This differs from lossless arithmetic compression.ģ) Autoencoders are learned automatically from data examples, which is a useful property: it means that it is easy to train specialized instances of the algorithm that will perform well on a specific type of input. An autoencoder trained on pictures of faces would do a rather poor job of compressing pictures of trees, because the features it would learn would be face-specific.Ģ) Autoencoders are lossy, which means that the decompressed outputs will be degraded compared to the original inputs (similar to MP3 or JPEG compression). This is different from, say, the MPEG-2 Audio Layer III (MP3) compression algorithm, which only holds assumptions about 'sound' in general, but not about specific types of sounds. Additionally, in almost all contexts where the term 'autoencoder' is used, the compression and decompression functions are implemented with neural networks.ġ) Autoencoders are data-specific, which means that they will only be able to compress data similar to what they have been trained on.

'Autoencoding' is a data compression algorithm where the compression and decompression functions are 1) data-specific, 2) lossy, and 3) learned automatically from examples rather than engineered by a human. You will need Keras version 2.0.0 or higher to run them. Note: all code examples have been updated to the Keras 2.0 API on March 14, 2017. a simple autoencoder based on a fully-connected layer.In this tutorial, we will answer some common questions about autoencoders, and we will cover code examples of the following models:

Keras chollet