DeepJet_models.py 文件源码

def Dense_model_microPF(Inputs,nclasses,Inputshapes,dropoutRate=-1):
    from keras.layers.local import LocallyConnected1D

    #npf  = Convolution1D(32, 1, kernel_initializer='lecun_uniform',  activation='relu')(Inputs[1])
    #npf = Dropout(dropoutRate)(npf)
    #npf  = Convolution1D(8, 1, kernel_initializer='lecun_uniform',  activation='relu')(npf)
    #npf = Dropout(dropoutRate)(npf)
    #npf  = Convolution1D(4, 1, kernel_initializer='lecun_uniform',  activation='relu')(npf)
    #npf = Dropout(dropoutRate)(npf)
    npf = Flatten()(Inputs[1])



    x = merge( [Inputs[0],npf] , mode='concat')


    x=  Dense(250, activation='relu',kernel_initializer='lecun_uniform')(x)
    x = Dropout(dropoutRate)(x)
    x=  Dense(200, activation='relu',kernel_initializer='lecun_uniform')(x)
    x = Dropout(dropoutRate)(x)
    x=  Dense(100, activation='relu',kernel_initializer='lecun_uniform')(x)
    x = Dropout(dropoutRate)(x)
    x=  Dense(100, activation='relu',kernel_initializer='lecun_uniform')(x)
    x = Dropout(dropoutRate)(x)
    x=  Dense(100, activation='relu',kernel_initializer='lecun_uniform')(x)
    predictions = Dense(nclasses, activation='softmax',kernel_initializer='lecun_uniform')(x)
    model = Model(inputs=Inputs, outputs=predictions)
    return model