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How NOT to program the TensorFlow Graph

How NOT to program t...

Using TensorFlow from Python is like using Python to program another computer. Some Python statements build your TensorFlow program, some Python statements execute that program, and of course some Python statements aren’t involved with TensorFlow at all. Being thoughtful about the graphs you construct can help you avoid confusion and performance pitfalls. Here are a […]

TensorFlow and Queues

TensorFlow and Queue...

There are many ways to implement queue data structures, and TensorFlow has some of its own. FIFO Queue with a list In Python, a list can implement a first-in first-out (FIFO) queue, with slightly awkward syntax: >>> my_list = [] >>> my_list.insert(0, 'a') >>> my_list.insert(0, 'b') >>> my_list.insert(0, 'c') >>> my_list.pop() 'a' >>> my_list.pop() 'b' […]

Use only what you need from TensorFlow

Use only what you ne...

There isn’t just one decision to use TensorFlow or not use TensorFlow; you have to make decisions about which pieces of TensorFlow you’re going to use. I’ve thought about whether Tensorflow suffers from the second-system effect, and my conclusion is that while TensorFlow has a huge abundance of features, it can’t really be said to […]

Experiment with Dask and TensorFlow

Experiment with Dask...

This post briefly describes potential interactions between Dask and TensorFlow and then goes through a concrete example using them together for distributed training with a moderately complex architecture. This post was written in haste, see disclaimers below. This work was originally at matthewrocklin.com and is supported by Continuum Analytics and the XDATA Program as part of the […]

Image Processing in Python

Image Processing in ...

This was originally posted on the Silicon Valley Data Science blog.  The first step in developing our Caltrain project was creating a proof of concept for the image processing component of the device we used to detect passing trains. We’re big fans of Jupyter Notebooks at SVDS, and so we’ve created a notebook to walk […]

Introduction to Trainspotting

Introduction to Trai...

This was originally posted on the Silicon Valley Data Science blog. At Silicon Valley Data Science, we have a slight obsession with the Caltrain. Our interest stems from the fact that half of our employees rely on the Caltrain to get to work each day. We also want to give back to the community, and […]

Implementing a CNN for Text Classification in Tensorflow

Implementing a CNN f...

The full code is available on Github. In this post we will implement a model similar to Kim Yoon’s Convolutional Neural Networks for Sentence Classification. The model presented in the paper achieves good classification performance across a range of text classification tasks (like Sentiment Analysis) and has since become a standard baseline for new text […]

RNNs in Tensorflow, a Practical Guide and Undocumented Features

RNNs in Tensorflow, ...

In a previous tutorial series I went over some of the theory behind Recurrent Neural Networks (RNNs) and the implementation of a simple RNN from scratch. That’s a useful exercise, but in practice we use libraries like Tensorflow with high-level primitives for dealing with RNNs. With that using an RNN should be as easy as […]

Data Science from Cyberspace #1

Data Science from Cy...

By: Alex Perrier – ODSC data science team contributor Every week we bring you a selection of the best data science articles we found floating around Cyberspace. Writing an R package from scratch In the Python vs R debate one argument in favor of R is the amazing diversity and richness of R packages. In […]