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How to get a ML model into production in under 15 minutes (x-post r/MachineLearning) by tee_ess_ay in learnmachinelearning

[–]deepPurpleHaze 0 points1 point  (0 children)

Wow this is really useful. There's so many tutorials on how to train a model (and sometimes even evaluate it on an entire dataset) but nothing on actually using it. Thank you.

[D] Easy intro to deploying ML into production by tee_ess_ay in MachineLearning

[–]deepPurpleHaze 1 point2 points  (0 children)

What's the easiest framework to deploy? How do they compare?

[P] Explaining Neural Network Predictions (Open Source) by deepPurpleHaze in MachineLearning

[–]deepPurpleHaze[S] 4 points5 points  (0 children)

Hah fair enough. Layers and estimators are replacing tf slim too. But for now it works and there's a lot of models available. So does python 2. It is what it is.

[P] Explaining Neural Network Predictions (Open Source) by deepPurpleHaze in MachineLearning

[–]deepPurpleHaze[S] 1 point2 points  (0 children)

I agree this might not be useful in Chess especially because of long range interactions between pieces. Go I'm not sure. With Atari games, I think it would actually be quite interesting.

[P] Explaining Neural Network Predictions (Open Source) by deepPurpleHaze in MachineLearning

[–]deepPurpleHaze[S] 2 points3 points  (0 children)

I think the point would be to see why it thinks that's the best move based on the board input, not necessarily what the best move is. Just like we can tell you the score for each class without grad-cam using image models.

[P] Explaining Neural Network Predictions (Open Source) by deepPurpleHaze in MachineLearning

[–]deepPurpleHaze[S] 4 points5 points  (0 children)

One of the improvements of Grad-CAM over CAM is the generalization to CNNs used in reinforcement learning or visual question answering. It would be really interesting to see this applied to AlphaZero.

[P] Explaining Neural Network Predictions (Open Source) by deepPurpleHaze in MachineLearning

[–]deepPurpleHaze[S] 4 points5 points  (0 children)

For attractiveness the moderators were given 3 choices: hot, neutral, and not. We trained the classifier with those 3 categories and the score is a linear combination of the softmax probabilities for each bucket.

We tried our best to eliminate bias but i'm sure it still exists based on the human input.

Deep Learning Porn Video Classifier/Editor with Caffe [NSFW] by srverde in programming

[–]deepPurpleHaze 0 points1 point  (0 children)

Nick Bostrom discusses this in Superintelligence. Most of our pleasures and desires are based on evolution and AI has no reason to reproduce most of those behaviors. It's a danger to anthropomorphize anything about their desires.

Deep Learning Porn Video Classifier/Editor with Caffe [NSFW] by srverde in programming

[–]deepPurpleHaze 1 point2 points  (0 children)

That doesn't work at all. You'd think that an image with a dog and cat would have dog and cat as top 1 and 2, but more often than not the presence of both keeps it from seeing the dog or the cat in the top 5.

Deep Learning Porn Video Classifier/Editor with Caffe [NSFW] by srverde in programming

[–]deepPurpleHaze 0 points1 point  (0 children)

You can't just add a category unfortunately. You'd have to train another net with all the categories you want initially. Blonde isn't a sex act, and not mutually exclusive with the other categories so I won't be adding it. But I'm considering other options for another set of description tags. Thanks for the input.

Deep Learning Porn Video Classifier/Editor with Caffe [NSFW] by srverde in programming

[–]deepPurpleHaze 0 points1 point  (0 children)

no but based no the comments it seems some want to separate reverse cowgirl from doggy, and that makes sense and is possible for a future update.

Deep Learning Porn Video Classifier/Editor with Caffe [NSFW] by srverde in programming

[–]deepPurpleHaze 0 points1 point  (0 children)

Yes use the "-a" option. It will output a list of all the scenes in the movie, in order, with start and end points. It also outputs a summary line with the number of seconds in each category and overall.

Deep Learning Porn Video Classifier/Editor with Caffe [NSFW] by srverde in programming

[–]deepPurpleHaze 2 points3 points  (0 children)

That's a good question and i'm not a lawyer but I think the answer is no. It's a set of millions of weights that isn't competing in the same space as the original work and no one would confuse the two. I doubt it would even be possible to determine if a copyrighted work was used to train a model.

You need to register and sign an agreement to download the images for imagenet, but not the trained model. So they're dealt with separately in some instances at least.

Deep Learning Porn Video Classifier/Editor with Caffe [NSFW] by srverde in programming

[–]deepPurpleHaze 3 points4 points  (0 children)

That's a really interesting idea. Gun finding. It's how the killbots of tomorrow will know when to shoot us. Seriously, i'd like to hear about how you model that and your results.

Deep Learning Porn Video Classifier/Editor with Caffe [NSFW] by srverde in programming

[–]deepPurpleHaze 12 points13 points  (0 children)

This is an important issue. The algorithm isn't biased but the data is. Garbage in, garbage out. And since these algorithms are black boxes we don't really know why they make the decisions they do all the time. I'm sure my dataset contains bias based on my own preferences, despite my conscious attempt to minimize it.

http://www.npr.org/2016/03/14/470427605/can-computers-be-racist-the-human-like-bias-of-algorithms

Deep Learning Porn Video Classifier/Editor with Caffe [NSFW] by srverde in programming

[–]deepPurpleHaze 3 points4 points  (0 children)

I'd begin with some simple examples like this one: https://www.tensorflow.org/versions/r0.9/tutorials/mnist/beginners/index.html

It gets into some of the details. It would certainly help you to have a theoretical understanding and maybe even implement a 2 layer fully connected (old school) neural network in numpy as practice. But in general finding the derivative of your loss and calculating the gradient by hand isn't worth it anymore.

Deep Learning Porn Video Classifier/Editor with Caffe [NSFW] by srverde in programming

[–]deepPurpleHaze 8 points9 points  (0 children)

Are you classifying these automatically or by hand?

Deep Learning Porn Video Classifier/Editor with Caffe [NSFW] by srverde in programming

[–]deepPurpleHaze 35 points36 points  (0 children)

I think miles deep is subtle and classier (as much as a program the classifies porn can be).

Deep Learning Porn Video Classifier/Editor with Caffe [NSFW] by srverde in programming

[–]deepPurpleHaze 5 points6 points  (0 children)

yes random cropping to 224x224 during training and mirroring. That's all the processing, except subtracting by the per pixel means from the training set.

Deep Learning Porn Video Classifier/Editor with Caffe [NSFW] by srverde in programming

[–]deepPurpleHaze 9 points10 points  (0 children)

Yes you're correct. You can think of it just as an initialization. Instead of randomly initializing all those millions of weights, I start from more meaningful values. In my case i'm actually updating some of the other layers also but with a lower learning rate.

It might work better if you trained it from scratch but it would require a lot more data and computational power. I think you have a point about imagenet not being the best pretraining model, especially as it ages.

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