## Wasserstein variational autoencoders

Variational auto-encoders (VAEs) are a latent space model. The idea is you have some latent space variable $z \in \mathbb{R}^{k}$ which describes your original variables $x\in\mathbb{R}^d$ in higher dimensional space by a latent model $p(x|z)$. Let’s assume that this distribution is given by a neural network with some parameters $\theta$ so that we assume $$x | z, \theta \sim N(g_\theta(z), 1).$$ Of course in reality, we don’t know $(z, \theta)$, we would like to infer these from the data. [Read More]

## Introduction To Tensorflow Estimator

In this post I am going to introduce tf.estimator library. So first of all, what is this library trying to do? When writing tensorflow code, there is a lot of repeated operations that we need to do: read the data in batches process the data, e.g. convert images to floats run a for loop and take a few gradient descent steps save model weights to disk output metrics to tensorboard The keras library makes this quite a bit easier, but there are times when you might need to use plain old tensorflow (it gets quite hacky to implement some multiple output models and GANs in keras). [Read More]