Generative-Moment-Matching-Networks
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This is an implementation of the paper Generative Moment Matching Networks, ICML 2015
. The paper can be found here: https://arxiv.org/abs/1502.02761. This implementation is in Python using Tensorflow.
Dependencies
The implementation depends on the following Python libraries:
argparse, cPickle, math, matplotlib, numpy, random, tensorflow
Usage
- Extract the data from
data.tar.gz
into the same folder as that of the implementationgenerativeMomentMatchingNetworks.py
. The data contains two filesmnist.pkl
andlfw.npy
, for the MNIST and LFW datasets respectively. The implementation uses LFW as the TFD (which is used in the paper) is not publicly available. - The implementation
generativeMomentMatchingNetworks.py
needs two command line arguments to work, the dataset (mnist, lfw
) and the network to be used (data_space, code_space
; more in the paper). These can be specified by the-d (or --dataset)
and-n (or --network)
respectively.
Example Usage: python generativeMomentMatchingNetworks.py -d mnist -n code_space
Results
Data Space
Code Space