attention層的定義：（思路參考https://github.com/philipperemy/keras-attention-mechanism）

# Attention GRU network       
class AttLayer(Layer):
    def __init__(self, **kwargs):
        self.init = initializations.get('normal')
        #self.input_spec = [InputSpec(ndim=3)]
        super(AttLayer, self).__init__(**kwargs)

    def build(self, input_shape):
        assert len(input_shape)==3
        #self.W = self.init((input_shape[-1],1))
        self.W = self.init((input_shape[-1],))
        #self.input_spec = [InputSpec(shape=input_shape)]
        self.trainable_weights = [self.W]
        super(AttLayer, self).build(input_shape)  # be sure you call this somewhere!

    def call(self, x, mask=None):
        eij = K.tanh(K.dot(x, self.W))
        
        ai = K.exp(eij)
        weights = ai/K.sum(ai, axis=1).dimshuffle(0,'x')
        
        weighted_input = x*weights.dimshuffle(0,1,'x')
        return weighted_input.sum(axis=1)

    def get_output_shape_for(self, input_shape):
        return (input_shape[0], input_shape[-1])

具體的用法：

input = Input(shape=(MAX_SEQUENCE_LENGTH,), dtype='int32')
embedded_sequences = embedding_layer(input)
l_lstm = Bidirectional(LSTM(100, return_sequences=True))(embedded_sequences)
l_att = AttLayer()(l_lstm)
preds = Dense(2, activation='softmax')(l_att)
model = Model(sequence_input, preds)
model.compile(loss='categorical_crossentropy',
             optimizer='rmsprop',
             metrics=['acc'])

print("model fitting - attention GRU network")
model.summary()
model.fit(x_train, y_train, validation_data=(x_val, y_val),
         nb_epoch=10, batch_size=50)