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I'm taking a course on Deep Learning right now, because my boss asked me to. It's Udacity course number UD730. The first homework assignment was image recognit

I'm taking a course on Deep Learning right now, because my boss asked me to. It's Udacity course number UD730. The first homework assignment was image recognition on notMNIST. That dataset has been used thousands of times before, so I decided to make it more interesting! I got data for 1500 Chinese fonts, so I could make a better OCR for Chinese characters. I put that into the example, made the "pickle" files, trained the model, got a 90% prediction-test result value... but now what? How do I take that model and make a real image OCR that can take a photo from my phone and analyse it? I can write scripts to get data, and follow the steps in a course to make a model, but unless I have a useful output, I don't know how I'm going to apply this to my other programs. If you're offering "end to end", then documentation and examples of some kind of API would be great. (e.g. send a PNG to this REST service, and get the prediction back as text).

deepnotderp 9y ago
Use an API like TensorFlow serving (that uses gRPC actually iirc) or deep detect.
spullara 9y ago
It is pretty easy to deploy these models on Algorithmia.
NegatioN 9y ago
> How do I take that model and make a real image OCR that can take a photo from my phone and analyse it? I assume you're talking about some kind of endpoint to submit the image to, and offload the computation entirely. However I just wanted to clarify for people that are new to this, that running the neural net on a mobile device usually requires entirely different models, because of the memory/compute limitations. It's a rapidly developing field, but it is not quite as accessible as developing for desktops yet. I do believe Tensorflow allows you to run models on mobile now though [0] [0]: https://www.tensorflow.org/mobile/ https://www.tensorflow.org/mobile/
vikiomega9 9y ago
It might be fairly simple to write up a REST API with flask and expose your models?
peterburkimsher 9y ago
Thank you for the suggestions. All of those look like servers for the Tensorflow model (i.e. take what I already have on my laptop, and put it in the cloud). Real life doesn't give me folders of training data to sort. It gives me photos and videos. Applying a systems engineering approach, I think I need to: 1. Break the picture down into thousands of small little squares. 2. Look for a character inside each picture. 3. If a character is found, add the character to the output text. That brings new questions. How big should each square tile be? What if the characters are not perfectly flat? How do I avoid getting duplicates from tiles that are next to each other? Whether the tile->text conversion is done with a simple classifier or "deep learning model", the bulk of the work is done using non-Tensorflow programming. I don't know about open-source projects I could build on and retrain using my data. So the most likely result is that I'll show off the fancy Tensorflow data as a "portfolio project" and archive it, never to be used in production.

deepnotderp 9y ago

Use an API like TensorFlow serving (that uses gRPC actually iirc) or deep detect.

spullara 9y ago

It is pretty easy to deploy these models on Algorithmia.

NegatioN 9y ago

> How do I take that model and make a real image OCR that can take a photo from my phone and analyse it? I assume you're talking about some kind of endpoint to submit the image to, and offload the computation entirely. However I just wanted to clarify for people that are new to this, that running the neural net on a mobile device usually requires entirely different models, because of the memory/compute limitations. It's a rapidly developing field, but it is not quite as accessible as developing for desktops yet. I do believe Tensorflow allows you to run models on mobile now though [0] [0]: https://www.tensorflow.org/mobile/ https://www.tensorflow.org/mobile/

vikiomega9 9y ago

It might be fairly simple to write up a REST API with flask and expose your models?

peterburkimsher 9y ago

Thank you for the suggestions. All of those look like servers for the Tensorflow model (i.e. take what I already have on my laptop, and put it in the cloud). Real life doesn't give me folders of training data to sort. It gives me photos and videos. Applying a systems engineering approach, I think I need to: 1. Break the picture down into thousands of small little squares. 2. Look for a character inside each picture. 3. If a character is found, add the character to the output text. That brings new questions. How big should each square tile be? What if the characters are not perfectly flat? How do I avoid getting duplicates from tiles that are next to each other? Whether the tile->text conversion is done with a simple classifier or "deep learning model", the bulk of the work is done using non-Tensorflow programming. I don't know about open-source projects I could build on and retrain using my data. So the most likely result is that I'll show off the fancy Tensorflow data as a "portfolio project" and archive it, never to be used in production.