_revs, word_idx_map, FLAGS.max_sentence_len)
te_revs, _ = read_data_file(FLAGS.test_file_path)
te_data = data_parse(te_revs, word_idx_map, FLAGS.max_sentence_len)
tc_lstm = TC_LSTM(
n_hidden=FLAGS.n_hidden,
n_class=FLAGS.n_class,
max_sentence_len=FLAGS.max_sentence_len,
l2_reg=FLAGS.l2_reg,
)
print('start training...')
tc_lstm.learn(word_idx_map,
w2v,
tr_data,
te_data,
n_iter=FLAGS.n_iter,
batch_size=FLAGS.batch_size,
learning_rate=FLAGS.learning_rate)
def main(_):
print('loading data...')
tr_revs, tr_revs_content = read_data_file(FLAGS.train_file_path)
word_idx_map, w2v = creat_word_embedding(tr_revs_content,
FLAGS.embedding_dim)
tr_x, _, tr_y = data_parse_one_direction(tr_revs, word_idx_map,
FLAGS.max_sentence_len)
te_revs, _ = read_data_file(FLAGS.test_file_path)
te_x, _, te_y = data_parse_one_direction(te_revs, word_idx_map,
FLAGS.max_sentence_len)
cnn = Text_CNN(max_len=2 * FLAGS.max_sentence_len + 1,
n_classes=FLAGS.n_classes)
print('start training...')
cnn.learn(word_idx_map,
w2v, (tr_x, tr_y), (te_x, te_y),
n_iters=FLAGS.n_iters,
batch_size=FLAGS.batch_size,
learning_rate=FLAGS.learning_rate)
import random
import numpy as np
import torch
from sklearn.metrics import accuracy_score
from sklearn.metrics import confusion_matrix, classification_report
from torch import nn
from torch.autograd import Variable
from torch.optim.lr_scheduler import StepLR
import config
import utils
from model import DependencyBLSTM
args = config.args
output_dir = args.project_dir + 'Models/' + args.sim_name + '/'
utils.creat_word_embedding()
def run():
if not os.path.exists(output_dir):
os.makedirs(output_dir)
save_path = args.project_dir + 'Models/' + args.sim_name + '/model.pt'
with open(output_dir + 'config', 'w') as config_file:
argss = (str(args).split('(')[1].split(')')[0].split(','))
for a in argss:
config_file.write("{}\n".format(a))
if os.path.exists(save_path):
model = torch.load(save_path)
model_loaded = True
print('Great!!! Pre-Trained Model Loaded !!!')