  Proper optimization of deep neural networks is an open research question
since an optimal procedure to change the learning rate throughout training is
still unknown. Manually defining a learning rate schedule involves troublesome
time-consuming try and error procedures to determine hyperparameters such as
learning rate decay epochs and learning rate decay rates. Although adaptive
learning rate optimizers automatize this process, recent studies suggest they
may produce overffiting and reduce performance when compared to fine-tuned
learning rate schedules. Considering that deep neural networks loss functions
present landscapes with much more saddle points than local minima, we proposed
the Training Aware Sigmoidal Optimizer (TASO), which consists of a two-phases
automated learning rate schedule. The first phase uses a high learning rate to
fast traverse the numerous saddle point, while the second phase uses low
learning rate to slowly approach the center of the local minimum previously
found. We compared the proposed approach with commonly used adaptive learning
rate schedules such as Adam, RMSProp, and Adagrad. Our experiments showed that
TASO outperformed all competing methods in both optimal (i.e., performing
hyperparameter validation) and suboptimal (i.e., using default hyperparameters)
scenarios.
