| dc.description.abstract | Deep Learning is a subcategory of machine learning algorithms and is a subject of relevant
studies in the area of Artificial Intelligence. Characterized in most cases as multi-layered
Artificial Neural Networks, deep learning networks present themselves as a means of achieving
improvements in numerous computational tasks, such as speech recognition, natural
language processing, and object identification in images, item present in the field of computer
vision. Its importance has grown steadily in recent years, and its popularity increases
as vast databases of information and devices with high computational capacity become
accessible. Companies invest in the field of associated research, and new applications
are available to end users, in addition to the strong hope of efficiency in their application
in the health area. This work proposes to analyze the performance and the way that the
loss values evolve until it converge, in a scenario of inevitable overfitting, of two relatively
popular Deep Learning libraries among developers and researchers: Caffe, developed by
the University of Berkley, and TensorFlow, developed by Google. Executions of two known
convolutional networks (AlexNet and GoogLeNet) were conducted as benchmarking in hybrid
architectures that use accelerators and in a cluster, varying hyperparameters of the
networks in a scenario of unavoidable overfitting. The results lead to conclusion that the
TensorFlow library presented a better performance in most cases, and tends to consume
less memory to store network information. However a portion of this performance is due in
part to the use of vectorized instructions, and in a contrary scenario, the Caffe library may
outperform the competitor, despite some technical deficiencies. Besides that, the Caffe library
presents a problem by reaching overfitting with negative values, a fact that should not
happens in a artificial neural network. | |
