Autoencoder Network.

By virture of being here, it is assumed that you have gone through the Quick Start.

Todo

Code is done, but text needs to be written in.

The full code for this tutorial with additional commentary can be found in the file pantry.tutorials.autoencoder.py. If you have toolbox cloned or downloaded or just the tutorials downloaded, Run the code as,

Todo

  • Need a validation and testing thats better than just measuring rmse. Can’t find something great.

Notes

This code contains two methods.
  1. A shallow autoencoder with just one layer.
  2. A Convolutional-Deconvolutional autoencoder that uses a deconv layer.

Both these methods are setup for MNIST dataset.

pantry.tutorials.autoencoder.convolutional_autoencoder(dataset=None, verbose=1)[source]

This function is a demo example of a deep convolutional autoencoder. This is an example code. You should study this code rather than merely run it. This is also an example for using the deconvolutional layer or the transposed fractional stride convolutional layers.

Parameters:
  • dataset – Supply a dataset.
  • verbose – Similar to the rest of the dataset.
pantry.tutorials.autoencoder.shallow_autoencoder(dataset=None, verbose=1)[source]

This function is a demo example of a sparse shallow autoencoder. This is an example code. You should study this code rather than merely run it.

Parameters:
  • dataset – Supply a dataset.
  • verbose – Similar to the rest of the dataset.