I've got a problem which I imagine should be really simple but I can't seem to find anything on. I'm using the Fast Artificial Neural Network Library with the Python bindings and my network has been trained on some data and saved. So far so good.
The problem I'm having is I just can't seem to find any command to print the weighting for the various nodes. Could someone tell me what I need to use to do that please?
Never mind, I found it.
Just open the saved file with a text edit. Feeling a little silly now.
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Is there a simple explanation for dummies like me? I know that there's a source code of Leela, I've heard that it uses neural networks with MCTS (plus UCT), but there are lot of hard things remaining. Do I need to train Leela myself by running it? Or do I need to download something from Internet? (so-called trained data?) If so, do I need constantly update this data? Does it play stronger with every game?
Thank you much for advance.
I trying to produce a neural network visualization similar to the one below (link):
I was wondering if anybody could suggest any resources that they have used themselves and are happy with. If, in particular, anyone knows of any freely available template from which I can build off, that would be great.
I happened to e-mail the author who let me know that he used Powerpoint for this one. Who would have thought!
Hi I have been searching though research papers on what features would be good for me to use in my handwritten OCR classifying neural network. I am a beginner so I have been just taking the image of the handwritten character, made a bounding box around it, and then resize it into a 15x20 binary image. So this means i have an input layer of 300 features. From the papers i have found on google (most of which are quite old) the methods really vary. My accuracy is not bad with just a binary grid of the image, but I was wondering if anyone had other features I could use to boost my accuracy. Or even just pointing me in the right direction. I would really appreciate it!
Thanks,
Zach
I haven't read any actual papers on this topic, but my advice would be to get creative. Use anything you could think of that might help the classifier identify numbers.
My first thought would be to try and identify "lines" in the image, maybe via a modified "sliding window" algorithm (sliding/rotating line?), or to try and identify a "line of best fit" to the image (to help the classifier respond to changes in italicism or writing style). Really though, if you're using a neural network, it should be picking up on these sorts of things without your manual help (that's the whole point of them!)
I would focus first on the structure and topology of your net to try and improve performance, and worry about additional features only if you cannot get satisfactory performance some other way. Also you could try improving the features you already have, make sure the character is centered in the image, maybe try an algorithm to skew italicised characters to make them vertical?
In my experience these sorts of things don't often help, but you could get lucky and run into one that improves your net :)
I am using an Arduino to control a car and I want to make it autonomous by using a webcam to see the object I want and make the car move to this location. I need several things:
MATLAB code
Interface between MATLAB and Arduino
How do I connect between them (software, not hardware)
I need any tutorial to learn or any instructions to make my project. I see many people have done this before, but unfortunately they did not mention how to start these kind of projects.
This question is fairly broad, so I apologize in advance for my somewhat general response.
The easiest way to interface a webcam with MATLAB is to make use of the Image Acquisition Toolbox. This link provides documentation detailing how to do this.
There is a good chance that you'll also want to make use of the Image Processing toolbox in MATLAB to be able to process the acquired images to determine where to go. See this doc. Though, after you've determined more specifically how you plan to process these images, there are probably numerous algorithms that you could find online that would not explicitly require this toolbox.
As far as interfacing with Arduino is concerned, there is a support package from the MathWorks that allows you to interface MATLAB code and Simulink models with Arduino. See this link
The only other general suggestion that I have is to consider using Simulink for this project rather than MATLAB. I feel that the model based approach of Simulink is a much better fit when designing control systems.
I hope that this helps you get things started.
For my computer vision class, I'm going to be doing a project where I extract information about a hallway based on an image of that hallway. In particular, the lines of the hallway which extend toward a vanishing point will be of interest. My question is whether I should use Matlab, OpenCV, or something else to implement this.
I don't have a ton of time for this project. This fact makes Matlab seem like a good option since it seems you can usually get things up and running quickly there. On the other hand, I hope to take what I do for this class project and extend it out further for research once the class is complete. This makes OpenCV seem better as (from what I've read) it's much more efficient. It's possible another choice would be to implement it in Matlab for the project than port that code to an OpenCV form later. It should be noted that I have plenty of experience with C/C++, but only a little in both Matlab and OpenCV.
At the moment, I'm leaning toward just using OpenCV from the start. However, I would like the opinion of someone who's had a bit more experience here than myself. If you'd recommend something over both OpenCV and Matlab, please say so. Also, if you have any tips on what packages or toolkits might be useful for such a project, they would be greatly appreciated.
Any suggestions? Thanks for your time!
Using which one it is easier for you to write a piece of code to read an image file and display it?
If you know C++ very well, then it should be easy to debug the code. Since you say you have little experience with Matlab, if you make a small mistake in the code debugging can take a long time.
So I suggest break down the problem into:
read image and display it, this is very easy in both
detect edges using a simple/classic method, this is super easy in both, display the result and visually check it's correctly done
use a robust line fitting method, the RANSAC and Hough transform methods are probably what you're going to use, OpenCV makes using the easier than you can guess, Matlab also has built in functions to detect lines using the Hough transform, and gives you the start/end points of each segment. But if you're finding a vanishing point, you shouldn't need those.
The decision is yours, this is not a very difficult problem, can find loads of help on the web. Good luck with the project, and please let us know how it goes.