lstm_model.py 文件源码

def create_model(options):
    post = Input(shape=(options['MAX_LEN'],))
    if options['USE_EMBEDDING']:
        embedding = Embedding(output_dim=options['EMBEDDING_DIM'], weights = [options['EMBEDDING_MATRIX']] ,input_dim = len(options['VOCAB']) + 2, mask_zero=True ) 
    else:
        embedding = Embedding(output_dim=options['EMBEDDING_DIM'] ,input_dim = len(options['VOCAB']) + 2, mask_zero=True ) 
    embed_post = embedding(post)
    processed_post = Bidirectional(GRU(options['HIDDEN_DIM'], return_sequences = True))(embed_post)
    output = Dense(len(options['CLASSES_2_IX']), activation='softmax')(processed_post)
    model = Model(inputs = [post], outputs = output)
    adam = Adam(clipnorm=1.)
    model.compile(optimizer=adam, loss='categorical_crossentropy',metrics=['accuracy'])
    model.summary()
    return model