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
