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brightmart / text_classification

all kinds of text classification models and more with deep learning
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HAN的attention里为什么加reduce_sum和reduce_max？ #139

Open fengdoudou1895 opened 4 years ago

fengdoudou1895 commented 4 years ago

在HAN的attention里面看到： attetion_logits = tf.reduce_sum(hidden_state_context_similarity,axis = 2) attention_logits_max = tf.reduce_max(attention_logits, axis = 1,keep_dims = True) p_attention = tf.nn.softmax(attetion_logits-attention_logits_max) 原论文里没看到这个操作，请问这是为什么呢？

acadTags commented 4 years ago

This is because of the fact that softmax is shift-invariant by a constant offset in the input.

softmax is invariant under translation by the same value in each coordinate See wikipedia and a StackOverflow answer.

Deducing the maximum value in the attention_logits allows a faster and more stable numerical computation.