ningshixian / Lstm_attention
attention-based LSTM/Dense implemented by Keras
Stars: ✭ 168
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LSTM_Attention
X = Input Sequence of length n.
H = LSTM(X); Note that here the LSTM has return_sequences = True,
so H is a sequence of vectors of length n.
s is the hidden state of the LSTM (h and c)
h is a weighted sum over H: 加权和
h = sigma(j = 0 to n-1) alpha(j) * H(j)
weight alpha[i, j] for each hj is computed as follows:
H = [h1,h2,...,hn]
M = tanh(H)
alhpa = softmax(w.transpose * M)
h# = tanh(h)
y = softmax(W * h# + b)
J(theta) = negative_log_likelihood + regularity
attModel1
GitHub 项目
Attention_Recurrent
GitHub 项目
/resAtt.png)
attModel2
GitHub 项目
Example: Attention block*
Attention defined per time series (each TS has its own attention)
attModel3
Github 项目
https://github.com/roebius/deeplearning_keras2/blob/master/nbs2/attention_wrapper.py
attModel4
Github 项目
hierarchical-attention-networks
Github：
synthesio/hierarchical-attention-networks
self-attention-networks
参考
-
https://github.com/Xls1994/CDRextraction/blob/master/customize_layer.py
- AttentivePoolingLayer
- 深度学习笔记——Attention Model（注意力模型）学习总结
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