#! /usr/bin python
import sys
import os
import numpy as np
sys.path.append("./scripts/")
from scripts.classification.textcnn import TextCnn
from data.data_loader import DataLoader
conf_file = sys.argv[1]
loader = DataLoader()
loader.load_dict("data/title_dict.json")
loader.build(conf_file)
data = loader.get_predict()
conf2 = {
"embedding_size": loader.word_vec_len,
"vocab_size": len(loader.weights),
"sequence_len": loader.max_len,
"epochs": 10
}
model = TextCnn(conf2)
model.load_model("model/weights.010-0.9952.hdf5")
result = model.predict(data)
f = open("/mnt/hgfs/share/pornCensor/query.sug.title/query.random.5w", "r")
fw = open("neg", "w")
lines = f.readlines()
for i in range(result.shape[0]):
if result[i][1] > 0.5:
print(lines[i])
else:
fw.write(lines[i])
type=str,
default="conf/query_conf",
help="conf_file containes sample files and labels")
parser.add_argument("--w2v_path",
type=str,
default="/mnt/hgfs/share/pornCensor/query.skip.vec.win3",
help="w2v file which provide w2v")
FLAGS, unparsed = parser.parse_known_args()
print("unparsed: ", unparsed)
params = {"ratio": 0.2, "max_len": 15, "embedding_size": 100}
loader = DataLoader()
loader.set_params(params)
loader.set_w2v(FLAGS.w2v_path)
loader.build(FLAGS.conf_file)
#loader.save_dict("data/title_dict.json")
train_data, test_data, train_label, test_label = loader.get_train_test()
conf = {
"embedding_size": loader.word_vec_len,
"vocab_size": len(loader.weights),
"sequence_len": loader.max_len,
"epochs": 100,
"classes": loader.classes
}
#model = Lr(conf)
model = TextCnn(conf)
#model = Fasttext(conf)
#model = TextRnn(conf)
#model = AttentiveTextRnn(conf)
