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The duration of the life cycle in deep neural networks depends on the data configuration decisions that lead to success in obtaining models. Analyzing hyperparameters along the evolution of the network’s execution allows adapting the data, thus reducing the life cycle time. However, there are challenges not only in collecting hyperparameters, but also in modeling the relationships between these data. This work presents a provenance data based approach to address these challenges, proposing a collection mechanism with flexibility in the choice and representation of data to be analyzed. Experiments of the approach with Keras, using a real application provide evidence of the flexibility, the efficiency of data collection, the analysis and the validation of network data.

Uma abordagem para coleta e análise de dados de configurações em redes neurais profundas