论文标题
基于Pytorch的深度自动编码器模型构建
Deep Autoencoder Model Construction Based on Pytorch
论文作者
论文摘要
本文提出了基于Pytorch的深度自动编码器模型。该算法将Pytorch的概念引入自动编码器中，并随机清除具有一定概率连接到隐藏层神经元的输入权重，以便实现稀疏网络的效果，这与稀疏自动编码器的起点相似。新算法有效地解决了模型过度拟合的问题，并提高了图像分类的准确性。最后，进行实验，并将实验结果与ELM，RELM，AE，SAE，DAE进行比较。
This paper proposes a deep autoencoder model based on Pytorch. This algorithm introduces the idea of Pytorch into the auto-encoder, and randomly clears the input weights connected to the hidden layer neurons with a certain probability, so as to achieve the effect of sparse network, which is similar to the starting point of the sparse auto-encoder. The new algorithm effectively solves the problem of possible overfitting of the model and improves the accuracy of image classification. Finally, the experiment is carried out, and the experimental results are compared with ELM, RELM, AE, SAE, DAE.
