This is an implementation of a simple variational autoencoder which trains on the MNIST dataset and generates similar images of digits.

1. CUDA toolkit 7.5
2. cuDNN v5
3. TensorFlow (https://github.com/tensorflow/tensorflow/tree/r0.10) from r0.10 branch, select binary compatible with the above.

Run python trainScriptClass.py. It will train a simple 2 layer VAE generator with 2 layer encoder for training. The parameters are defined below:

1. batch_size: size of the training and testing batch (for batch size of 20, it nearly reached 11 GB on NVIDIA-Titan X Maxwell)
2. X_size: size of the input (here it is the total number of pixels)
3. hidden_enc_1_size: hidden layer 1 size in the encoder
4. hidden_enc_2_size: hidden layer 2 size in the encoder
5. hidden_gen_1_size: hidden layer 1 size in the generator
6. hidden_gen_2_size: hidden layer 2 size in the generator
7. z_size: size of the latent variable

The model trains with a default learning rate of 1e-4 using the adam optimizer.

The model is trained for 200000 iterations and 20 randomly generated samples and the checkpoint of the corresponding model are saved in `generated_class/' directory after every 50000 iterations.

1. This was implemented based on the Carl Doersch's tutorial available at: https://arxiv.org/abs/1606.05908
2. Another useful reference for implementing VAEs is: https://jmetzen.github.io/2015-11-27/vae.html

Feb 10, 2017

• Added convolution + deconvolution based VAE
• Batch size is again fixed at initialization, have to alter the technique.

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• Added support for a Beta weighting term in the KL Divergence loss
• Batch size is no longer fixed at initialization
• Added functions to encode a given x and decode a given z, and also to perform both these operations to generate an image "like" the given one.
• beta_trainScriptClass_conditional.py adds visualization of latent features