The effect of different hidden unit number of sparse autoencoder

Sparse autoencoder is the fundamental part in some deep architecture. The hidden layer output is the compression of the input data which gives a better representation of the input than the original raw input. However, the determination of hidden unit number is always experiential. In this paper, the different hidden unit number is discussed. The weight of sparse autoencoder will learn the digital number outline of the handwriting instead of pen strokes when the hidden unit number is smaller. The weight can learn the pen strokes of the handwriting when the hidden unit number is larger.