Tensor board is one of the most powerful out of box tool available for model performance visualisation and focus optimisation tweaking.

We all want to train the deep learning models in the most optimum way to increase even the last two decimal of the prediction accuracy. We have so many parameters to tweak in the deep learning model starting from the optimiser and its parameters, activation function, number of layers/filters etc. that finding the right combinations of all these parameters is like finding a needle in the haystack.

Fortunately, we can leverage hyperparameters to tune the performance and accuracy of the model, but we need to have a broad sense of the parameter combination to try and test.

Tensor board is one of the most powerful inbuilt tool available to visualise individual model’s performance based on different metrics and also for comparison among different models. It can guide in ascertaining the ballpark parameter combinations which we can further try with hyperparameter tuning.

In this article, I will discuss the deep learning model visualisation with a combination of optimisers and activation function for simple regression. It will enable us to learn how we can discard unsuitable combinations quickly and focus our performance tuning efforts on a few potential parameters.

**_Step 1: _**We will use the **Scikit learn** make_regression method to generate a random dataset for regression testing and train_test_split to divide the datasets into training and testing set.

```
from sklearn.datasets import make_regression
from sklearn.model_selection import train_test_split
```

** Step 2:**In the code below, we have imported the Tensor Board and deep learning Keras package. We will use Keras for modelling and Tensor Board for visualisation.

python machine-learning data-science data-visualization deep-learning deep learning

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