DeepJet_models.py 文件源码

def Dense_model_ConvCSV(Inputs,nclasses,Inputshape,dropoutRate=0.25):
    """
    Inputs similar to 2016 training, but with covolutional layers on each track and sv
    """

    a  = Convolution1D(8, 1, kernel_initializer='lecun_uniform',  activation='relu')(Inputs[1])
    a = Dropout(dropoutRate)(a)
    a  = Convolution1D(8, 1, kernel_initializer='lecun_uniform',  activation='relu')(a)
    a = Dropout(dropoutRate)(a)
    a  = Convolution1D(8, 1, kernel_initializer='lecun_uniform',  activation='relu')(a)
    a = Dropout(dropoutRate)(a)
    a=Flatten()(a)

    c  = Convolution1D(8, 1, kernel_initializer='lecun_uniform',  activation='relu')(Inputs[2])
    c = Dropout(dropoutRate)(c)
    c  = Convolution1D(8, 1, kernel_initializer='lecun_uniform',  activation='relu')(c)
    c = Dropout(dropoutRate)(c)
    c  = Convolution1D(8, 1, kernel_initializer='lecun_uniform',  activation='relu')(c)
    c = Dropout(dropoutRate)(c)
    c=Flatten()(c)

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

    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)
    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