CPC G06N 5/04 (2013.01) [G06F 30/20 (2020.01); G06N 3/105 (2013.01); G06N 5/022 (2013.01); G06F 2111/10 (2020.01)] | 20 Claims |
1. A computer-implemented method for generating source code for a deep learning network, comprising:
extracting, from user-provided multi-modal inputs, one or more items related to generating a deep learning network model, wherein the one or more extracted items comprise an image of a deep learning network design and a classification task to be performed by the deep learning network model;
processing the image to extract information comprising at least one of: text from the image using an optical character recognition process; and edges and nodes from the image using an edge detection process;
retrieving a similar pre-existing deep learning network model, created using deep learning source code corresponding to a first library language, from a repository based at least in part on a comparison of: the information extracted from the image of the deep learning network design; and multiple deep learning network models stored in the repository;
adapting the retrieved pre-existing deep learning network model to the one or more multi-model inputs to generate the deep learning network model, wherein said adapting comprises: changing a last layer in the pre-existing deep learning network model to have a number of nodes corresponding to a number of classes associated with the classification task to be performed by the deep learning network model; and re-training and updating one or more parameters of the retrieved pre-existing deep learning network model;
creating an intermediate representation of the deep learning network model, wherein the intermediate representation comprises: one or more items of data pertaining to the deep learning network model; and one or more design details attributed to the deep learning network model;
automatically converting the intermediate representation into source code corresponding to a second library language that is different than the first library language;
automatically performing a static validation of the deep learning source code to determine whether one or more specified network layers are present in the generated deep learning network model; and
outputting the deep learning source code corresponding to the second library language to at least one user;
wherein the steps are carried out by at least one computing device.
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