Hi everyone,
I’m looking for implementations of FID, Inception Score and other GAN evaluation metrics in TF Eager.
The bundled tf.contrib.gan.eval.* methods seem to choke with eager execution enabled.
Any pointers to snippets or repos?
Thanks
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Although I have spent quite a lot of time recently with CNNs for image classification and semantic segmentation, I have realized that to obtain a deep understanding of them, I have to make one on my own from scratch. So, I put down PyTorch, my go-to framework, and created an implementation using NumPy only 🙂
The result can be found here: https://github.com/cosmic-cortex/neural-networks-from-scratch
Basically, it is a mini deep learning framework, so one can easily experiment with different architectures. Currently, the following components are supported.
Layers:
Loss functions:
Activation functions:
There are two examples as well, a simple multilayer perceptron and a basic CNN on MNIST classification, but custom datasets are supported as well, if you would like to experiment on your own data.
I have to say, I have really enjoyed this ride! It was extremely instructional, moreover I have discovered several mindblowing details, for instance that the gradient for convolution is a transpose convolution operator 🙂 Truly recommended for everyone in DL/ML to try doing the same. During this venture, the fantastic CS231n course was very helpful, so this is also recommended.
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The inaugural Amazon re:MARS event pairs the best of what’s possible today with perspectives on the future of machine learning, automation, robotics, and space travel. Based on the exclusive MARS event founded by Jeff Bezos, Amazon re:MARS brings together the world of business and technology in a premier thought-leadership event. With more than 100 sessions, business leaders have the opportunity to hear best practices for implementing emerging technology for business value. For developers, re:MARS offers technical breakout sessions and hands-on workshops that dive deep into AI and robotics tools from AWS and Amazon Alexa.
You’ll also hear from leading experts across science, academia, and business. Speakers such as Jeff Bezos, founder and CEO of Amazon; Andrew Ng, founder and CEO of deeplearning.ai and Landing AI; Robert Downey Jr, actor and producer; and Colin Angle, chairman, CEO and founder of iRobot, will share the latest research and scientific advancements, industry innovations, and their perspectives on how these domains will evolve.
Register today for Amazon re:MARS and visit the session catalog for the latest lineup! There’s a lot in the works. Here’s a taste of the breakout topics and technical content for beginners and advanced technical builders.
Precision medicine for healthier lives
Keith Bigelow, GM of Analytics, GE Healthcare
GE Healthcare has developed machine learning models using Amazon SageMaker to track and predict brain development in a growing fetus. Powered by these models, the new offering SonoCNS drives the placement of the probe to evaluate congenital and neurological issues in the fetus, for example, to accurately measure and understand brain volume growth. Using Amazon SageMaker for machine learning, GE Healthcare’s operators can quickly detect abnormalities, helping to save babies’ lives and give parents peace of mind. For hospitals, this also translates to improved productivity, efficiency, and accuracy.
Intelligent identity and access management
Paul Hurlocker, Vice President, Center for Machine Learning, Capital One
As one of the largest banks in the U.S., Capital One prioritizes a responsible and well-managed data environment and ecosystem. To meet these needs, Capital One has combined machine learning and native AWS graph capabilities to build a platform that proactively informs access levels for individual associates and teams. The platform results in a faster, enhanced on-boarding process, workflow, and productivity for associates, and helps mitigate risk through proactive management of privileges and licenses.
A hype-free and cutting-edge discussion on autonomous driving
Matthew Johnson-Roberson, Associate Professor, University of Michigan
How close are we to fully autonomous vehicles? What would happen if we put the current technology on the road today? What are the problems that still need to be solved? This session will cover the latest advances in self-driving cars without the marketing, providing a true picture of how far we are from never touching a steering wheel again.
How TED uses AI to spread ideas farther and faster
Jenny Zurawell, Director, & Helena Batt, Deputy Director, TED Translators
TED Talks are a powerful way to share ideas and spark dialogue. To make TED content accessible, volunteer translators need to subtitle more than 300,000 minutes of video this year alone. See how TED leverages Amazon Transcribe and Amazon Translate to speed up the creation of crowdsourced subtitles, expand the online reach of ideas, and transform subtitle production in media.
From seed to store: Using AI to optimize the indoor farms of the future
Irving Fain, Co-founder and CEO, Bowery Farms
For the last 10,000 years, large-scale agriculture has lived outdoors, optimized to withstand unpredictable environmental conditions and long supply chains. But what possibilities do you unlock when you can control every single environmental factor, from the light intensity to nutrient mix to air flow? In this talk, learn how Bowery Farms uses machine learning and computer vision to optimize indoor vertical farms and scale agricultural production to create higher yielding, better tasting, safer, and more sustainable locally-grown produce in cities around the world.
Futuring the farm to improve crop health
Peri Subrahmanya, IoT Product Manager, & Craig Williams, Principal Solution Architect, Bayer Crop Science
One third of all food produced globally is lost or wasted before it is consumed, according to the Food and Agriculture Organization of the United Nations (FAO). This equals a loss of $750 billion annually. With AWS IoT, Bayer Crop Science can prevent process loss in real time and use real-time data collection and analysis for its global seed business, collecting an average of one million traits per day during planting or harvest season.
AI, spatial understanding, robots, and the smart home
Chris Jones, Chief Technology Officer, iRobot
Consumers increasingly expect connected products in their home to deliver easy-to-use and personalized experiences tailored to their home and activity. To deliver such a personalized experience, the smart home needs to intelligently coordinate diverse connected devices located throughout the home. This talk will focus on how robots operating in homes today are ideally positioned to enable this intelligence by providing a constantly updated understanding of the physical layout of the home and the locations of each connected device within the space.
Predicting weather to save energy costs
Andrew Stypa, Lead AI/ML Business Analyst, & Richard Scott, Global Marketing Director, Kinect Energy Group
Learn how Kinect Energy Group uses advanced machine learning capabilities to predict electric spot prices for regional power markets using the Amazon SageMaker DeepAR time-series forecasting model, incorporating historical pricing and weather data to drive the machine learning models. Improved price predictions assist with increased trading volumes for forward pricing contracts.
Creating the intelligent asset: Fusing IoT, robotics, and AI
Jason Crusan, Vice President of Technology, Woodside Energy
What if you could learn more about your facility from your tablet than by walking around it yourself? Through 4D interactive virtual worlds, Woodside’s “Intelligent Asset” offers an immersive experience in which operators can explore their facility remotely in real time. Learn how Woodside, the leading energy provider in Australia, combined the latest AWS services including Amazon Kinesis Video Streams, Amazon SageMaker, AWS RoboMaker, and AWS IoT.
Distributed AI: Alexa living on the edge
Ariya Rastrow, Principal Applied Scientist, Alexa Speech
Distributed edge learning, which leverages on-device computation for training models and centrally aggregated anonymized updates, is a promising new approach capable of achieving customer-level personalization at scale while addressing privacy and trust concerns. Practitioners, employers, and users of AI should understand this new edge-first paradigm and how it will impact the discipline in the near future.
Applying space-based data and machine learning to the UN’s sustainability goals
Dr. Shay Har-Noy, Vice President, Maxar Technologies
The United Nations has outlined 17 Sustainable Development Goals that address global challenges such as poverty, hunger, and health. While the goals are focused on life on Earth, space-based data and machine learning are yielding insights. Learn how a combination of analytics and satellite imagery are helping solve pressing problems.
Enabling sustainable human life in space using AI
Dr. Natalie Rens, CEO & Founder, Astreia
Human settlement of space will pose one of the grandest challenges in history. We imagine entire communities living on the moon and beyond without depending on constant supervision or support from Earth. We’ll discuss the challenges for sustainable life in space, and our plan to use artificial intelligence to ensure the safety and wellbeing of our first space settlers.
From business intelligence to artificial intelligence
Elizabeth Gonzalez, Business Intelligence and Advanced Analytics Leader, INVISTA
INVISTA is a manufacturer of chemicals, polymers, fabrics, and fibers and delivers products and brands incorporated into your clothing, your car, and even your carpet. Join this session to learn about INVISTA’s transformative journey from BI to AI, where they will share their experience empowering data science by adjusting talent and processes and building a modern analytics platform. Experimentation with change management, project management, model maintenance, and development lifecycles has helped drive profitable innovations.
When SpongeBob met Alexa
Zach Johnson, Founder and CEO Xandra, Tim Adams VP, Emerging Products Lab, Viacom
Viacom and Xandra are collaborating to push the limits of voice design with a focus on exceptional user experience. Nickelodeon’s SpongeBob Challenge is one of the highest-rated Alexa skills for kids. Learn how to build delight and fun into an Alexa skill through conversation design, rich soundscapes, advanced game mechanics, and analytics.
Amazon Go: A technology deep dive
Ameet Vaswani, Senior Manager, Software Development, & Gerard Medioni Director, Research, Amazon Go
This technical session will outline the core technologies behind the custom-built Just Walk Out technology for Amazon Go. Learn about the algorithmic challenges in building a highly accurate customer-facing application using deep learning and computer vision, and the technical details of the high throughput services for Amazon Go that transfer gigabytes of video from stores to cloud systems.
Mitigate bias in machine learning models
Stefano Soatto, Director of Applied Science, AWS
Using real-world examples, this session will explore how to understand, measure, and systematically mitigate bias in machine learning models. Understanding these principles is an important part of building a machine learning strategy. This session will cover both the business and technical considerations.
Realizing nature’s secrets to make bug-like robots
Kaushik Jayaram, Postdoc Scholar, Harvard University
This session will take a closer look at the incredible bodies of cockroaches, geckos, and other small animals to examine what it can teach robotics engineers. The session will also outline the latest developments in the field of microrobotics, real-world applications of these robots, and hint at how close (or far) we are from realizing predictions from science fiction.
A chance encounter, sushi, robots, and the environment
Dr. Erika Angle, Co-Founder, Director of Education, Ixcela
Robots can help save our fragile planet. This session discusses the importance of leveraging robotic technology to save our oceans, detailing efforts underway to create an affordable, unmanned undersea robot designed to dive 1000 feet deep and control the lionfish population. Intended for use by fisherman, tourists and environmentalists, the RSE Guardian robot will address a serious environmental problem by creating an economically scalable solution for catching lionfish, establishing a new food source, and inspiring future generations in the process.
The open-source bionic leg: Constructing and controlling a prosthesis driven by AI
Elliott J. Rouse, Assistant Professor, Mechanical Engineering, University of Michigan
For decades, sci-fi movies have shown the promise of life with bionic limbs, but they are nowhere to be seen in today’s society. We have created an open-source bionic leg to help transform these robots from fiction to reality. This talk will focus on innovations in our design approach and showcase a leading AI-based control strategy for wearable robots. Finally, we’ll demo our open-source bionic leg in action with a participant on stage.
Solving Earth’s biggest problems with a cloud in space
Yvonne Hodge, Vice President of IT, Lockheed Martin Space
Can a cloud in space impact the world’s poverty? Are there ways to make agriculture more efficient? Can internet connectivity for the world change how the world lives? Join this interactive discussion as we consider new approaches to solving Earth’s problems including how a cloud in space could positively impact our lives using space data.
Where will the road to space take you?
Patrick Zeitouni, Head of Advanced Development Programs, Blue Origin
This year, Blue Origin will send its first astronauts to space on its New Shepard rocket. Democratization of space is key to the company’s long-term mission to enable a future where millions of people are living and working in space, moving heavy industry off Earth to protect and preserve the planet for generations to come. To achieve this, the cost of access to space must be lowered, which is why Blue Origin is focusing on the development of operationally reusable rockets to send more humans to space than ever before. Join Patrick Zeitouni, the head of Advanced Development Programs for Blue Origin, on the journey to that future. Hear about operational reuse at work and the important part the Moon plays in humanity realizing this bright future.
Get started with machine learning using AWS DeepRacer
Ever wondered what it takes to create an autonomous race car? Come join us for this half-day workshop, and you’ll get hands-on experience with reinforcement learning. Developers with no prior machine learning experience will learn new skills and apply their knowledge in a fun and exciting way. You’ll join a pit crew where you will build and train machine learning models that you can then try out with our AWS DeepRacer autonomous race cars! Please bring your laptop and start your engines, the race is on!
Practical machine learning with Amazon SageMaker
Until recently, developing machine learning models took considerable time, effort, and expertise. In this workshop, you’ll learn a simple end-to-end approach to machine learning, from how to select the right algorithms and models for your business needs, how to prepare your data, then how to build, train, and deploy optimized models. Upon completion of this full-day workshop, you’ll have learned the latest machine learning concepts such as reinforcement learning and deep learning by using Amazon SageMaker for predictive insights.
re:Vegas Blackjack
In this session, you’ll use computer vision and machine learning to help your team win the re:MARS Blackjack Challenge. During this half-day course, you’ll form teams to build and train a neural network for computer vision using Amazon SageMaker, and develop an algorithm to make decisions that give your team the best chance to win. The team with the highest simulated earnings will win the re:MARS Blackjack Challenge and a coveted patch commemorating their experience.
Get started with robotics and AI
Teach a robot how to find a needle in a haystack. In this workshop, you’ll learn how to develop a robot that can roam around a room and identify objects it encounters, searching for a specific type of item. You will get hands-on with AWS RoboMaker, and learn how to connect robots to a huge variety of other AWS services, like Amazon Rekognition and Amazon Kinesis. Upon completion, you’ll have trained a robot to find what you’re looking for in a pile of irrelevant data.
Voice control for any “thing”
From microwaves to cars, we are headed towards a future surrounded by devices that can communicate with the world around them. In this hands-on session, you will learn how to add custom voice control to your connected devices with Alexa. Leave your laptop behind—bring your big ideas, and we’ll supply the hardware. You’ll create an Alexa built-in prototype, AWS IoT “thing,” and your own Alexa skill—all on a Raspberry Pi. You’ll walk out with your own voice-enabled prototype that interfaces with whatever inputs and outputs you can imagine.
Building the Starship Enterprise computer today
Nearly every science fiction story has shown us that voice interfaces are the future. This workshop will show you how to make that science fiction a reality. Bring your laptop, because this hands-on session will teach you the advanced topics required for creating compelling voice interfaces. You will learn how to build Alexa skills, how to design conversational experiences, and how to your brand and monetize your best content.
Cynthya Peranandam is a Principal Marketing Manager 
for AWS artificial intelligence solutions, helping customers use deep learning to provide business value. In her spare time she likes to run and listen to music.
![[P] TensorFlow 2.0 Implementation of Yolo V3 Object Detection Network](https://b.thumbs.redditmedia.com/orSBX0D4OPlhISI4Nxqoi3w40KuCeHhJvP0-Oi2t8QU.jpg "[P] TensorFlow 2.0 Implementation of Yolo V3 Object Detection Network") |
Hey reddit r/ml, I am sharing my implementation of YoloV3 in TensorFlow 2.0 alpha https://github.com/zzh8829/yolov3-tf2 There is a lot of buzz around TensorFlow 2.0 with tons of blog posts and tutorial. But I haven’t found a complete example that uses all the latest features and best practices brought by TF2. This project is created with the goal of being clean, efficient and complete with zero legacy debts. Some of the key features include:
The YoloV3 implementation is mostly referenced from the origin paper, original darknet with inspirations from many existing code written in PyTorch, Keras and TF1 (I credited them at the end of the README). I tried to fixed all the inconsistency, incompleteness and minor errors existing in other repos here. The project works with both YoloV3 and YoloV3-Tiny and is compatible with pre-trained darknet weights. Example of detection output: This project has been quite a great learning experience for me. After working with TF1 and then Keras and then PyTorch, coming back to TensorFlow 2.0 feels very refreshing and enjoyable. TF2 will definitely rise and shine in the coming months following the official GA release. submitted by /u/zzh8829 |
Hi everyone,
I’ve developed a tool to simplify training of deep learning models on AWS: https://github.com/apls777/spotty. My goal was to make training on AWS GPU instances as simple as training on a local computer. Spotty automatically manages all necessary AWS resources (AMIs, volumes, snapshots, SSH keys), runs Spot Instances to save up to 70% of the costs and uses tmux to easily detach remote processes from their SSH sessions.
To train the model (and make it trainable by everyone with a couple of commands), you just need to create 1 configuration file, where you describe a Docker container and AWS instance parameters.
Then the workflow is super-simple:
Here is an article on how to train a model using Spotty with a real-life example: https://towardsdatascience.com/how-to-train-deep-learning-models-on-aws-spot-instances-using-spotty-8d9e0543d365.
I really hope you will find this tool useful and will be happy to hear any feedback.
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I am 37 years old and I have always been interested in technology in general, mainly related to computing. In 2005/2006 I was studying Python in a self-taught mode with 23/24 years old.
Due to the situations that life imposes, I do not work with technology currently (work in the judiciary of Brazil), although I have never stopped reading and inform myself at least a little at all times.
Last year I bought a collection of books by various authors about Python and artificial intelligence (neural networks, deep learning, genetic algorithms, etc.), but I have not started yet, but I’ll probably start from the middle of the year onwards.
I believe that in order to study AI and feel motivated to learn we must have some objective in mind, to study by studying, just out of curiosity, I think that in most cases it can lead to disinterest over time, especially if the subject becomes increasingly complex .
In this way, I have an area of interest in applying any knowledge of AI: Blender 3D. Yes, free software that allows 3D modeling, animation, etc. of Blender foundation. I have been practicing Blender since 2009 so I know a lot about the tool and look forward to the release of version 2.8 that will happen in July.
I imagine some kind of application where you talk about objects, for example, AI understands what you want and produces that object, for example, a dog, after studying deep dog images and comparisons with previously made 3D models.
Telling this story, the big question is, since being around 40 years old, would it be a waste of time to study the subject and focus on other things, letting the new generations create things?
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Taking a good group selfie can be tricky—you need to hover your finger above the shutter, keep everyone’s faces in the frame, look at the camera, make good expressions, try not to shake the camera and hope no one blinks when you finally press the shutter! After building the technology behind automatic photography with Google Clips, we asked ourselves: can we bring some of the magic of this automatic picture experience to the Pixel phone?
With Photobooth, a new shutter-free mode in the Pixel 3 Camera app, it’s now easier to shoot selfies—solo, couples, or even groups—that capture you at your best. Once you enter Photobooth mode and click the shutter button, it will automatically take a photo when the camera is steady and it sees that the subjects have good expressions with their eyes open. And in the newest release of Pixel Camera, we’ve added kiss detection to Photobooth! Kiss a loved one, and the camera will automatically capture it.
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| Photobooth automatically captures group shots, when everyone in the photo looks their best. |
Photobooth joins Top Shot and Portrait mode in a suite of exciting Pixel camera features that enable you to take the best pictures possible. However, unlike Portrait mode, which takes advantage of specialized hardware in the back-facing camera to provide its most accurate results, Photobooth is optimized for the front-facing camera. To build Photobooth, we had to solve for three challenges: how to identify good content for a wide range of user groups; how to time the shutter to capture the best moment; and how to animate a visual element that helps users understand what Photobooth sees and captures.
Models for Understanding Good Content
In developing Photobooth, a main challenge was to determine when there was good content in either a typical selfie, in which the subjects are all looking at the camera, or in a shot that includes people kissing and not necessarily facing the camera. To accomplish this, Photobooth relies on two distinct models to capture good selfies—a model for facial expressions and a model to detect when people kiss.
We worked with photographers to identify five key expressions that should trigger capture: smiles, tongue-out, kissy/duck face, puffy-cheeks, and surprise. We then trained a neural network to classify these expressions. The kiss detection model used by Photobooth is a variation of the Image Content Model (ICM) trained for Google Clips, fine tuned specifically to focus on kissing. Both of these models use MobileNets in order to run efficiently on-device while continuously processing the images at high frame rate. The outputs of the models are used to evaluate the quality of each frame for the shutter control algorithm.
Shutter Control
Once you click the shutter button in Photobooth mode, a basic quality assessment based on the content score from the models above is performed. This first stage is used as a filter that avoids moments that either contain closed eyes, talking, or motion blur, or fail to detect the facial expressions or kissing actions learned by the models. Photobooth temporally analyzes the expression confidence values to detect their presence in the photo, making it robust to variations in the output of machine learning (ML) models. Once the first stage is successfully passed, each frame is subjected to a more fine-grained analysis, which outputs an overall frame score.
The frame score considers both facial expression quality and the kiss score. As the kiss detection model operates on the entire frame, its output can be used directly as a full-frame score value for kissing. The face expressions model outputs a score for each identified expression. Since a variable number of faces may be present in each frame, Photobooth applies an attention model using the detected expressions to iteratively compute an expression quality representation and weight for each face. The weighting is important, for example, to emphasize the expressions in the foreground, rather than the background. The model then calculates a single, global score for the quality of expressions in the frame.
The final image quality score used for triggering the shutter is computed by a weighted combination of the attention based facial expression score and the kiss score. In order to detect the peak quality, the shutter control algorithm maintains a short buffer of observed frames and only saves a shot if its frame score is higher than the frames that come after it in the buffer. The length of the buffer is short enough to give users a sense of real time feedback.
Intelligence Indicator
Since Photobooth uses the front-facing camera, the user can see and interact with the display while taking a photo. Photobooth mode includes a visual indicator, a bar at the top of the screen that grows in size when photo quality scores increase, to help users understand what the ML algorithms see and capture. The length of the bar is divided into four distinct ranges: (1) no faces clearly seen, (2) faces seen but not paying attention to the camera, (3) faces paying attention but not making key expressions, and (4) faces paying attention with key expressions.
In order to make this indicator more interpretable, we forced the bar into these ranges, which prevented the bar scaling from being too rapid. This resulted in smooth variability of the bar length as the quality score changes and improved the utility. When the indicator bar reaches a length representative of a high quality score, the screen flashes to signify that a photo was captured and saved.
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| Using ML outputs directly as intelligence feedback results in rapid variation (left), whereas specifying explicit ranges creates a smooth signal (right). |
Conclusion
We’re excited by the possibilities of automatic photography on camera phones. As computer vision continues to improve, in the future we may generally trust smart cameras to select a great moment to capture. Photobooth is an example of how we can carve out a useful corner of this space—selfies and group selfies of smiles, funny faces, and kisses—and deliver a fun and useful experience.
Acknowledgments
Photobooth was a collaboration of several teams at Google. Key contributors to the project include: Kojo Acquah, Chris Breithaupt, Chun-Te Chu, Geoff Clark, Laura Culp, Aaron Donsbach, Relja Ivanovic, Pooja Jhunjhunwala, Xuhui Jia, Ting Liu, Arjun Narayanan, Eric Penner, Arushan Raj, Divya Tyam, Raviteja Vemulapalli, Julian Walker, Jun Xie, Li Zhang, Andrey Zhmoginov, Yukun Zhu.
I’ve taught Linux/UNIX/shell scripting at my past few jobs and realized I should record lessons and put them online. This is for everyone who wants/needs to learn Linux on the fly. Hopefully it’s useful.
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