I'm new to deep learning and I'm working on a project that involves working on cartoon images and recognizing the emotions of the cartoon characters, I tried the approach of transfer learning but on doing some research I realised that the ImageNet and InceptionV3 only work for human faces. What approach should I follow? The training set is limited of about 300 images and the test set has around 180 images. I'm still a beginner in this field and I thought this would be a good project to start with. Any suggestions/guidance will be much appreciated. Thank you .
If your data is very low you can use data augmentation.Take a look:
https://towardsdatascience.com/data-augmentation-for-deep-learning-4fe21d1a4eb9
and also:
https://machinelearningmastery.com/how-to-configure-image-data-augmentation-when-training-deep-learning-neural-networks/
If data augmentation did not help you, you must try another
algorithms.neural nets needs lots of data.If your Data is less your
network will overfitt.
you can use data augmentation. for data augmentation, you could use the imgaug package. here is the documentation of Imgaug package
I am very new in this field and I would like to create a Neural Network to classify a dataset that I have in MongoDB. I would like some advise about where should I start, what technology should I use or any tutorial that you think it can help.
If you know about any open source code that already does this, I would love to take a look at it.
Thank you !!
Pick a platform
In essence, you should pick a platform or framework that does much of the dirty work for you and read up on some tutorials for that.
The big choice is between natural language processing frameworks such as NLTK or spaCy or Stanford NLP tools; or a generic machine learning framework such as Tensorflow or PyTorch.
Text classification is a popular task that's reasonably entry-level, is well supported by pretty much everything (so it's not much to say there in a shopping question, pick whatever you like) and would have a bunch of tutorials available online for any major platform.
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!
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.
I'm considering using a neural network to power my enemies in a space shooter game i'm building and i'm wondering; how do you train neural networks when there is no one definitive good set of outputs for the network?
I'm studying neural networks at the moment, and they seem quite useless without well defined input and output encodings, and they don't scale at all to complexity (see http://en.wikipedia.org/wiki/VC_dimension). that's why neural network research has had so little application since the initial hype more than 20-30 years ago while semantic/state based AI took over everyone's interests because of it's success in real world applications.
A so a good place to start might be to figure out how to numerically represent the state of the game as inputs for the neural net.
The next thing would be to figure out what kind of output would correspond to actions in the game.
think about the structure of neural network to use. To get interesting complex behavior from neural networks, the network almost has to be recurrent. You'll need a recurrent network because they have 'memory', but beyond that you don't have much else to go on. However, recurrent networks with any complex structure is really hard to train to behave.
The areas where neural networks have been successful tend to be classification (image, audio, grammar, etc) and limited success in statistical prediction (what word would we expect to come after this word, what will the stock price be tomorrow?)
In short, it's probably better for you to use Neural nets for a small portion of the game rather as the core enemy AI.
You can check out AI Dynamic game difficulty balancing for various AI techniques and references.
(IMO, you can implement enemy behaviors, like "surround the enemy", which will be really cool, without delving into advanced AI concepts)
Edit: since you're making a space shooter game and you want some kind of AI for your enemies, I believe you'll find interesting this link: Steering Behaviors For Autonomous Characters
Have you considered that it's easily possible to modify an FSM in response to stimulus? It is just a table of numbers after all, you can hold it in memory somewhere and change the numbers as you go. I wrote about it a bit in one of my blog fuelled deleriums, and it oddly got picked up by some Game AI news site. Then the guy who built a Ms. Pacman AI that could beat humans and got on the real news left a comment on my blog with a link to even more useful information
here's my blog post with my incoherant ramblings about some idea I had about using markov chains to continually adapt to a game environment, and perhaps overlay and combine something that the computer has learned about how the player reacts to game situations.
http://bustingseams.blogspot.com/2008/03/funny-obsessive-ideas.html
and here's the link to the awesome resource about reinforcement learning that mr. smarty mcpacman posted for me.
http://www.cs.ualberta.ca/%7Esutton/book/ebook/the-book.html
here's another cool link
http://aigamedev.com/open/architecture/online-adaptation-game-opponent/
These are not neural net approaches, but they do adapt and continually learn, and are probably better suited to games than neural networks.
I'll refer you to two of Matthew Buckland's books.
Programming Game AI by example
AI Techniques for Game Programming
The second book goes into back-propagation ANN, which is what most people mean when they
talk about NN anyway.
That said, I think the first book is more useful if you want to create meaningful game AI. There's a nice, meaty section on using FSM successfully (and yes, it's easy to trip yourself up with a FSM).