我的主管要求我为CNN（应用于文本）实现一个Attention层，但是我很确定它不适用于ConvD1层。
Using Keras I have a pretty straightforward model, with an embedding-layer followed by a convolutional layer.
Since the input for ConvD1 is (#documents, words, embedding_size) followed by a MaxPooling-Layer, I considered dropping max-pool and inserting Attention here, but I really dont know whats my query and value-input in this case.
我知道tf.backend有一个Attention-Layer，但是可以在这里应用它吗？还是我需要某种自我关注？
我需要的是查看哪个（单词）最负责相应的分类。
sequence_input = Input(shape=(MAX_SEQUENCE_LENGTH,), dtype='int32')
embedded_sequences = embedding_layer(sequence_input)
conv1 = Conv1D(filters=2, kernel_size=2, padding='same')(embedded_sequences)
conv1 = MaxPooling1D(pool_size=32)(conv1)
conv1 = Dropout(0.2)(conv1)
x = Dense(50, activation="relu",
kernel_regularizer=regularizers.l2(0.01),
bias_regularizer=regularizers.l2(0.01))(conv1)
x = Dropout(0.3)(x)
preds = Dense(1, activation='sigmoid',name='output')(x)
model = Model(sequence_input, preds)
model.compile(loss='binary_crossentropy',
optimizer='adam',
metrics=[TruePositives(name='true_positives'),
TrueNegatives(name='true_negatives'),
FalseNegatives(name='false_negatives'),
FalsePositives(name='false_positives'),
])
