Tag: tensorflow

ArchiGAN: Designing buildings with reinforcement learning

I’ve seen some uses of reinforcement learning and generative algorithms for architectural purposes already, like these evolving blueprints for school floorplans. However, this new application called ArchiGAN blew me away!

ArchiGAN (try here) was made by Stanislas Chaillou as a Harvard master’s thesis project. The program functions in three steps:

building footprint massing
program repartition
furniture layout

Generation stack image — Stanislas’ three generation steps

Each of these three steps uses a TensorFlow Pix2Pix GAN-model (Christopher Hesse’s implementation) in the back-end, and their combination makes for a entire apartment building “generation stack” — according to Stanislas — which also allows for user input at each step.

The design of a building can be inferred from the piece of land it stands on. Hence, Stanislas fed his first model using GIS-data (Geographic Information System) from the city of Boston in order to generate typical footprints based on parcel shapes.

Model 1 results image — The inputs and outputs of model I

Stanislas’ second model was responsible for repartition and fenestration (the placement of windows and doors). This GAN took the footprint of the building (the output of model I) as input, along with the position of the entrance door (green square), and the positions of the user-specified windows.

Stanislas used a database of 800+ plans of apartments for training. To visualize the output, rooms are color-coded and walls and fenestration are blackened.

Model II results image — The inputs and outputs of model II

Finally, in the third model, the rooms are filled with appropriate furniture. What training data Stanislas has used here, he did not specify in the original blog.

Model III results image — The inputs and outputs of model III

Now, to put all things together, Stanislas created a great interactive tool you can play with yourself. The original NVIDEA blog contains some great GIFs of the tool being used:

Stanislas’ GAN-models progressively learned to design rooms and realistically position doors and windows. It took about 250 iterations to get some realistic floorplans out of the algorithm. Here’s how an example learning sequence looked like:

Architectural sequence image — Visualization of the training process

Now, Stanislas was not done yet. He also scaled the utilization of GANs to design whole apartment buildings. Here, he chains the models and processes multiple units as single images at each step.

Apartment building generation pipeline image — Generating whole appartment blocks using ArchiGAN

Stanislas did other cool things to improve the flexibility of his ArchiGAN models, about which you can read more in the original blog. Let these visuals entice you to read more:

GAN-enabled building layouts image — ArchiGAN scaled to handle whole appartment blocks and neighborhoods.

I believe a statistical approach to design conception will shape AI’s potential for Architecture. This approach is less deterministic and more holistic in character. Rather than using machines to optimize a set of variables, relying on them to extract significant qualities and mimicking them all along the design process represents a paradigm shift.
Stanislas Chaillou (via)

I am so psyched about these innovative applications of machine learning, so please help me give Stanislas the attention and credit he deserved.

Currently, Stanislas is Data Scientist & Architect at Spacemaker.ai. Read more about him in his NVIDEA developer bio here. He recently published a sequence of articles, laying down the premise of AI’s intersection with Architecture. Read here about the historical background behind this significant evolution, to be followed by AI’s potential for floor plan design, and for architectural style analysis & generation.

Putting R in Production, by Heather Nolis & Mark Sellors

It is often said that R is hard to put into production. Fortunately, there are numerous talks demonstrating the contrary.

Here’s one by Heather Nolis, who productionizes R models at T-Mobile. Her teams even shares open-source version of some of their productionized Tensorflow models on github. Read more about that model here.

There’s another great talk on the RStudio website. In this talk, Mark Sellors discusses some of the misinformation around the idea of what “putting something into production” actually means, and provides some tips on overcoming obstacles.

Cover image via Fotolia.

Papers with Code: State-of-the-Art

OK, this is a really great find!

The website PapersWithCode.com lists all scientific publications of which the codes are open-sourced on GitHub. Moreover, you can sort these papers by the stars they accumulated on Github over the past days.

The authors, @rbstojnic and @rosstaylor90, just made this in their spare time. Thank you, sirs!

Papers with Code allows you to quickly browse state-of-the-art research on GANs and the code behind them, for instance. Alternatively, you can browse for research and code on sentiment analysis or LSTMs.

Tensorflow for R Gallery

Tensorflow is a open-source machine learning (ML) framework. It’s primarily used to build neural networks, and thus very often used to conduct so-called deep learning through multi-layered neural nets.

Although there are other ML frameworks — such as Caffe or Torch — Tensorflow is particularly famous because it was developed by researchers of Google’s Brain Lab. There are widespread debates on which framework is best, nonetheless, Tensorflow does a pretty good job on marketing itself.

Google search engine searches on *Tensorflow* in comparison to searches on *Machine learing* and *Deep learning*

I primarily work in the programming language R, and have written before about how to start with deep learning in R using Keras — an user-friendly API built on top of, among others, Tensorflow. Now, it has become even easier to learn how to implement the power of Tensorflow in R, for RStudio has compiled a gallery of featured posts on Tensorflow implementations in R. It features a variety of applications related to collaborative filtering, image recognition, audio classification, times series forecasting, and fraud detection, all using Keras and TensorFlow. I highly recommend you check it out if you want to learn more about deep learning in R.

Generating Pusheen with AI

Zack Nado wrote the best machine learning application I’ve seen so far: a neural network architecture that generates new Pusheen pictures.

Image result for pusheen — This is an orginal Pusheen picture.

In his blog, Zack describes his generative adversarial network (GAN) , a special type of machine learning architecture where two neural networks try to fool each other. Zack first gave the discriminator network some real Pusheen images, so it gets an idea of what Pusheen looks like. Next, the generator network gets a bunch of random numbers so it can generate completely new (fake) images. These generated images are then fed back into the discriminator, so it knows what generated images look like. Zack repeated this process several hundred thousand times, so he obtained a generator network that’s great at making new Pusheen images which the discriminator (nearly) can’t dinstinguish from the original, real ones. Below is the learning process of the generator network visualized:

ezgif.com-video-to-gif — Samples output by the generator network. It learns distinctive features of “real” Pusheen (e.g., tail, eyes, ears) over time [original]

In the end, the generated images are very much like the real Pusheen. Zack added an interactive module (using Tensorflow.js) to the blog so you can generate some Pusheens yourself. (it didn’t work for me though…) On a final note, Zack wrote the orginal blog both in plain English, for non-experts, and in jargon, for the more experienced data scientists. I highly recommend you read either one of those versions!

Some of the Pusheen’s generated by Zack’s GAN [original]

Machine Learning and AI courses at Google

Google has announced to provide open access to its artificial intelligence and machine learning courses. On their overview page, you will find many educational resources from machine learning experts at Google. They announced to share AI and machine learning lessons, tutorials and hands-on exercises for people at all experience levels. Simply filter through the resources and start learning, building and problem-solving.

For instance, up your game straight away with this 15-hour Machine Learning crash course. Zuri Kemp – who leads Google’s machine learning education program – said that over 18,000 Googlers have already enrolled in the course. Designed by the engineering education team, the courses explores loss functions and gradient descent and teached you to build your own neural network in Tensorflow.