  Convolutional Neural Networks(CNNs) are complex systems. They are trained so
they can adapt their internal connections to recognize images, texts and more.
It is both interesting and helpful to visualize the dynamics within such deep
artificial neural networks so that people can understand how these artificial
networks are learning and making predictions. In the field of scientific
simulations, visualization tools like Paraview have long been utilized to
provide insights and understandings. We present in situ TensorView to visualize
the training and functioning of CNNs as if they are systems of scientific
simulations. In situ TensorView is a loosely coupled in situ visualization open
framework that provides multiple viewers to help users to visualize and
understand their networks. It leverages the capability of co-processing from
Paraview to provide real-time visualization during training and predicting
phases. This avoid heavy I/O overhead for visualizing large dynamic systems.
Only a small number of lines of codes are injected in TensorFlow framework. The
visualization can provide guidance to adjust the architecture of networks, or
compress the pre-trained networks. We showcase visualizing the training of
LeNet-5 and VGG16 using in situ TensorView.
