convolutional.py 文件源码

def convolutional_model_deepcsv(Inputs,nclasses,nregclasses,dropoutRate=-1):

    cpf=Inputs[1]
    vtx=Inputs[2]

    cpf  = Convolution1D(64, 1, kernel_initializer='lecun_uniform',  activation='relu', name='cpf_conv0')(cpf)
    cpf = Dropout(dropoutRate)(cpf)                                                   
    cpf  = Convolution1D(32, 1, kernel_initializer='lecun_uniform',  activation='relu', name='cpf_conv1')(cpf)
    cpf = Dropout(dropoutRate)(cpf)                                                   
    cpf  = Convolution1D(32, 1, kernel_initializer='lecun_uniform',  activation='relu', name='cpf_conv2')(cpf)
    cpf = Dropout(dropoutRate)(cpf)                                                   
    cpf  = Convolution1D(8, 1, kernel_initializer='lecun_uniform',  activation='relu' , name='cpf_conv3')(cpf)


    vtx = Convolution1D(64, 1, kernel_initializer='lecun_uniform',  activation='relu', name='vtx_conv0')(vtx)
    vtx = Dropout(dropoutRate)(vtx)
    vtx = Convolution1D(32, 1, kernel_initializer='lecun_uniform',  activation='relu', name='vtx_conv1')(vtx)
    vtx = Dropout(dropoutRate)(vtx)
    vtx = Convolution1D(32, 1, kernel_initializer='lecun_uniform',  activation='relu', name='vtx_conv2')(vtx)
    vtx = Dropout(dropoutRate)(vtx)
    vtx = Convolution1D(8, 1, kernel_initializer='lecun_uniform',  activation='relu', name='vtx_conv3')(vtx)

    cpf=Flatten()(cpf)
    vtx=Flatten()(vtx)

    x = Concatenate()( [Inputs[0],cpf,vtx ])

    x  = block_deepFlavourDense(x,dropoutRate)

    predictions = Dense(nclasses, activation='softmax',kernel_initializer='lecun_uniform',name='ID_pred')(x)
    model = Model(inputs=Inputs, outputs=predictions)
    return model