Deep learning for the game of Go achieved considerable success with the victory of AlphaGo against Ke Jie in May 2017. Thus far, there is no clear understanding of why deep learning performs so well in the game of Go. In this paper, we introduce visualization techniques used in image recognition that provide insights into the function of intermediate layers and the operation of the Go policy network. When used as a diagnostic tool, these visualizations enable us to understand what occurs during the training process of policy networks. Further, we introduce a visualization technique that performs a sensitivity analysis of the classifier output by occluding portions of the input Go board, and revealing parts that important for predicting the next move. Further, we attempt to identify important areas through Grad-CAM and combine it with the Go board to provide explanations for next move decisions.