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Picking a Hyperparameter Tuning Library for Your Model

It is unanimously agreed that model training is more about iterating and tweaking little knobs called hyperparameters to squeeze out the best from your model.

Photo by yinka adeoti on Unsplash. This picture has way fewer knobs to tweak than a typical deep learning model!

During the first three months of my machine learning journey, I quickly realized that I could not train a model that makes the most out of my data; I had to try thousands of different hyperparameter combinations and select the best model for myself.

At that moment, I knew about techniques like GridSearchCV from scikit-learn but I knew that it was not efficient and needed a lot of computing power and time. I needed a more efficient algorithm for keras; eventually, I stumbled upon two hyperparameter tuning libraries: aisaratuners and keras-tuner.

In this article, I will compare both these libraries and report my findings so you can have a better idea of which library is for you!

Aisaratuners library is a dynamic hyperparameter tuning library that tunes your model’s hyperparameters to give you the most optimized model with the best hyperparameters. Initially, it uses Latin Hypercube Sampling, a sophisticated statistical technique to pick hyperparameters, then uses its API for SOTA pattern recognition. In the end, you get the hyperparameters for the optimized model.

Fig. 1: How aisaratuners converges to the best hyperparameters. Source:

Enough talking, let’s test the library to see if it actually gets the job done.

We will use the Fashion MNIST dataset since it is easy to work with and is still slightly difficult to get 90%+ validation accuracy. To keep this article concise, I will only include code relevant to the library and not include the code for loading and preprocessing data. You can refer to the notebooks at the end of this article.

We will train a rather simple but dynamic model, with the help of aisaratuners library. Here we go.

To get started, we need to pip install the package:

!pip install aisaratuners # pip install using a jupyter notebook

We only need two imports from aisaratuners library to get started, yes, it is that simple!

Now comes the fun part, we get to decide which hyperparameters we get to tune dynamically for our model.

We instantiate our Hp object which is a container for all the hyperparameters we wish to tune. You can set min/max ranges, as well.

Next, in a function containing our model, we will include the Hp object and all the ranges we have defined:

One thing I like while working with aisaratuners was the fact that I had to introduce minimal and understandable changes to the model. We just need to use the Hp object we created and access any random number we set in the numrange method in the above cell.

Let’s now run the optimizer and see how the algorithm performs:

We have set 3 rounds, each with 5 trials. This means in any round, the algorithm will sample 5 sets of hyperparameters from the solution space and train models. This is depicted in Fig. 1 above. In each round, the model will try to reduce this solution space. Also, we have set two criteria, one to maximize the validation accuracy and one to minimize validation loss. The mode argument is set to ‘p’ to use the free available API for aisaratuners to function. You could use a commercial API to use as well. Find more information here.

It took me 889.84 seconds to run this optimization.

Please note that this time will vary for everyone and I was running the optimization using Google Colab, which has a variety of GPUs.

It took me less than 15 minutes (14.81) minutes to acquire the best model. That is amazing. It usually would take me at least two hours of manual tuning. Instead, we automatically tuned for the best model using aisaratuners in much less time.

Here are the results:

Fig. 2: Round 1: Orange, Round 2: Green, Round 3: Yellow

The five trials of each round are labeled. My findings were:

Orange: Some variance (e.g. 48% validation accuracy) in the first round, but overall, the accuracies were definitely in the higher region. This is impressive! Aisaratuners algorithm did well in initial sampling.

Green: In the second round, the search space was reduced well enough because all the models were now achieving 90%+ validation accuracies.

Yellow: Even now, when typically I would stop trying to achieve better results, aisaratuners still managed to squeeze a one per cent increase on the validation set.

This was exactly what I needed: an automatic hyperparameter tuner. What exceeded my expectations were the visualizations this library provided me with. The above information can be visualized in a picture-worth-a-thousand-words manner. Interactive visualizations are labeled in the captions.

(Interactive) Infer how in each round the validation accuracy is increasing.
(Interactive) Infer how in the second and third round the variance in the accuracy is becoming negligible. Thus, the tuner has achieve the best model with the range of each hyperparameter you specified
A dynamic overview of the optimization.

This image gives you a sense of what the optimizer did under the hood. The regions where the lines are dense indicates the best value in the specified range for that hyperparameter. For example, aisaratuners knows that the best number of convolutional filters is 150 and the optimum learning rate for the model is around 0.0001454. How cool is that!

(Interactive) This visualization also captures the convergence of each hyperparameter to its best value in the range during the rounds.
(Interactive) 3D visualization of the solution space and the trails. Notice how at high validation accuracies, the trials get closer to the surface indicating the excellent algorithm behind this tuner.

That concludes our tutorial for aisaratuners. As with all things, there are some improvements I would suggest:

  • Including step size to the range functions will allow for more control of ranges
  • Including Boolean customizability to have control over things like optimizer choice (SGD, Adam, RMSProp, etc.)
  • Add the ability to conduct one trial multiple times. Usually, a model has a slight (2% or so) variance in the accuracy, multiple executions will make sure you can take an average of the accuracies. Sure, this will slow the optimization process but will make the results much more accurate. I think this tradeoff is sometimes worth it.
  • Creating well-formatted, easy-to-use documentation.

Aisaratuners definitely worked as claimed, the model I got was accurate and the visualizations were great. But let’s compare its algorithm with keras-tuner.

Keras-tuner is also a hyperparameter tuning library specifically made for keras. One of the most popular algorithms is RandomSearch.

  • RandomSearch is the basic way of testing out different hyperparameter combinations. It randomly samples hyperparameters and tries to converge on the best model. This is still better than grid search, though.

You require Python 3.6 and Tensorflow 2.0 for keras-tuner to work.

!pip install keras-tuner

All code relevant to the library is shared. The rest of the code can be found linked at the end of the article.

Syntactically, both the libraries are very similar. I will try my best to tune to the same hyperparameters as I did using aisaratuners, and also make sure to keep the number of trials and epochs the same. I want the comparison to be fair.

Without further ado, let’s dive into the code. Let’s install the dependencies and set the log directory first:

Unlike aisaratuners, you do not need to contain all our hyperparameters in a predefined container object. Instead, you simply create a model function and make it dynamic as shown below:

The syntax is very similar, although keras-tuner does give you a step option. I find that helpful.

Since keras-tuner does not include the ability to set rounds and trials as aisaratuners has, I have set the number of trials to 15 (equal to total trials aisaratuners had in three rounds of five trials each). This also makes sense since the keras-tuner tuning algorithm is random and cannot intelligently reduce the search space in each round. Furthermore, I like the ability to conduct multiple executions per trail to reduce variance in each trial’s result; however, we are going to keep that at 1, so as to produce the fairest conditions.

Not that we have instantiated the tuner, let’s see it in action.

It took me 1162.78 seconds to complete this optimization.

Please note that this time will vary for everyone and I was running the optimization using Google Colab, which has a variety of GPUs. For the comparisons, I made sure I was using Google Colab’s Tesla T4 GPU instance. You can always check your GPU instance in Colab using !nvidia-smi . In case you need a different GPU instance, you can factory reset your GPU runtime.

So, 19.38 minutes. 15 trials. Not bad, not great, either. Compared to aisaratuners which took 14.81 minutes, keras-tuner completed the optimization much slower. But maybe, it has converged to a better validation accuracy? How much better should the validation accuracy be to compensate for increased optimization time?

It turns out, keras-tuner did get a better validation accuracy — only by 0.27%. One thing I did not like about keras-tuner’s algorithm is that it was unpredictable. For instance, in the seventh trial, one can achieve 92% validation accuracy and in the tenth trial, it could be around 73%. There was not a converging trend as in aisaratuners results (shown in Fig. 2). One would not be exactly sure whether one has got the best hyperparameters mid-way through the optimization process. In other words, had I started this comparison with keras-tuners instead of aisaratuners, no doubt it would have taken me longer. I would then not stop at 15 trials since I did not know if the tuner had reached the highest accuracy due to lack of convergence or ease of visualization.

Here is how you get the summary of results, get the best model’s hyperparameters or get the best model itself:

I cannot show the output of tuner.results_summary() since it was poorly formatted. You can always view the notebook shared at the end of the article.

You can also produce some visualizations using keras-tuner of the tuning process; however, they are complicated to produce. They require using TensorBoard and make use of the logs directory.

I would like to pass on some remarks about keras-tuner before making a final verdict. These are:

  • I believe it cannot work with Python > 3.6. With Python 3.9 out, keras-tuner should make sure their library is compatible with the later versions of Python.
  • Make the visualizations easier to produce, at least initially. This will allow for much easier iterations.
  • Improve the output of tuner.results_summary() . With no convenient visualizations to look at, it was very troublesome to read the summary in poorly formatted output.

keras-tuner also provides pre-made tunable models, but they are not within the scope of this article.

That concludes our comprehensive comparison of the two libraries. We looked at their somewhat different syntax. Using each library, we trained a very similar convolutional neural network on the Fashion MNIST dataset to record the optimization results and the time it took for the algorithms to complete the optimization.

In the end, I chose to stick with the aisaratuners library. I recommend this library because it is much easier to configure, and runs on Python ≥ 3.6. It optimizes faster and the interactive visualizations make the tuning process easy to grasp and more fun. If my suggestions are implemented into their library, it would only make things better.

Let me know what you think down in the comments! :)

Follow me on Medium and GitHub for more content.

The notebooks for either library can be found on GitHub here.

Check out the libraries on GitHub:



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Osama Akhtar

Osama Akhtar

I love to write my opinions and experiences about the technologies I am interested in. I believe interacting plays a huge part in one’s learning.