Denoise noisy digits and make predictions using an autoencoder made in Flax

In my most previous blog post I discussed how to denoise an image, and that post can be read here:- https://medium.com/@tracyrenee61/how-to-denoise-an-image-in-google-colab-97f5cef0b318

In this post I intend to denoise images to use them in an autoencoder written in Flax and Jax.

Autoencoders are trained on encoding input data such as images into a smaller feature vector, and afterward, reconstruct it by a second neural network, called a decoder. An block diagram of an autoencoder can be seen below:-

In order to create the autoencoder in flax, I chose a noisy digit dataset from Kaggle, and it can be accessed here:- https://www.kaggle.com/datasets/kadenm/noisy-digitbased-captcha-images/code

I have written the program in Kaggle’s free online Jupyter Notebook and stored it in my account for the data science company.

Once I created the Jupyter Notebook and defined the problem statement, I imported the libraries that I would need to execute the program, being:-

1. Numpy to create numpy arrays and carry out numerical computations,