def predict(self, data, sess, model, index=None, argmax=True, batch_size=100, pt_h=None, pt=None, ensemble=None, verbose=False): if self.crf: assert index is not None predictions = Batch.predict(sess=sess[0], decode_sess=sess[1], model=model, transitions=self.transition_char, crf=self.crf, scores=self.scores[index], decode_holders=self.decode_holders[index], argmax=argmax, batch_size=batch_size, data=data, dr=self.drop_out, pixels=pt, pt_h=pt_h, ensemble=ensemble, verbose=verbose) else: predictions = Batch.predict(sess=sess[0], model=model, crf=self.crf, argmax=argmax, batch_size=batch_size, data=data, dr=self.drop_out, ensemble=ensemble, verbose=verbose) return predictions
def predict(self, data, sess, model, index=None, argmax=True, batch_size=100, pt_h=None, pt=None, ensemble=None, verbose=False): """ 预测标签 :param data: 一个 bucket 中的所有句子 :param sess: [tf.Session],两个,一个是训练的,一个是解码的 :param model: [tf.placeholder],接受 feed 给模型的数据 :param index: 当前 bucket 的序号 :param argmax: :param batch_size: :param pt_h: :param pt: :param ensemble: :param verbose: :return: """ if self.crf: assert index is not None predictions = Batch.predict(sess=sess[0], decode_sess=sess[1], model=model, transitions=self.transition_char, crf=self.crf, scores=self.scores[index], decode_holders=self.decode_holders[index], argmax=argmax, batch_size=batch_size, data=data, dr=self.drop_out, pixels=pt, pt_h=pt_h, ensemble=ensemble, verbose=verbose) else: predictions = Batch.predict(sess=sess[0], model=model, crf=self.crf, argmax=argmax, batch_size=batch_size, data=data, dr=self.drop_out, ensemble=ensemble, verbose=verbose) return predictions