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Deep neural networks (DNNs) achieve state-of-the-art performance in many of the tasks such as image classification, speech recognition, and so on, but the principle of them is like a black box. In this article, we propose a method to combine several connected layers into one layer to visualize the transform relations represented by the connected layers. In theory, this method can visualize the transformation between any two layers in DNNs and is more efficient to analyze the changes of the transformation across different layers compared with other visualization algorithms like deconvolution or saliency maps. Furthermore, we visualize the transform relations not only for a specific input image but the class which all the input images belong to.