Monitoring and troubleshooting machine learning algorithms can be a daunting task, especially if you have to wait a long time for the training to complete before you know the results. To work around this, TensorFlow includes a computational graph visualization tool called **TensorBoard**. With TensorBoard, we can visualize and graph's important values (loss, accuracy, batch training time, and so on) even during training.

# Visualizing graphs in TensorBoard

# Getting ready

To illustrate the various ways we can use TensorBoard, we will reimplement the linear regression model from *The TensorFlow way of linear regression* recipe in Chapter 3, *Linear Regression*. We'll generate linear data with errors, and use TensorFlow loss and...