def train(conf):
loader = Loader(conf['embedding'], conf['text'])
data, label_str, word2vec = loader.load()
data = data[:700]
labels = np.array(label_str[:700], dtype=np.int32)
classifier = BiLstm(2, conf['embedding']['sequence_length'], word2vec.vocab_size, word2vec.embed_size)
trainer = Trainer(classifier, word2vec.embeddings)
trainer.train(data, labels)
def predict(conf):
loader = Loader(conf['embedding'], conf['text'])
data, label_str, word2vec = loader.load()
classifier = BiLstm(7, conf['embedding']['sequence_length'],
word2vec.vocab_size, word2vec.embed_size)
predictor = Predictor(classifier)
res = predictor.predict(data)
f = open('./data/breakdown_predict.pik', 'wb')
print(res[0])
pickle.dump(res, f)
f.close()
def train(conf):
loader = Loader(conf['embedding'], conf['text'])
data, label_str, word2vec = loader.load()
labels = np.zeros_like(label_str)
for idx, val in enumerate(label_str):
if val in gender_mapping:
labels[idx] = gender_mapping[val]
else:
labels[idx] = 0
classifier = BiLstm(4, conf['embedding']['sequence_length'], word2vec.vocab_size, word2vec.embed_size)
trainer = Trainer(classifier, word2vec.embeddings)
trainer.train(data, labels)