han.py 文件源码

def han2(MAX_NB_WORDS, MAX_WORDS, MAX_SENTS, EMBEDDING_DIM, WORDGRU, embedding_matrix, DROPOUTPER):

    wordInputs = Input(shape=(MAX_WORDS,), name="wordInputs", dtype='float32')

    #print 'in han2 max-nb-words'
    #print MAX_NB_WORDS

    wordEmbedding = Embedding(MAX_NB_WORDS, EMBEDDING_DIM, weights=[embedding_matrix], mask_zero=True, trainable=True, name='wordEmbedding')(wordInputs) 

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

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

    #wordDrop = Dropout(DROPOUTPER, name='wordDrop')(Si)

    wordEncoder = Model(wordInputs, Si)
    # -----------------------------------------------------------------------------------------------

    docInputs = Input(shape=(None, MAX_WORDS), name='docInputs' ,dtype='float32')

    sentenceMasking = Masking(mask_value=0.0, name='sentenceMasking')(docInputs)

    sentEncoding = TimeDistributed(wordEncoder, name='sentEncoding')(sentenceMasking) 

    hi = Bidirectional(GRU(WORDGRU, return_sequences=True), merge_mode='concat', name='gru2')(sentEncoding)

    alpha_s, Vb = AttentionLayer(name='att2')(hi)

    #sentDrop = Dropout(DROPOUTPER, name='sentDrop')(Vb)

    v6 = Dense(1, activation="sigmoid", kernel_initializer = 'he_normal', name="dense")(Vb)
    model = Model(inputs=[docInputs] , outputs=[v6])

    sgd = optimizers.SGD(lr=0.01, decay=1e-6, momentum=0.9, nesterov=True)
    model.compile(loss='binary_crossentropy', optimizer=sgd, metrics=['accuracy'])

    return model, wordEncoder