关于模型中CRF层的几个小疑问 about sequence-labeling HOT 7 OPEN

i solve the problem by change the code if condition "i==0" by "j==0" in getTransition(y_train_batch) function,and loss come to 0 as expected,you all can check on that，and will this improve the final accuracy?someone can try that

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Hi，@tomsonsgs ：
I have tried your method，and it works and make loss above 0, but the accuracy didn't improve obviously. I don't understand why did your make it break when j==0. In my opinion，we could ignore the transition-score that the last word of one sentence to the ending_tag("") if breaking when j==0.
For example, we have a true label_sequence when training: B M E O O B E ........ If breaking when j==0, we ignore the transition-score of "E to ". Although my loss came under 0, I think the primary method is right .

Can you tell me why did you make it break when j==0？？

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因为用i==0会让目标路径多加了一个最后一个字母到填充符的转移量，而计算整体路径得分只计算到最后一个字母自身得分没有加之后的转移得分

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你可以看下原始代码关于所有路径得分的计算过程就知道了，他没有加最后的转移得分

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@tomsonsgs
我看他在前向计算总路径得分的时候，对transition的运用并没有看出哪边对最后的转义得分做了省略，能否说明一下具体是哪几步操作呢？

万分感谢！

last_alphas = tf.gather(alphas, tf.range(0, self.batch_size) * (self.num_steps + 2) + length)
将length改成length+1是否可行， 这样i==0应该就不用改了？？！！

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想请问一下为什么代码中crf层的dummy_val设置成-1000？是有什么讲究么？

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@fxh0919 一个极小值，表示某节点取到该类别得分极小，可以-2000等等，因为开始的话必然在起始状态，其他类别的可能性为0，但在log后一般取极小表示概率接近于0

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