  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.
