https://github.com/jmmanley/conv-autoencoder

a convolutional autoencoder in python and keras.
https://github.com/jmmanley/conv-autoencoder

autoencoder computer-vision convolutional-autoencoder convolutional-neural-networks keras python

Last synced: 3 months ago
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a convolutional autoencoder in python and keras.

• Host: GitHub
• URL: https://github.com/jmmanley/conv-autoencoder
• Owner: jmmanley
• License: gpl-3.0
• Created: 2018-10-16T22:15:09.000Z (over 6 years ago)
• Default Branch: master
• Last Pushed: 2020-05-25T14:59:24.000Z (over 4 years ago)
• Last Synced: 2024-04-26T21:47:49.596Z (10 months ago)
• Topics: autoencoder, computer-vision, convolutional-autoencoder, convolutional-neural-networks, keras, python
• Language: Jupyter Notebook
• Size: 75.2 KB
• Stars: 5
• Watchers: 2
• Forks: 5
• Open Issues: 1
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
# Convolutional Autoencoder (CAE) in Python

An implementation of a convolutional autoencoder in python and keras.

## Installation

`pip install cae`

## Usage

```

from cae import cae

import numpy as np

# create a fake dataset, here: 1000 random 224x224 RGB images

images = np.random.normal(size=(1000, 224, 224, 3))

latent_dim = 8 # desired latent dimensionality

model = cae(images.shape[1:], latent_dim) # there are a number of **kwargs

                                          # parameters that are likely

                                          # worth tuning!!!

# TRAIN THE NETWORK

model.fit(images)

# SAVE THE WEIGHTS FOR EASY RELOADING LATER WITH model.load_weights(path)

model.save_weights('PATH/TO/SAVE/')

```

## Final words

`cae.py` contains the implementation, which is tested on the MNIST dataset in `mnist_test.ipynb`.

In general, auto-encoders map an input x to a latent representation y (generally in a much smaller dimensional space), using deterministic functions of the type y = sigma(Wx+b). In order to encode images, it is useful to implement a convolutional architecture. Here, we utilize convolutional layers and max-pooling layers (which allow translation-invariant representations), followed by a flattening and dense layer to encode the images in a reduced-dimensional space. For decoding, you essentially need to perform the inverse operation. For more information on CAEs, consult e.g. http://people.idsia.ch/~ciresan/data/icann2011.pdf.