Autoencoder Implementation
Finally, we can bring it all together and create an autoencoder that restores the input image with the highest possible quality.
We will use the MNIST dataset because it is relatively simple, and the training time for our network will not be too long.
Note
You can find the source code via the following Link. If you want to run the code or even change some components, you can copy the notebook and work with the copy.
We can see that our model accurately restores handwritten digits.
However, if we attempt to generate new data using samples from a Gaussian distribution, the images appear smoothed and resemble random, unstructured noise.
To address this issue, we need to regularize our latent space by using a Variational Autoencoder (VAE).
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Completion rate improved to 5.26Autoencoder Implementation
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Finally, we can bring it all together and create an autoencoder that restores the input image with the highest possible quality.
We will use the MNIST dataset because it is relatively simple, and the training time for our network will not be too long.
Note
You can find the source code via the following Link. If you want to run the code or even change some components, you can copy the notebook and work with the copy.
We can see that our model accurately restores handwritten digits.
However, if we attempt to generate new data using samples from a Gaussian distribution, the images appear smoothed and resemble random, unstructured noise.
To address this issue, we need to regularize our latent space by using a Variational Autoencoder (VAE).
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