A method to reduce a neural network includes: adding a reduced layer, which is reduced from a layer in the neural network, to the neural network; computing a layer loss and a result loss with respect to the reduced layer based on the layer and the reduced layer; and determining a parameter of the reduced layer based on the layer loss and the result loss.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A method to reduce a neural network, the method comprising: adding a first reduced layer, which is reduced from a first layer in the neural network, to the neural network; computing a first layer loss and a first result loss with respect to the first reduced layer based on the first layer and the first reduced layer; and determining a parameter of the first reduced layer based on the first layer loss and the first result loss. 2. The method of claim 1, wherein the determining of the parameter of the first reduced layer comprises: computing a network loss based on the first layer loss and the first result loss; and determining the parameter of the first reduced layer to reduce the network loss. 3. The method of claim 2, further comprising: removing the first layer from the neural network, in response to the network loss being less than a threshold loss. 4. The method of claim 2, wherein the computing of the network loss comprises: determining a layer weight to be applied to the first layer loss to be a value greater than a result weight to be applied to the first result loss, in response to the network loss being greater than or equal to an initial loss; and computing the network loss based on the layer weight, the first layer loss, the result weight, and the first result loss. 5. The method of claim 2, further comprising: determining a layer weight to be applied to the first layer loss to be a value greater than a result weight to be applied to the first result loss, in response to the network loss being less than an initial loss; and computing the network loss based on the first layer weight, the first layer loss, the first result weight, and the first result loss. 6. The method of claim 1, wherein the computing of the first layer loss and the first result loss comprises: computing the first layer loss based on an output of the first layer and an output of the first reduced layer; and computing the first result loss based on the output of the first reduced layer and a training output of training data. 7. The method of claim 6, wherein the computing of the first layer loss comprises computing a similarity between the output of the first layer and the output of the first reduced layer, and the computing of the first result loss comprises computing a similarity between the output of the first reduced layer and the training output. 8. The method of claim 1, further comprising: maintaining the parameter of the first reduced layer, in response to a network loss being less than a threshold loss; adding a second reduced layer, which is reduced from a second layer in the neural network, to the neural network; re-computing a result loss based on the first reduced layer; computing a second layer loss and a second result loss based on the second layer and the second reduced layer; and determining a parameter of the second reduced layer based on the re-computed result loss, the second layer loss, and the second result loss. 9. The method of claim 1, wherein the adding of the first reduced layer comprises: generating reduced layers corresponding to layers in the neural network, respectively; and connecting each of the reduced layers to a layer among the layers in the neural network based on a connection order of the layers in the neural network. 10. The method of claim 9, wherein the computing of the first layer loss and the first result loss comprises: computing layer losses for a same epoch based on an output of each of the layers in the neural network and an output of each of the reduced layers; and computing result losses based on the output of each of the reduced layers and a training output of training data. 11. The method of claim 10, wherein the determining of the parameter of the first reduced layer comprises: computing a network loss based on a weighted sum of the layer losses and a weighted sum of the result losses; and determining parameters of reduced layers to reduce the network loss. 12. The method of claim 1, wherein the adding of the first reduced layer comprises: generating the first reduced layer by reducing a number of parameters of the first layer; and connecting the first reduced layer to another layer in the neural network. 13. The method of claim 1, wherein the adding of the first reduced layer comprises reducing layers in the neural network to a single layer. 14. The method of claim 1, wherein the adding of the first reduced layer comprises reducing a layer connected adjacent to an input layer in the neural network. 15. The method of claim 1, wherein the adding of the first reduced layer comprises reducing a layer connected adjacent to an output layer in the neural network. 16. The method of claim 1, further comprising: repeating the determining of the parameter of the neural network and computation of a network loss computed based on the first layer loss and the first result loss until the network loss is less than a threshold loss. 17. The method of claim 1, further comprising: determining parameters of nodes in the neural network using gradient descent based on a loss to be back-propagated to the neural network and output values of the nodes. 18. The method of claim 1, further comprising: obtaining the neural network comprising layers each including nodes, and connection weights to connect the nodes included in each of the layers to a node included in another layer among the layers. 19. A non-transitory computer-readable medium storing instructions that, when executed by a processor, cause the processor to perform the method of claim 1. 20. An apparatus to reduce a neural network, the apparatus comprising: a memory storing an instruction; and a processor configured to execute the instruction to add a reduced layer, which is reduced from a layer in the neural network, to the neural network, compute a layer loss and a result loss with respect to the reduced layer based on the layer in the neural network and the reduced layer, and determine a parameter of the reduced layer based on the layer loss and the result loss.