DeepLearn_cornet.py 文件源码

def buildModel(loss_type,lamda):

    inpx = Input(shape=(dimx,))
    inpy = Input(shape=(dimy,))

    hx = Dense(hdim_deep,activation='sigmoid')(inpx)
    hx = Dense(hdim_deep2, activation='sigmoid',name='hid_l1')(hx)
    hx = Dense(hdim, activation='sigmoid',name='hid_l')(hx)

    hy = Dense(hdim_deep,activation='sigmoid')(inpy)
    hy = Dense(hdim_deep2, activation='sigmoid',name='hid_r1')(hy)
    hy = Dense(hdim, activation='sigmoid',name='hid_r')(hy)

    #h = Activation("sigmoid")( Merge(mode="sum")([hx,hy]) )
    h =  Merge(mode="sum")([hx,hy]) 

    #recx = Dense(hdim_deep,activation='sigmoid')(h)
    recx = Dense(dimx)(h)
    #recy = Dense(hdim_deep,activation='sigmoid')(h)
    recy = Dense(dimy)(h)

    branchModel = Model( [inpx,inpy],[recx,recy,h])

    #inpx = Input(shape=(dimx,))
    #inpy = Input(shape=(dimy,))

    [recx1,recy1,h1] = branchModel( [inpx, ZeroPadding()(inpy)])
    [recx2,recy2,h2] = branchModel( [ZeroPadding()(inpx), inpy ])

    #you may probably add a reconstruction from combined
    [recx3,recy3,h] = branchModel([inpx, inpy])

    corr=CorrnetCost(-lamda)([h1,h2])

    if loss_type == 1:
        model = Model( [inpx,inpy],[recy1,recx2,recx3,recx1,recy2,recy3,corr])
        model.compile( loss=["mse","mse","mse","mse","mse","mse",corr_loss],optimizer="rmsprop")
    elif loss_type == 2:
        model = Model( [inpx,inpy],[recy1,recx2,recx1,recy2,corr])
        model.compile( loss=["mse","mse","mse","mse",corr_loss],optimizer="rmsprop")
    elif loss_type == 3:
        model = Model( [inpx,inpy],[recy1,recx2,recx3,recx1,recy2,recy3])
        model.compile( loss=["mse","mse","mse","mse","mse","mse"],optimizer="rmsprop")
    elif loss_type == 4:
        model = Model( [inpx,inpy],[recy1,recx2,recx1,recy2])
        model.compile( loss=["mse","mse","mse","mse"],optimizer="rmsprop")

    return model, branchModel