In this project we will implement an autoencoder. We provide a python starter code which contains a
project and a
val_grader. Please implement your encoder and decoder as two separate functions in
project, as the grader will load them to encode and decode the testing images. This project is open-ended and has only two criteria:
- The encoded latent vector for each image should be a
numpyarray of less than 8192 byte.
- Input to
encodeand output to
decodefunctions should be an RGB
We provide a sample grader for you to test your code:
python -m val_grader project
We will use a hidden dataset to measure the performance of your autoencoder. The metric we will use is the L1 distance between the original and the decoded images.
Please do not save any intermediate results to disk or memory. During the actual grading we will run
decode in isolation.
Please load your model once when your module starts up and not every time your encode or decode.