Building an autoencoder

Autoencoders have several interesting applications like anomaly detection or image denoising. They aim at producing an output identical to its inputs. The input will be compressed into a lower dimensional space, encoded. The model then learns to decode it back to its original form.

You will encode and decode the MNIST dataset of handwritten digits, the hidden layer will encode a 32-dimensional representation of the image, which originally consists of 784 pixels (28 x 28). The autoencoder will essentially learn to turn the 784 pixels original image into a compressed 32 pixels image and learn how to use that encoded representation to bring back the original 784 pixels image.

The Sequential model and Dense layers are ready for you to use.

Let's build an autoencoder!

Create a Sequential model.
Add a dense layer with as many neurons as the encoded image dimensions and input_shape the number of pixels in the original image.
Add a final layer with as many neurons as pixels in the input image.
Compile your autoencoder using adadelta as an optimizer and binary_crossentropy loss, then summarise it.

Exercise

Building an autoencoder

Instructions