Managing Hyperparameter tuning with TensorBoard's HParams

Tuning hyperparameters in a machine learning project can be a real pain. The process is iterative and can take a long time to test all the hyperparameter combinations. But fortunately, HParams, a TensorBoard plugin, comes to the rescue. It allows testing to find the best combination of hyperparameters.

Getting ready

To illustrate how the HParams plugin works, we will use a sequential model implementation on the MNIST dataset. We'll configure HParams and compare several hyperparameter combinations in order to find the best hyperparameter optimization.

How to do it...

1. First, we'll load the libraries necessary for the script:

   import tensorflow as tf
   from tensorboard.plugins.hparams import api as hp
   import numpy as np
   import datetime
   

2. Next, we'll load and prepare the MNIST dataset:

   (x_train, y_train), (x_test, y_test) = tf.keras.datasets.mnist.load_data...