"IndependentBernoulli/", batch_shape=(), event_shape=(3,), dtype=int32 To install TFP together with TensorFlow, simply append tensorflow-probability to the default list of extra packages: 1ī <- tfd $ Independent ( bs, reinterpreted_batch_ndims = 1L ) b ( We’ll regard this post as a “proof on concept” for using TFP with Keras - from R - and plan to follow up with more elaborate examples from the area of semi-supervised representation learning. This time though, we’ll make use of TFP to sample from the prior and approximate posterior distributions. Then, we’ll build a variational autoencoder similar to that in Representation learning with MMD-VAE. We’ll quickly show how to get started with one of the basic building blocks: distributions. Instead, our aim here is to provide a first introduction to TFP, focusing on direct applicability to and interoperability with deep learning. The field of possible applications is vast - and far too diverse to cover as a whole in an introductory blog post. Now imagine all these working seamlessly with the TensorFlow framework - core, Keras, contributed modules - and also, running distributed and on GPU. Probabilistic inference (via MCMC or variational inference).Probabilistic modeling (Edward2 and probabilistic network layers).Distributions and bijectors (bijectors are reversible, composable maps).With the abundance of great libraries, in R, for statistical computing, why would you be interested in TensorFlow Probability ( TFP, for short)? Well - let’s look at a list of its components:
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