network.py 文件源码

def build_output(self):
        pi = Dense(1, activation='sigmoid', kernel_initializer=self.init,
                   kernel_regularizer=l1_l2(self.l1_coef, self.l2_coef),
                   name='pi')(self.decoder_output)

        disp = Dense(1, activation=DispAct,
                     kernel_initializer=self.init,
                     kernel_regularizer=l1_l2(self.l1_coef,
                                              self.l2_coef),
                     name='dispersion')(self.decoder_output)

        mean = Dense(self.output_size, activation=MeanAct, kernel_initializer=self.init,
                       kernel_regularizer=l1_l2(self.l1_coef, self.l2_coef),
                       name='mean')(self.decoder_output)
        output = ColWiseMultLayer(name='output')([mean, self.sf_layer])
        output = SliceLayer(0, name='slice')([output, disp, pi])

        zinb = ZINB(pi, theta=disp, ridge_lambda=self.ridge, debug=self.debug)
        self.loss = zinb.loss
        self.extra_models['pi'] = Model(inputs=self.input_layer, outputs=pi)
        self.extra_models['dispersion'] = Model(inputs=self.input_layer, outputs=disp)
        self.extra_models['mean_norm'] = Model(inputs=self.input_layer, outputs=mean)
        self.extra_models['decoded'] = Model(inputs=self.input_layer, outputs=self.decoder_output)

        self.model = Model(inputs=[self.input_layer, self.sf_layer], outputs=output)

        if self.ae:
            self.encoder = self.get_encoder()