Machine learning is a difficult subject. There are several alternatives to consider, as well as a lot to keep track of. Thankfully, there’s TensorBoard, which simplifies the procedure.

There are several elements to consider while constructing machine learning models, including the number of training epochs, the loss metric, and even the model structure. Each of these choices has the potential to spread and determine whether a model is effective or not.

What is TensorBoard?

Simply put – a visualization tool included in TensorFlow. It’s handy since it offers unique capabilities like displaying your ML model as a graphical representation with nodes and connections linking them (data flows). Additionally, it enables us to assess the performance of our models in comparison to specified KPIs.

Further motives for Google in inventing TensorBoard were that when we create models using TensorFlow, we have no idea what occurs behind the scenes, especially in algorithms like artificial neural networks, where the operation is a complete black box with a highly complicated structure.


TensorBoard is made up of multiple different parts. Different metrics, such as accuracy, log loss, and squared error may be tracked using these components. They also enable the model to be shown as a graph, among other things.

  • Scalars – When you initially start TensorBoard, the first tab you’ll notice is Scalars. The model’s performance throughout numerous epochs is the emphasis here.

The loss function of the models, as well as any metrics you’ve tracked, are displayed here.

The smoothing function is an important component of this tab. When working with a large number of epochs or a shaky model, it’s easy to lose track of the general trend.

As a result, you want to make sure that your model is developing rather than stalling throughout training.

You can see the general trends of the model during the training phase by raising the smoothness.

The scalars tab is critical for detecting overfitting in a model. For example, if your training measure continues to improve but the validation plot does not, you may be overfitting on the validation set.

  • Graphs and Histograms – You can see the structure of the model you’ve developed by going to the graphs tab. It essentially depicts what goes on behind the scenes.

When you need to explain the structure of the graphs with others, this information comes in handy. There’s also the option of uploading or downloading graphs.

The graph also shows how different metrics are employed and the optimizer, in addition to the basic model structure.

The tabs for distributions and histograms are very similar. They do, however, allow you to examine the same information in a variety of ways.

The distributions tab provides an excellent picture of how the model’s weights have changed over time. This viewpoint might be used as a first indicator of whether or not anything is improper.

The histogram view provides a more precise summary of your model’s exact values learned.

These two representations are used to see if the model is depending too heavily on a small number of weights. Alternatively, if the weights converge across a large number of epochs.

  • Time – The time-series tab is the last tab seen in TensorBoard.

This perspective is quite similar to the view of scalars. The observations of your goal measure for each iteration of training, rather than each epoch, is one difference.

This method of observing model training is significantly more granular. When the model isn’t converging and the progress across epochs isn’t yielding any answers, this form of study is optimal.


TensorBoard is a very useful tool. You can quickly assess your machine learning and deep learning models using a variety of components and views.

The tool is simple to use and gives useful information on how to improve your model’s training.