When training a neural network, it may be useful to keep track of network parameters, typically the inputs and outputs from the nodes, so you can see whether your model is learning such verifying after each training step if the function error is minimized or not. Of course, writing code to display the behavior of the network during the learning phase, it can be not easy.
$ sudo pip install tensorboard
Fortunately, TensorFlow provides TensorBoard which is a framework designed for analysis and debugging of neural network models. TensorBoard uses the so-called summaries to view the parameters of the model; once a TensorFlow code is executed, we can call TensorBoard to view summaries in a graphical user interface (GUI).
Furthermore, TensorBoard can be used to display and study the TensorFlow's computational graph, that can be very complex for a Deep Neural Network model.