  In this paper, we propose a novel method for generating a synthetic dataset
obeying Gaussian distribution. Compared to the commonly used benchmark datasets
with unknown distribution, the synthetic dataset has an explicit distribution,
i.e., Gaussian distribution. Meanwhile, it has the same characteristics as the
benchmark dataset MNIST. As a result, we can easily apply Deep Neural Networks
(DNNs) on the synthetic dataset. This synthetic dataset provides a novel
experimental tool to verify the proposed theories of deep learning.
