Picasso: A free open-source visualizer for CNNs – merantix – Medium

While it's easier than ever to define and train deep neural networks (DNNs), understanding the learning process remains somewhat opaque. Monitoring the loss or classification error during training won't always prevent your model from learning the wrong thing or learning a proxy for your intended classification task. Regardless of the veracity of this tale, the point is familiar to machine learning researchers: training metrics don't always tell the whole story. And the stakes are higher than ever before: for rising applications of deep learning like autonomous vehicles, these kinds of training errors can be deadly [2]. Fortunately, standard visualizations like partial occlusion [3] and saliency maps [4] provide a sanity check on the learning process.