I'm trying to train a model with TPUs but do I have to use Google Colab or similar platforms?
You can only train models on TPUs using Google Cloud.
It completely depends on your model. If it is medium-sized or big like AlexNet or VGG19, you have to use Google Colab or Kaggle or other similar platforms which offer you free, limited-time access to their TPUs. If your model is light like LeNet, you can train it on your own computer even if you have only a CPU (not GPU). When using Colab, don't forget to change the runtime into TPU.
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Is there an emulator to debug code for TPU, instead of expending money debugging on real TPU before everything is ready to work?
Any other technique that would have a similar effect would be appreciated as answer as well.
I've found out that there's not an emulator nor any kind of virtualization of TPUs for development.
What can be done instead, is to run your code in Colab notebooks, being restricted by some limitations (i.e. your data has to be stored in Google Cloud to be accessible for the notebook).
There are several articles in websites like Medium about training/using models in TPUs through notebooks, each one focusing on different aspects or with different content within the example code. Here is one of them, just to provide an example and further useful links for anyone interested.
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’m looking a simple tool to train and test neural networks for classification tasks. It need not be very sophisticated tool and I just want to train and test simple data sets such as given in the following web site.
http://www.stats.ox.ac.uk/pub/PRNN/
It's not a pre-made utility, but you could roll your own quite quickly using the Encog neural network framework (for both Java and .NET).
I've used it before and it was quite easy to get to grips with. The documentation is quite good, and if you need it, I've also found support on the forums to be good.
* UPDATE *
I just remembered that Encog does actually ship with a pre-made utility called Encog Workbench, which should do what you want.
I saw that the tool weka is having a gui interface. This gui interface is very easy for non coding users to classify data sets into classes. Matlab is very difficult since say for example making a neural network you need to write code and to do that you need to have a solid understanding of whats going on. Are there other tools like weka or else is there a plugin to matlab that gives more power to it?
RapidMiner has a functional GUI, and will work for both classification and clustering. It is the most popular open-source (free) data mining application available as of 2012.
RapidMiner: http://rapid-i.com/
It also has numerous training videos and tutorials that you can follow along with - I learned basic clustering methods using a K-means cluster method in about 3 hours. See the Vancouver Data blog for some great RapidMiner analytics videos. Top-notch stuff, really.
Vancouver Data (Neil McGuigan): http://vancouverdata.blogspot.com/
As a bonus, you can install the Weka plug-in, which then gives you GUI Weka. All of the add-ons are free and well-integrated. Other add-ons include a GUI 'R' (the stats program), Reporting Services, Text and Web Analytics, etc. It is fairly simple to use straight 'out of the box' (IMO).
Weka is very (very) powerful and you can write your own classifier if that's what you need to do.
Between Matlab and Weka there's pretty much nothing you can't do in terms of Machine Learning.
You might want to check out Netlab toolkit for Matlab, which is a neural network toolkit developed by a Professor at Aston University - it is available from http://www1.aston.ac.uk/eas/research/groups/ncrg/resources/netlab/
Hi I trained my network into Matlab neural network toolbox I save it.. I can used into matlab environment by calling sim(net,p) but How can I export it to anther .net apps ?
You might be better off exporting just the weights (etc.) element of the network and using them in a library designed for .NET like AForge.NET, rather than deal with all of the interop.