A method and computer system for managing a neural network. Data is sent into an input layer in a portion of layers of nodes in the neural network. The data moves on an encode path through the portion such that an output layer in the portion outputs encoded data. The encoded data is sent into the output layer on a decode path through the portion back to the input layer to obtain a reconstruction of the data by the input layer. A determination is made as to whether an undesired amount of error has occurred in the output layer based on the data sent into the input layer and the reconstruction of the data. A number of new nodes is added to the output layer when a determination is present that the undesired amount of the error occurred, enabling reducing the error using the number of the new nodes.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A method for managing a neural network, the method comprising: sending data into an input layer in a portion of layers of nodes in the neural network in which the data moves on an encode path through the portion such that an output layer in the portion outputs encoded data; sending the encoded data into the output layer on a decode path through the portion back to the input layer to obtain a reconstruction of the data by the input layer; determining whether an undesired amount of error has occurred in the output layer based on the data sent into the input layer and the reconstruction of the data; and adding a number of new nodes to the output layer when a determination is present that the undesired amount of the error has occurred, enabling reducing the error using the number of the new nodes. 2. The method of claim 1, wherein determining whether the undesired amount of the error has occurred comprises: comparing the data to the reconstruction of the data; and determining whether a difference between the data and the reconstruction of the data is greater than a threshold for when the undesired amount of the error is present. 3. The method of claim 1 further comprising: adding an additional layer from the layers in the neural network to the portion when the undesired amount of the error is absent, wherein the additional layer becomes the output layer, and wherein the additional layer is in a location subsequent to the output layer; and repeating sending the data into the input layer in the portion of the layers of the nodes in the neural network in which the data moves on the encode path through the portion such that the output layer in the portion outputs encoded data; sending the encoded data into the output layer on the decode path through the portion back to the input layer to obtain the reconstruction of the data by the input layer; and determining whether the undesired amount of the error has occurred in the output layer based on the data sent into the input layer and the reconstruction of the data. 4. The method of claim 1, wherein the portion of the layers is a single layer and the input layer is the output layer and further comprising: selecting another layer in the layers of the nodes as the single layer when the undesired amount of the error is absent; sending the data into the single layer on the encode path such that the single layer outputs encoded data; sending the encoded data into the single layer on the decode path; and determining whether the undesired amount of the error has occurred in the single layer based on the data sent into the input layer and the reconstruction of the data by the single layer. 5. The method of claim 4, wherein sending the data into the input layer in the portion of the layers of the nodes in the neural network in which the data moves on the encode path through the portion such that the output layer in the portion outputs encoded data; sending the encoded data into the output layer on the decode path through the portion back to the input layer to obtain the reconstruction of the data by the input layer; and determining whether the undesired amount of the error has occurred in the output layer based on the data sent into the input layer and the reconstruction of the data; and adding the number of the new nodes in the output layer when the determination is present that the undesired amount of the error has occurred are performed during a normal operation of the neural network. 6. The method of claim 1 further comprising: training the neural network with the number of the new nodes in the output layer. 7. The method of claim 6, wherein training the neural network with the new nodes in the output layer comprises: training the number of the new nodes. 8. The method of claim 6, wherein training the neural network with number of the new nodes in the layer comprises: sending new data on the encode path, wherein the new data trains the number of the new nodes; creating replay data using statistics about encoding of the data by the neural network; and sending the replay data on the encode path. 9. The method of claim 1, wherein the layers in the neural network comprise: encode layers in the layers that encode; and a classifier that classifies the data. 10. The method of claim 1, wherein the neural network is a deep learning neural network. 11. A computer system comprising: a neural network manager that sends data into an input layer in a portion of layers of nodes in the neural network in which the data moves on an encode path through the portion such that an output layer in the portion outputs encoded data; sends the encoded data into the output layer on a decode path through the portion back to the input layer to obtain a reconstruction of the data by the input layer; determines whether an undesired amount of error has occurred in the output layer based on the data sent into the input layer and the reconstruction of the data; and adds a number of new nodes to the output layer when a determination is present that the undesired amount of the error has occurred, enabling reducing the error using the number of the new nodes. 12. The computer system of claim 11, wherein in determining whether the undesired amount of the error has occurred, the neural network manager compares the data to the reconstruction of the data and determines whether a difference between the data and the reconstruction of the data is greater than a threshold for when the undesired amount of the error is present. 13. The computer system of claim 11, wherein the neural network manager adds an additional layer from the layers in the neural network to the portion of the layers in a location subsequent to the output layer in the portion when the undesired amount of the error is absent, wherein the additional layer becomes the output layer; and repeats sending the data into the input layer in the portion of the layers of the nodes in the neural network in which the data moves on the encode path through the portion such that the output layer in the portion outputs encoded data; sending the encoded data into the output layer on the decode path through the portion back to the input layer to obtain the reconstruction of the data by the input layer; and determining whether the undesired amount of the error has occurred in the output layer based on the data sent into the input layer and the reconstruction of the data. 14. The computer system of claim 11, wherein the portion of the layers is a single layer and the input layer is the output layer, wherein the neural network manager selects another layer in the layers of the nodes as the single layer when the undesired amount of the error is absent; sends the data into the single layer such that the single layer outputs encoded data; sends the encoded data into the single layer on the decode path; and determines whether the undesired amount of the error has occurred in the single layer based on the data sent into the input layer and the reconstruction of the data by the single layer. 15. The computer system of claim 14, wherein the neural network manager sends the data into the input layer in the portion of the layers of the nodes in the neural network in which the data moves on the encode path through the portion such that the output layer in the portion outputs the encoded data; sends the encoded data into the output layer on the decode path through the portion back to the input layer to obtain the reconstruction of the data by the input layer; and determines whether the undesired amount of the error has occurred in the output layer based on the data sent into the input layer and the reconstruction of the data; and adds the number of the new nodes in the output layer when the determination is present that the undesired amount of the error has occurred during a normal operation of the neural network. 16. The computer system of claim 11, wherein the neural network manager trains the neural network with the number of the new nodes in the output layer. 17. The computer system of claim 16, wherein in training the neural network with the new nodes in the output layer, the neural network manager trains the number of the new nodes. 18. The computer system of claim 16, wherein in training the neural network with the number of the new nodes in the layer, the neural network manager sends new data on the encode path, wherein the new data trains the number of the new nodes; creates replay data using statistics about encoding data by the neural network; and sends the replay data on the encode path. 19. The computer system of claim 11, wherein the layers in the neural network comprise encode layers in the layers that encode and a classifier that classifies the data. 20. The computer system of claim 11, wherein the neural network is a deep learning neural network. 21. A computer program product for managing a neural network, the computer program product comprising: a computer readable storage media; first program code, stored on the computer readable storage media, for sending data into an input layer in a portion of layers of nodes in the neural network in which the data moves on an encode path through the portion such that an output layer in the portion outputs encoded data; second program code, stored on the computer readable storage media, for sending the encoded data into the output layer on a decode path through the portion back to the input layer to obtain a reconstruction of the data by the input layer; third program code, stored on the computer readable storage media, for determining whether an undesired amount of error has occurred in the output layer based on the data sent into the input layer and the reconstruction of the data; and fourth program code, stored on the computer readable storage media, for adding a number of new nodes to the output layer when a determination is present that the undesired amount of the error has occurred, enabling reducing the error using the number of the new nodes. 22. The computer program product of claim 21, wherein the third program code comprises: program code, stored on the computer readable storage media, for comparing the data to the reconstruction of the data; and program code, stored on the computer readable storage media, for determining whether a difference between the data and the reconstruction of the data is greater than a threshold for when the undesired amount of the error is present. 23. The computer program product of claim 21 further comprising: fifth program code, stored on the computer readable storage media, for adding an additional layer from the layers in the neural network to the portion of the layers in a location subsequent to the output layer in the portion when the undesired amount of the error is absent, wherein the additional layer becomes the output layer; and sixth program code, stored on the computer readable storage media, for repeating running of the first program code, the second program code, and the third program code. 24. The computer program product of claim 21, wherein the portion of the layers is a single layer and the input layer is the output layer and further comprising: fifth program code, stored on the computer readable storage media, for selecting another layer in the layers of the nodes as the single layer when the undesired amount of the error is absent; sixth program code, stored on the computer readable storage media, for sending the data into the single layer such that the single layer outputs the encoded data; seventh program code, stored on the computer readable storage media, for sending the encoded data into the single layer on the decode path; and eighth program code, stored on the computer readable storage media, for determining whether the undesired amount of the error occurred in the single layer based on the data sent into the input layer and the reconstruction of the data by the single layer. 25. The computer program product of claim 21 further comprising: fifth program code, stored on the computer readable storage media, for sending new data on the encode path, wherein the new data trains the number of the new nodes; sixth program code, stored on the computer readable storage media, for creating replay data using statistics about encoding the data by the neural network; and seventh program code, stored on the computer readable storage media, for sending the replay data on the encode path.