convolutional.py 文件源码

def model_deepFlavourNoNeutralReference(Inputs,nclasses,nregclasses,dropoutRate=0.1):
    """
    reference 1x1 convolutional model for 'deepFlavour'
    with recurrent layers and batch normalisation
    standard dropout rate it 0.1
    should be trained for flavour prediction first. afterwards, all layers can be fixed
    that do not include 'regression' and the training can be repeated focusing on the regression part
    (check function fixLayersContaining with invert=True)
    """  
    globalvars = BatchNormalization(momentum=0.6,name='globals_input_batchnorm') (Inputs[0])
    cpf    =     BatchNormalization(momentum=0.6,name='cpf_input_batchnorm')     (Inputs[1])
    vtx    =     BatchNormalization(momentum=0.6,name='vtx_input_batchnorm')     (Inputs[2])
    ptreginput = BatchNormalization(momentum=0.6,name='reg_input_batchnorm')     (Inputs[3])

    cpf, vtx = block_deepFlavourBTVConvolutions(
        charged=cpf,
        vertices=vtx,
        dropoutRate=dropoutRate,
        active=True,
        batchnorm=True
        )


    #
    cpf  = LSTM(150,go_backwards=True,implementation=2, name='cpf_lstm')(cpf)
    cpf=BatchNormalization(momentum=0.6,name='cpflstm_batchnorm')(cpf)
    cpf = Dropout(dropoutRate)(cpf)

    vtx = LSTM(50,go_backwards=True,implementation=2, name='vtx_lstm')(vtx)
    vtx=BatchNormalization(momentum=0.6,name='vtxlstm_batchnorm')(vtx)
    vtx = Dropout(dropoutRate)(vtx)


    x = Concatenate()( [globalvars,cpf,vtx ])

    x = block_deepFlavourDense(x,dropoutRate,active=True,batchnorm=True,batchmomentum=0.6)

    flavour_pred=Dense(nclasses, activation='softmax',kernel_initializer='lecun_uniform',name='ID_pred')(x)

    reg = Concatenate()( [flavour_pred, ptreginput ] ) 

    reg_pred=Dense(nregclasses, activation='linear',kernel_initializer='ones',name='regression_pred',trainable=True)(reg)

    predictions = [flavour_pred,reg_pred]
    model = Model(inputs=Inputs, outputs=predictions)
    return model