with tf.Session() as sess: s = socket.socket(socket.AF_INET, socket.SOCK_STREAM) s.bind((socket.gethostname(), 6666)) s.listen(1) print("server ready") while True: c, _ = s.accept() sock_func(c, sess, module) # t = threading.Thread(target=sock_func, args=[c, sess, module]) # t.start() def sock_func(sock, sess, module): try: while True: data = sock.recv(4096).decode("utf-8") print(data, len(data)) result = json.dumps(module.predict(data, sess), ensure_ascii=False).encode("utf-8") sock.send(result) except Exception: print("EXCEPTION") sock.close() if __name__ == '__main__': args = argparse.Namespace() args.save_path = "saves/wikipedia_150/" args.load_from_file = True ner_module = NER.NER(args) open_socket(ner_module)
embeddings_file_path=None, stacked_embeddings=stacked_embeddings) model_params = { "filter_width": 3, "embeddings_dropout": True, "n_filters": [256], "dense_dropout": True, "token_embeddings_dim": 300, "char_embeddings_dim": 50, "cell_type": 'lstm', "use_batch_norm": True, "concat_embeddings": True, "use_crf": True, "use_char_embeddins": True, "net_type": 'rnn', "use_capitalization": False, } net = NER.NER(corp, stacked_embeddings, **model_params) learning_params = { 'dropout_rate': 0.5, 'epochs': 200, 'learning_rate': 0.001, # 0.0003 'batch_size': 20, 'learning_rate_decay': 0.94 } results = net.fit(**learning_params)