Optimizing Convolutional Neural Network Performance by Mitigating Underfitting and Overfitting.

With human society stepping into the data era, deep learning has been widely used in various industries. However, in the training process of deep learning, underfitting and overfitting are often encountered, leading to poor network generalization performance. Based on a Convolutional Neural network (CNN), this paper optimizes the model by mitigating underfitting and overfitting. Incorporating multiple approaches, the accuracy of the model is finally improved by 4 percentage points by adjusting the learning rate and adding regularization, etc.