dga_lstm.py 文件源码

def build_lstm(input_shape):
    model = Sequential()
    model.add(Masking(input_shape=input_shape, mask_value=-1.))
    # model.add(GRU(128, return_sequences=True))

    model.add(GRU(128, return_sequences=False))
    # Add dropout if overfitting
    # model.add(Dropout(0.5))
    model.add(Dense(1))
    model.add(Activation('sigmoid'))
    model.compile(loss='binary_crossentropy', optimizer='rmsprop', metrics=['accuracy'])
    return model