han1.py 文件源码

def HAN1(MAX_NB_WORDS, MAX_WORDS, MAX_SENTS, EMBEDDING_DIM, WORDGRU, embedding_matrix, DROPOUTPER):
    #model = Sequential()
    wordInputs = Input(shape=(MAX_WORDS,), name='word1', dtype='float32')

    wordEmbedding = Embedding(MAX_NB_WORDS, EMBEDDING_DIM, weights=[embedding_matrix], mask_zero=True, trainable=True, name='emb1')(wordInputs) #Assuming all the sentences have same number of words. Check for input_length again.


    hij = Bidirectional(GRU(WORDGRU, name='gru1', return_sequences=True))(wordEmbedding)


    wordDrop = Dropout(DROPOUTPER, name='drop1')(hij)

    alpha_its, Si = AttentionLayer(name='att1')(wordDrop)   

    v6 = Dense(1, activation="sigmoid", name="dense")(Si)
    #model.add(Dense(1, activation="sigmoid", name="documentOut3"))
    model = Model(inputs=[wordInputs] , outputs=[v6])
    model.compile(loss='binary_crossentropy', optimizer='rmsprop', metrics=['accuracy'])
    return model