We introduce network with sub-network, a neural network which their weight layer could be removed into sub-neural networks on demand during inference. This method provides selectivity in the number of weight layer. To develop the parameters which could be used in both base and sub-neural networks models, firstly, the weights and biases are copied from sub-models to base-model. Each model is forwarded separately. Gradients from both networks are averaged and, used to update both networks. From the empirical experiment, our base-model achieves the test-accuracy that is comparable to the regularly trained models, while it maintains the ability to remove the weight layers.
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