AI model automatic generation method based on computational graph evolution
A technology for automatically generating and calculating graphs, applied in computing models, genetic models, calculations, etc., can solve problems such as smaller network differences and long model search processes, so as to prevent performance decline, ensure efficiency, and improve model design efficiency Effect
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Embodiment 1
[0056] As described in step (1), the user prepares numerical calculation data (in csv format or image format), and the data contains label columns; set the maximum evolution generation of the model to 3 generations, and the number of model populations in each generation is 5; preset fitness The smaller the model, the better the performance; calculate the fitness threshold of the graph model - if the optimal model fitness is less than 50, it is considered to meet the evolution end condition, and the calculation stops; the model with a preset fitness of more than 1000 is considered an invalid model.
[0057] As described in step (2), use the genetic random operator to randomly generate 5 models of the first generation, respectively: randomly generate 5 models of the first generation, respectively: computing graph model 1, computing graph model 2, computing graph model 3. Computing graph model 4 and computing graph model 5.
[0058] As described in step (3), encode the model expr...
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