针对传统二次代价函数在卷积神经网络训练过程中图像识别准确率不高的问题, 提出基于交叉熵代价函数的卷积神经网络算法。经数学推导, 证明交叉熵代价函数较二次代价函数在图像识别中精度更高, 并应用MNIST数据集和CIFAR-10数据集, 使用AlexNet卷积神经网络, 分别采用二次代价函数和交叉熵代价函数对图像识别模型进行训练, 当数字图像识别精确率和损失值稳定后,使用测试数据对代价函数进行多次测试, 对比识别准确率。仿真结果表明, 此方法不仅能提高数字图像识别的准确率, 而且相较于传统的代价函数, 训练模型速度更快, 明显缩减了训练深度神经网络模型的过程。

In order to solve the problem that the accuracy of image recognition is not high in the training process of convolutional neural network by using traditional quadratic cost function, a convolutional neural network algorithm based on cross-entropy cost function is proposed. By mathematical derivation, it is proved that the cross-entropy cost function is more accurate than the quadratic cost function in image recognition. Based on MNIST dataset and CIFAR-10 dataset, and using AlexNet convolutional neural network, the quadratic cost function and the cross-entropy cost function are adopted to train the image recognition model respectively. When the recognition accuracy and loss value of the digital image are stable, the cost function is tested several times by using the test data, and comparison is made to the recognition accuracy of the two functions. The simulation results show that the proposed method can not only improve the accuracy of digital image recognition, but also has a faster model training speed than the traditional cost function. The process of training deep neural network model is obviously