[英]Meaning of "drop" in SpaCy custom NER model training?
Below code is an example training loop for SpaCy's named entity recognition( NER
).下面的代码是 SpaCy 命名实体识别 ( NER
) 的示例训练循环。
for itn in range(100):
random.shuffle(train_data)
for raw_text, entity_offsets in train_data:
doc = nlp.make_doc(raw_text)
gold = GoldParse(doc, entities=entity_offsets)
nlp.update([doc], [gold], drop=0.5, sgd=optimizer)
nlp.to_disk("/model")
drop
as per spacy
is the drop out rate. drop
as per spacy
是辍学率。 Can somebody explain the meaning of the same in detail?有人可以详细解释相同的含义吗?
According to the documentation here , the SpaCy Entity Recognizer
is a neural network that should implement the thinc.neural.Model API.根据此处的文档,SpaCy Entity Recognizer
是一个神经网络,应该实现Thinc.neural.Model API。 The drop
argument that you are talking about is something called dropout rate which is a way to optimize a neural network.你所说的drop
参数是一种叫做dropout rate 的东西,它是一种优化神经网络的方法。
The recommended value is 0.2
based on my experience which means that about 20% of the neurons used in this model will be dropped randomly during training.根据我的经验,推荐值为0.2
,这意味着该模型中使用的大约 20% 的神经元将在训练过程中随机丢弃。
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