Tensorflow Tutorial Uses Python

Around the Hackaday secret bunker, we’ve been talking quite a bit about machine learning and neural networks. There’s been a lot of renewed interest in the topic recently because of the success of TensorFlow. If you are adept at Python and remember your high school algebra, you might enjoy [Oliver Holloway’s] tutorial on getting started with Tensorflow in Python.

[Oliver] gives links on how to do the setup with notes on Python versions. Then he shows some basic setup operations. From there, he has the software “learn” how to classify random points that either fall into a circle or don’t. Granted, this is easy enough to do with traditional programming, so it isn’t a great practical example, but it is illustrative for learning purposes.

Given that it is easy to algorithmically decide which points are in the circle and which are not, it is simple to develop training data. It is also easy to look at the result and see how close it is to the actual circle. You’ll see that it takes a lot of slow learning before the result space looks like a circle and not a triangle or some other odd shape.

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Smarter Phones In Your Hacks With TensorFlow Lite

One way to run a compute-intensive neural network on a hack has been to put a decent laptop onboard. But wouldn’t it be great if you could go smaller and cheaper by using a phone instead? If your neural network was written using Google’s TensorFlow framework then you’ve had the option of using TensorFlow Mobile, but it doesn’t use any of the phone’s accelerated hardware, and so it might not have been fast enough.

TensorFlow Lite architecture
TensorFlow Lite architecture

Google has just released a new solution, the developer preview of TensofFlow Lite for iOS and Android and announced plans to support Raspberry Pi 3. On Android, the bottom layer is the Android Neural Networks API which makes use of the phone’s DSP, GPU and/or any other specialized hardware to speed up computations. Failing that, it falls back on the CPU.

Currently, fewer operators are supported than with TensforFlor Mobile, but more will be added. (Most of what you do in TensorFlow is done through operators, or ops. See our introduction to TensorFlow article if you need a refresher on how TensorFlow works.) The Lite version is intended to be the successor to Mobile. As with Mobile, you’d only do inference on the device. That means you’d train the neural network elsewhere, perhaps on a GPU-rich desktop or on a GPU farm over the network, and then make use of the trained network on your device.

What are we envisioning here? How about replacing the MacBook Pro on the self-driving RC cars we’ve talked about with a much smaller, lighter and less power-hungry Android phone? The phone even has a camera and an IMU built-in, though you’d need a way to talk to the rest of the hardware in lieu of GPIO.

You can try out TensorFlow Lite fairly easily by going to their GitHub and downloading a pre-built binary. We suspect that’s what was done to produce the first of the demonstration videos below.

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Prototyping, Making A Board For, And Coding An ARM Neural Net Robot

[Sean Hodgins]’s calls his three-part video series an Arduino Neural Network Robot but we’d rather call it an enjoyable series on prototyping, designing a board with surface mount parts, assembling it, and oh yeah, putting a neural network on it, all the while offering plenty of useful tips.

In part one, prototype and design, he starts us out with a prototype using a breadboard. The final robot isn’t on an Arduino, but instead is on a custom-made board built around an ARM Cortex-M0+ processor. However, for the prototype, he uses a SparkFun SAM21 Arduino-sized board, a Pololu DRV8835 dual motor driver board, four photoresistors, two motors, a battery, and sundry other parts.

Once he’s proven the prototype works, he creates the schematic for his custom board. Rather than start from scratch, he goes to SparkFun’s and Pololu’s websites for the schematics of their boards and incorporates those into his design. From there he talks about how and why he starts out in a CAD program, then moves on to KiCad where he talks about his approach to layout.

Part two is about soldering and assembly, from how he sorts the components while still in their shipping packages, to tips on doing the reflow in a toaster oven, and fixing bridges and parts that aren’t on all their pads, including the microprocessor.

In Part three he writes the code. The robot’s objective is simple, run away from the light. He first tests the photoresistors without the motors and then writes a procedural program to make the robot afraid of the light, this time with the motors. Finally, he writes the neural network code, but not before first giving a decent explanation of how the neural network works. He admits that you don’t really need a neural network to make the robot run away from the light. But from his comparisons of the robot running using the procedural approach and then the neural network approach, we think the neural network one responds better to what would be the in-between cases for the procedural approach. Admittedly, it could be that a better procedural version could be written, but having the neural network saved him the trouble and he’s shown us a lot that can be reused from the effort.

In case you want to replicate this, [Sean]’s provided a GitHub page with BOM, code and so on. Check out all three parts below, or watch just the parts that interest you.

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Neural Network Really Ties The Room Together

If there’s one thing that Hollywood knows about hackers, it’s that they absolutely love data visualizations. Sometimes it’s projected on a big wall (Hackers, WarGames), other times it’s gibberish until the plot says otherwise (Sneakers, The Matrix). But no matter what, it has to look cool. No hacker worth his or her salt can possibly work unless they’ve got an evolving Venn diagram or spectral waterfall running somewhere in the background.

Inspired by Hollywood portrayals, specifically one featured in Avengers: Age of Ultron, [Zack Akil] decided it was time to secure his place in the pantheon of hacker wall visualizations. But not content to just show meaningless nonsense on his wall, he set out to create something that was at least showing actual data.

[Zack] created a neural network to work through multi-label classification data in Python using the scikit-learn machine learning suite. The code takes the values from the neutral network training algorithm and converts them to RGB colors by way of an Arduino. Each “node” in the neutral network is 3D printed in translucent filament, and fitted with an RGB LED module. These modules are then connected to each other via side-glow fiber optic tubes, so that the colors within the tubes are mixed depending on the colors of the nodes they are attached to. This allows for a very organic “growing” effect, as colors move through the network node-by-node.

In the end this particular visualization doesn’t really mean anything; the data it’s working on only exists for the purposes of the visualization itself. But [Zack] succeeded in creating a practical visualization of machine learning, and if you’re the kind of person who needs to keep tabs on learning algorithms, some variation of this design may be just what you’re looking for.

If AI isn’t your thing but you still want a wall of RGB LEDs, maybe you can use this phased array antenna visualizer instead. If you’re really hip, maybe you’ll go the analog route and put a big gauge on the wall.

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Artificial Intelligence at the Top of a Professional Sport

The lights dim and the music swells as an elite competitor in a silk robe passes through a cheering crowd to take the ring. It’s a blueprint familiar to boxing, only this pugilist won’t be throwing punches.

OpenAI created an AI bot that has beaten the best players in the world at this year’s International championship. The International is an esports competition held annually for Dota 2, one of the most competitive multiplayer online battle arena (MOBA) games.

Each match of the International consists of two 5-player teams competing against each other for 35-45 minutes. In layman’s terms, it is an online version of capture the flag. While the premise may sound simple, it is actually one of the most complicated and detailed competitive games out there. The top teams are required to practice together daily, but this level of play is nothing new to them. To reach a professional level, individual players would practice obscenely late, go to sleep, and then repeat the process. For years. So how long did the AI bot have to prepare for this competition compared to these seasoned pros? A couple of months.

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Table-Top Self Driving With The Pi Zero

Self-driving technologies are a hot button topic right now, as major companies scramble to be the first to market with more capable autonomous vehicles. There’s a high barrier to entry at the top of the game, but that doesn’t mean you can’t tinker at home. [Richard Crowder] has been building a self-driving car at home with the Raspberry Pi Zero.

The self-driving model is trained by first learning from the human driver.

[Richard]’s project is based on the EOgma Neo machine learning library. Using a type of machine learning known as Sparse Predictive Hierarchies, or SPH, the algorithm is first trained with user input. [Richard] trained the model by driving it around a small track. The algorithm takes into account the steering and throttle inputs from the human driver and also monitors the feed from the Raspberry Pi camera. After training the model for a few laps, the car is then ready to drive itself.

Fundamentally, this is working on a much simpler level than a full-sized self-driving car. As the video indicates, the steering angle is predicted based on the grayscale pixel data from the camera feed. The track is very simple and the contrast of the walls to the driving surface makes it easier for the machine learning algorithm to figure out where it should be going. Watching the video feed reminds us of simple line-following robots of years past; this project achieves a similar effect in a completely different way. As it stands, it’s a great learning project on how to work with machine learning systems.

[Richard]’s write-up includes instructions on how to replicate the build, which is great if you’re just starting out with machine learning projects. What’s impressive is that this build achieves what it does with only the horsepower of the minute Raspberry Pi Zero, and putting it all in a package of just 102 grams. We’ve seen similar builds before that rely on much more horsepower – in processing and propulsion.

We Should Stop Here, It’s Bat Country!

[Roland Meertens] has a bat detector, or rather, he has a device that can record ultrasound – the type of sound that bats use to echolocate. What he wants is a bat detector. When he discovered bats living behind his house, he set to work creating a program that would use his recorder to detect when bats were around.

[Roland]’s workflow consists of breaking up a recording from his backyard into one second clips, loading them in to a Python program and running some machine learning code to determine whether the clip is a recording of a bat or not and using this to determine the number of bats flying around. He uses several Python libraries to do this including Tensorflow and LibROSA.

The Python code breaks each one second clip into twenty-two parts. For each part, he determines the max, min, mean, standard deviation, and max-min of the sample – if multiple parts of the signal have certain features (such as a high standard deviation), then the software has detected a bat call. Armed with this, [Roland] turned his head to the machine learning so that he could offload the work of detecting the bats. Again, he turned to Python and the Keras library.

With a 95% success rate, [Roland] now has a bat detector! One that works pretty well, too. For more on detecting bats and machine learning, check out the bat detector in this list of ultrasonic projects and check out this IDE for working with Tensorflow and machine learning.