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Author: torontoai

1000x Faster Data Augmentation

Written on . Posted in Uncategorized.



Effect of Population Based Augmentation applied to images, which differs at different percentages into training.

In this blog post we introduce Population Based Augmentation (PBA), an
algorithm that quickly and efficiently learns a state-of-the-art approach to
augmenting data for neural network training. PBA matches the previous best
result on CIFAR and SVHN but uses one thousand times less
compute, enabling researchers and practitioners to effectively learn
new augmentation policies using a single workstation GPU. You can use PBA
broadly to improve deep learning performance on image recognition tasks.

We discuss the PBA results from our recent paper and then show how
to easily run PBA for yourself on
a new data set in the Tune framework.

Continue reading

[R] Performing Streaming Speech Recognition on LOCAL FILE with IBM Watson

Written on . Posted in Reddit MachineLearning.

For comparison test of the performance of streaming STT from various companies, I had to make sure I input the same audio data each time. Thus, I modified ‘watson-streaming-stt’ (with mic) to work on LOCAL FILE.

In case anyone of you wants to do the same comparison test, I post the link here. I will update codes and README to make it easy to use and understand soon.

Modified Codes

Original Codes

submitted by /u/soominjung
[link] [comments]

AWS DeepRacer League: The June race gets underway, as the first Virtual Circuit champion is crowned!

Written on . Posted in Amazon.

The AWS DeepRacer League is the world’s first global autonomous racing league, open to anyone. Developers of all skill levels can get hands on with machine learning in a fun and exciting way, racing for prizes and glory at 21 events globally and online via the DeepRacer console. The Virtual Circuit launched at the end of April, allowing developers to compete from anywhere in the world via the console – no car or track required – for a chance to top the leaderboard and score points in one of the 6 monthly competitions.

The rubber hits the road for the June race!

On June 3rd the Kumo Torakku challenge opened, and will be open for racing until June 30th, at midnight PST. Inspired by the Suzuka circuit in Japan, this track will help developers of all skill levels put their models to the test and advance their knowledge and practice of machine learning. All you need to do is log into the console, where you will be taken through a few quick and easy steps to get your model up and running and ready to race. With the AWS Free Tier you are covered for up to 10 hours of training (in your first 30 days of usage), so you can enter the AWS DeepRacer League at no cost to you.

Once you have learned the basics you will be able to immerse yourself inside the AWS DeepRacer online simulator and watch your model train, until it is ready for submission to the leaderboard. Will it make it round the hairpin, to get views of Mt Fuji? Will you optimize for speed or direction to get the model through the curves? Can you tune your model to take pole position? Get racing today, and don’t forget, if you compete in multiple online races you will score more points, and increase your chances to be eligible for one of the overall Virtual Circuit prizes!

AWS DeepRacer League is open to all and you don’t need the AWS DeepRacer car or to visit an in-person race for a chance to compete, with the virtual circuit you can participate in the race from the comfort of the console. Start your engines, the June race is on!

Watch a successful full lap of the Kumo Torakku, from the AWS DeepRacer 3D online simulator

The Suzuka circuit and the new Kumo Torakku virtual race track

What’s new in the Kumo Torakku?

Aside from enjoying the scenery, you will now have the ability to train your model at a maximum speed of 8 meters per second. But beware, the Kumo Torakku has tight corners and a car travelling at that speed may not be able to take the turns well. It may take time for your model to converge and training time could increase with more throttle, so you will have to experiment with speed in your reward function to help you to succeed. Get started today for your chance to win your expenses paid ticket and join the best of the best at re:Invent 2019.

Cheers to the London Loop winner!

And if that doesn’t inspire you, here’s a quick spotlight and celebration of the May race winner. After a month long race, the London Loop closed on Friday May 31st and the first champion of the virtual tournament was crowned. Karl, who works for the National Australia Bank (NAB) took home the top prize and will now be heading to re:Invent 2019 to join the race for the Championship Cup. At NAB, teams are encouraged to experiment with new concepts and technologies, and the team there have been on their machine learning journey with DeepRacer since it launched at re:Invent 2018. They have created their own DeepRacer community, hosted their own competition, and even saw a team member take third place at the AWS Summit in Sydney.

Karl was joined on the London Loop podium by his teammate Paul, who came third in the May race. Paul recently posted about their experience with the AWS DeepRacer League and you can check it out here. Also be on the lookout for part two where they will share more tips on how to compete to win. Karl, Paul and the rest of the NAB team made a combined 533 attempts to conquer the London Loop challenge. They worked hard on their models, tuning them over time and ultimately clinching the win, and they even said “the virtual league was much more fun than the real race!”

Congratulations to the team and here’s to more AWS DeepRacer success!

About the Author

Alexandra Bush is a Senior Product Marketing Manager for AWS AI. She is passionate about how technology impacts the world around us and enjoys being able to help make it accessible to all. Out of the office she loves to run, travel and stay active in the outdoors with family and friends.

 

 

 

[D] Having trouble with Deep Q-learning on the OpenAI Gym Lunar Lander.

Written on . Posted in Reddit MachineLearning.

A few months ago I spent some time trying to learn deep reinforcement learning, and became obsessed with the OpenAI Gym Lunar Lander environment. I ended up doing KNN on memory (as in, “memory replay”), and I got some intelligent behavior out of the lander, but it was far from perfect (and yes, I know KNN is not “deep learning”, but I used what I understood). Recently I took another shot at it using deep Q-learning with neural networks, but I’m having even less success than before.

I wanted to ask about, what I believe to be, a major contributor to my difficulties.

In Deep Q-Learning you have a neural networks that will be used to approximate Q(s, a) which is the value of action a in state s, or rather, the value you can expect to obtain in the long run after taking action a in state s. We can also say that the value of a state is V(s) = Q(s, maximizing_a), meaning the value of a state is the value of choosing the optimal action in that state.

As far as I understand, deep Q-learning revolves around the Q(s, a) = r + V(s') equation, meaning the long term expected value of a state and action are simply the immediate reward, plus the expected value of the next state. In deep Q-learning you basically turn this equation into training data and train your neural network on it.

My problem is that Q predicts action values like this: Float32[-32.5629, -32.8037, -32.6016, -32.5938] (There are 4 possible actions at each step in the lunar lander environment.)

Q seems to understand [correctly] that it really doesn’t matter much what I do for a single step in the lander environment. A single action barely changes the situation at all, so the expected values of all actions are very very close. I don’t think my neural network is able to make such a subtle distinction between which action is best, because any single action has a very very small effect. This is especially troublesome because, again, Q is correct, there really isn’t much difference between the actions at a single step, so I can’t just “fix” Q, because it’s already working, but it will never be 100% correct since it’s just a function approximator.

So what do I do? Do I throw more parameters at it? Do I train it another way? Any suggestions?

submitted by /u/Buttons840
[link] [comments]

[P] Creating an AI algorithm that can play Battleship using neural networks (Reupload)

Written on . Posted in Reddit MachineLearning.

Hi,

​

I am a student who is researching the topic of neural networks. I am posting a Google Forms survey containing 12 videos, each containing a game being played by the neural network versus the human player in Battleship, and require your help in this project.

​

Please observe the videos and answer which board (A or B) is being played by the AI for each of the 12 games.

​

Link: https://docs.google.com/forms/d/e/1FAIpQLSfvQWt2RIu1mfq62OP9X_ieM8dlwoPsuBu-exgW4rPrGdyWmg/viewform?usp=sf_link

submitted by /u/I_am_roundar
[link] [comments]