  Convolutional neural networks (CNNs) are a representative class of deep
learning algorithms including convolutional computation that perform
translation-invariant classification of input data based on their hierarchical
architecture. However, classical convolutional neural network learning methods
use the steepest descent algorithm for training, and the learning performance
is greatly influenced by the initial weight settings of the convolutional and
fully connected layers, requiring re-tuning to achieve better performance under
different model structures and data. Combining the strengths of the simulated
annealing algorithm in global search, we propose applying it to the
hyperparameter search process in order to increase the effectiveness of
convolutional neural networks (CNNs). In this paper, we introduce SA-CNN neural
networks for text classification tasks based on Text-CNN neural networks and
implement the simulated annealing algorithm for hyperparameter search.
Experiments demonstrate that we can achieve greater classification accuracy
than earlier models with manual tuning, and the improvement in time and space
for exploration relative to human tuning is substantial.
