Metadata-Version: 2.1
Name: cae
Version: 0.4
Summary: A simple convolutional autoencoder
Home-page: https://github.com/jmmanley/conv-autoencoder
Author: Jason Manley
Author-email: jmanley@rockefeller.edu
License: GPLv3
Description: # 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.
        
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Description-Content-Type: text/markdown
