My question is :
Can we choose how to connect the neurons in tensorflow ?
Instead of fully connected.
Yes, sure you can do that. I suggest you start with the Tensorflow Tutorial. Or you can have a look at my XOR example.
Tensorflow lets you define a computational graph which is then automatically optimized by an objective you define and an algorithm you choose. This graph can be a fully connected standard feed forward network or anything else.
Related
Does this:
https://www.asus.com/gr/AIoT-Industrial-Solutions/Tinker-Edge-T/
have any difference with this:
https://coral.ai/products/dev-board/
?? For example can I train a custom CNN? Has anyone experience to give an advice? I want to buy a SBC with TPU in order to train a CNN faster but the information I found on google do not help...
The asus Tinker Edge T actually have the same edgetpu that is on the dev board. I believe the SOM is exactly the same and only the base board are different.
The edgetpu are targeted for inferencing only, not training, so you probably won't profit from it. However, you can take advantage of fast inference time to do things like backprop which allows you to do some transfer learning:
https://coral.ai/docs/edgetpu/retrain-classification-ondevice-backprop/
I am building a code and trying to keep things as generic as possible. I have seen a number of tutorials and post but they are all platform specific (tensorflow\pytorch).
Is there a good way to load and use a previously trained neural network model in a manner that the code will be able to cope with both torch and tensorflow? Does it matter in which version of tensorflow\torch the network was built in? I want the code to be as generic as possible.
Also, do I need to know the structure of the original network or can I load it and use it without the notion of the structure?
I don't think it is possible to write a program that can load pre-trained models from both Torch and Tensorflow as they save in different formats.
You might want to look into the Open Neural Network Exchange Format (https://onnx.ai/) if you are creating the models yourself, this is an initiative backed by Amazon, Facebook, Microsoft, and others to create a portable file format for deep learning models.
I want to simulate my protocol in Matlab. I do not know about any toolbox nor i get any information about WSN coding in Matlab.
I want to know about it.
regards,
You can use Low Energy Adaptive Clustering Hierarchy (LEACH) cluster based routing protocol in WSN:
http://www.mathworks.com/matlabcentral/fileexchange/25853-wireless-networks
And you can write your own simulator using the MATLAB scripting language. Examples:
SimEvents, Prowler, TrueTime and Complex Networks Package
Hope this helps.
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.
Hey guys, Am wondering if anybody can help me with a starting point for the design of a Neural Network system that can recognize visual patterns, e.g. checked, and strippes. I have knowledge of the theory, but little practical knowledge. And net searches are give me an information overload. Can anybody recommend a good book or tutorial that is more focus on the practical side.
Thank you!
Are you only trying to recognize patterns such as checkerboards and stripes? Do you have to use a neural network system?
Basically, you want to define a bunch of simple features on the board and use them as input to the learning system. It can often be easier to define a lot of binary features and feed them into a single-layer network (what can become essentially linear regression).
Look at how neural networks were used for learning to play backgammon (http://www.research.ibm.com/massive/tdl.html), as this will help give you a sense of the types of features that make learning with a neural network work well.
As suggested above, you probably want to reduce your image a set of features. A corner detector (perhaps the Harris method) could be used to determine features in the checkerboard pattern. Likewise, an edge detector (perhaps Canny) could be used in the stripes case. As mentioned above, the Hough transform is a good line detection method.
MATLAB's image processing toolbox contains these methods, so you might try those for rapid prototyping. OpenCV is an open-source computer vision library that also provides these tools (and many others).