  This paper presents a novel method for autonomously enhancing deep neural
network training. My approach employs an Evaluation Neural Network (ENN)
trained via deep reinforcement learning to predict the performance of the
target network. The ENN then works as an additional evaluation function during
backpropagation. Computational experiments with Multi-Layer Perceptrons (MLPs)
demonstrate the method's effectiveness. By processing input data at 0.15^2
times its original resolution, the ENNs facilitated efficient inference.
Results indicate that MLPs trained with the proposed method achieved a mean
test accuracy of 93.02%, which is 2.8% higher than those trained solely with
conventional backpropagation or with L1 regularization. The proposed method's
test accuracy is comparable to networks initialized with He initialization
while reducing the difference between test and training errors. These
improvements are achieved without increasing the number of epochs, thus
avoiding the risk of overfitting. Additionally, the proposed method dynamically
adjusts gradient magnitudes according to the training stage. The optimal ENN
for enhancing MLPs can be predicted, reducing the time spent exploring optimal
training methodologies. The explainability of ENNs is also analyzed using
Grad-CAM, demonstrating their ability to visualize evaluation bases and
supporting the Strong Lottery Ticket hypothesis.
