Part of a series of respositories dedicated to the personal learning of different neural network.
Structure of the code:
- data: data folder container
- models: models folder container
- notes: personal notes container
- results: results from the neural network
The initial tutorial followed is the tensorflow alpha 2.0 (tutorial)[https://www.tensorflow.org/alpha/tutorials/generative/cvae] Convolutional Variational Autoencoder, base on the MNIST dataset.
In our VAE example, we use two small ConvNets for the generative and inference network. Since these neural nets are small, we use tf.keras.Sequential to simplify our code. Let x and z denote the observation and latent variable respectively in the following descriptions.
This defines the generative model which takes a latent encoding as input, and outputs the parameters for a conditional distribution of the observation, i.e. p(x|z). Additionally, we use a unit Gaussian prior p(z) for the latent variable.
This defines an approximate posterior distribution q(z|x), which takes as input an observation and outputs a set of parameters for the conditional distribution of the latent representation. In this example, we simply model this distribution as a diagonal Gaussian. In this case, the inference network outputs the mean and log-variance parameters of a factorized Gaussian (log-variance instead of the variance directly is for numerical stability).
During optimization, we can sample from q(z|x) by first sampling from a unit Gaussian, and then multiplying by the standard deviation and adding the mean. This ensures the gradients could pass through the sample to the inference network parameters.
- MNIST dataset