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Continuing our series on combining Keras with TensorFlow eager execution, we show how to implement neural style transfer in a straightforward way. Based on this easy-to-adapt example, you can easily perform style transfer on your own images.
Neural style transfer with eager execution and Keras Read More »
Continuing from the recent introduction to bijectors in TensorFlow Probability (TFP), this post brings autoregressivity to the table. Using TFP through the new R package tfprobability, we look at the implementation of masked autoregressive flows (MAF) and put them to use on two different datasets.
Experimenting with autoregressive flows in TensorFlow Probability Read More »
Image captioning is a challenging task at intersection of vision and language. Here, we demonstrate using Keras and eager execution to incorporate an attention mechanism that allows the network to concentrate on image features relevant to the current state of text generation.
Attention-based Image Captioning with Keras Read More »
This post is a first introduction to MCMC modeling with tfprobability, the R interface to TensorFlow Probability (TFP). Our example is a multi-level model describing tadpole mortality, which may be known to the reader from Richard McElreath’s wonderful “Statistical Rethinking”.
Tadpoles on TensorFlow: Hierarchical partial pooling with tfprobability Read More »
Conditional GANs (cGANs) may be used to generate one type of object based on another – e.g., a map based on a photo, or a color video based on black-and-white. Here, we show how to implement the pix2pix approach with Keras and eager execution.
Image-to-image translation with pix2pix Read More »
This post builds on our recent introduction to multi-level modeling with tfprobability, the R wrapper to TensorFlow Probability. We show how to pool not just mean values (“intercepts”), but also relationships (“slopes”), thus enabling models to learn from data in an even broader way. Again, we use an example from Richard McElreath’s “Statistical Rethinking”; the
Embeddings are not just for use in natural language processing. Here we apply embeddings to a common task in collaborative filtering – predicting user ratings – and on our way, strive for a better understanding of what an embedding layer really does.
Collaborative filtering with embeddings Read More »
As of today, there is no mainstream road to obtaining uncertainty estimates from neural networks. All that can be said is that, normally, approaches tend to be Bayesian in spirit, involving some way of putting a prior over model weights. This holds true as well for the method presented in this post: We show how
Adding uncertainty estimates to Keras models with tfprobability Read More »
Advanced applications like generative adversarial networks, neural style transfer, and the attention mechanism ubiquitous in natural language processing used to be not-so-simple to implement with the Keras declarative coding paradigm. Now, with the advent of TensorFlow eager execution, things have changed. This post explores using eager execution with R.
More flexible models with TensorFlow eager execution and Keras Read More »
TensorFlow feature columns provide useful functionality for preprocessing categorical data and chaining transformations, like bucketization or feature crossing. From R, we use them in popular “recipes” style, creating and subsequently refining a feature specification. In this post, we show how using feature specs frees cognitive resources and lets you focus on what you really want
TensorFlow feature columns: Transforming your data recipes-style Read More »
