I'm just starting with Torch and neural networks and just glancing at a lot of sample code and tutorials, I see a lot of variety in the how people structure their neural networks. There are layers like Linear(), Tanh(), Sigmoid() as well as criterions like MSE, ClassNLL, MultiMargin, etc.
I'm wondering what kind of factors people keep in mind when creating the structure of their network? For example, I know that in a ClassNLLCriterion, you want to have the last layer of your network be a LogSoftMax() layer so that you can input the right log probabilities.
Are there any other general rules or guidelines when it comes to creating these networks?
Thanks
Here is a good webpage which contains the pros and cons of some of the main activation functions;
http://cs231n.github.io/neural-networks-1/#actfun
It can boil down to the problem at hand and knowing what to do when something goes wrong. As an example, if you have a huge dataset and you can't churn through it terribly quickly then a ReLU might be better in order to quickly get to a local minimum. However you could find that some of the ReLU units "die" so you might want to keep a track on the proportion of activated neurons in that particular layer to make sure this hasn't happened.
In terms of criterions, they are also problem specific but a bit less ambiguous. For example, binary cross entropy for binary classification, MSE for regression etc. It really depends on the objective of the whole project.
For the overall network architecture, I personally find it can be a case of trying out different architectures and seeing which ones work and which don't on your test set. If you think that the problem at hand is terribly complex and you need a complex network to solve the problem then you will probably want to try making a very deep network to begin with, then add/remove a few layers at a time to see if you have under/overfitted. As another example, if you are using convolutional network and the input is relatively small then you might try and use a smaller set of convolutional filters to begin with.
I wrote a neural network and made a small application with things eating other things.
But I don't really know, how to make the thing genetic.
Currently I'm recording all the inputs and outputs from every individual every frame.
At the end of an generation, I then teach every knew individual the data from the top 10 best fitting individuals from prevous generations.
But the problem is, that the recorded data from a a pool of top 10 individuals at 100 generations, is about 50MB large. When I now start a new generation with 20 individuals I have to teach them 20x50MB.
This process takes longer than 3 minutes, and I am not sure if this is what I am supposed to do in genetic neural networks.
My approach works kind of good actually. Only the inefficiency bugs me. (Of course I know, I could just reduce the population.)
And I could't find me a solution to what I have to crossover and what to mutate.
Crossovering and mutating biases and weights is nonsense, isn't it? It only would break the network, would't it? I saw examples doing just this. Mutating the weight vector. But I just can't see, how this would make the network progress reaching it's desired outputs.
Can somebody show me how the network would become better at what it is doing by randomly switching and mutating weights and connections?
Would't it be the same, just randomly generating networks and hoping they start doing what they are supposed to do?
Are there other algorithms for genetic neural networks?
Thank you.
Typically, genetic algorithms for neural networks are used as an alternative to training with back-propagation. So there is no training phase (trying to combine various kinds of supervised training with evolution is an interesting idea, but isn't done commonly enough for there to be any standard methods that I know of).
In this context, crossover and mutation of weights and biases makes sense. It provides variation in the population. A lot of the resulting neural networks (especially early on) won't do much of anything interesting, but some will be better. As you keep selecting these better networks, you will continue to get better offspring. Eventually (assuming your task is reasonable and such) you'll have neural networks that are really good at what you want them to do. This is substantially better than random search, because evolution will explore the search space of potential neural networks in a much more intelligent manner.
So yes, just about any genetic neural network algorithm will involve mutating the weights, and perhaps crossing them over as well. Some, such as NEAT, also evolve the topology of the neural network and so allow mutations and crossovers that add or remove nodes and connections between nodes.
How do I approach the problem with a neural network and a intrusion detection system where by lets say we have an attack via FTP.
Lets say some one attempts to continuously try different logins via brute force attack on an ftp account.
How would I set the structure of the NN? What things do I have to consider? How would it recognise "similar approaches in the future"?
Any diagrams and input would be much appreciated.
Your question is extremely general and a good answer is a project in itself. I recommend contracting someone with experience in neural network design to help come up with an appropriate model or even tell you whether your problem is amenable to using a neural network. A few ideas, though:
Inputs need to be quantized, so start by making a list of possible numeric inputs that you could measure.
Outputs also need to be quantized and you probably can't generate a simple "Yes/no" response. Most likely you'll want to generate one or more numbers that represent a rough probability of it being an attack, perhaps broken down by category.
You'll need to accumulate a large set of training data that has been analyzed and quantized into the inputs and outputs you've designed. Figuring out the process of doing this quantization is a huge part of the overall problem.
You'll also need a large set of validation data, which should be quantized in the same way as the training data, but that should not take any part in the training, as otherwise you will simply force a correlation network that may well be completely meaningless.
Once you've completed the above, you can think about how you want to structure your network and the specific algorithms you want to use to train it. There is a wide range of literature on this topic, but, honestly, this is the simpler part of the problem. Representing the problem in a way that can be processed coherently is much more difficult.
Why do we use neural networks? It's biologic. Aren't there any more solutions that're more "suitable" for computers?
In other words: Why do we use the human brain as a model for inspiration for artifical intelligence?
Neural networks aren't really very biological. They resemble, at a very general level, the architecture of neurons, but it's a great exaggeration to say that they work "just like the brain" (an exaggeration that's encouraged by some neural-net advocates, alas).
Neural nets are mostly used for fuzzy, difficult problems that don't yield to traditional algorithmic approaches. IOWs, there are more "suitable" solutions for computers, but sometimes those solutions don't work, and in those cases one approach is a neural network.
Why do we use neural networks?
Because they're simple to construct, and often appear to be a good approach to certain classes of problems, such as pattern recognition.
Aren't there any more solutions that're more "suitable" for computers?
Yes, implementations that more closely match a computer's architecture can be more suitable for the computer, but then can be less suitable for an effective solution.
Why do we use the human brain as a model for inspiration for artifical intelligence?
Because our brain is the superior example we have of something intelligent.
Neural Networks are still used for two reasons.
They are easy to understand for people who don't want to delve into the math of a more complicated algorithm.
They have a really good name. I mean when you role into a CEO's office to sell him your model which would you rather say, Neural Network or Support Vector Machine. When he asks how it works you can just say "just like the neurons in your brain", which is something most people understand. If you try and explain a support vector machine Mr. CEO is going to be lost (Not because he is dumb but because SVMs are harder to understand).
Sometimes they are still useful however I think that the training time is often just too long.
I don't understand the question. Neural nets are suitable for certain functions, and not others. The same is true for various other sorts of classes of algorithms, regardless of what they might have been inspired by.
If we have a good many inputs to something, and we want some outputs, and we have a set of example inputs with known desired outputs, and we don't want to calculate a function ourselves, neural nets are excellent. We feed in the example inputs, compare the output to the example outputs, and adjust the inner workings of the NN in an automatic fashion, to make the NN output closer to the desired output.
This sort of function derivation is very useful in various forms of pattern recognition and general classification. It isn't a panacea, of course. It has no explanatory power (in that you can't look at the innards to see why it classifies something in a particular way), it doesn't offer guarantees of correctness within certain limits, validating how well it works is difficult, and gathering enough examples for training and validation can be expensive or even impossible. The trick is to know when to use a NN and what sort to use.
There are, of course, people who oversell the things as some sort of super solution or even an explanation of human thought, and you might be reacting to them.
Neural network are only "inspired" by the neural structure of our brain, but they are not even close to the complexity of the behaviour of a real neuron (to date there is no neuron model that captures the complexity of a SINGLE neuron, don't even think about a neuronal population...)
Although "neural", machine "learning" and other "pseudo-bio" (like "genetic algorithms") terms are very "cool", that does not mean that they are actually based on real biological processes.
Just that they may very approximatively remind of a biological situation.
NB: of course this does not make them useless! They're very very important in many fields!
Neural networks have been around for a while, and originally were developed to model as close an understanding as we had at the time to the way neurons work in the brain. They represent a network of neurons, hence "neural network." Since computers and brains are very different hardware-wise, implementing anything like a brain with a computer is going to be rather clunky. However, as others have stated so far, neural networks can be useful for some things that are vague such as pattern recognition, facial recognition, and other similar uses. They are also still useful as a basic model of how neurons connect and are often used in Cognitive Science and other fields of artificial intelligence to try to understand how small parts of the complex human brain might make simple decisions. Unfortunately, once a neural network "learns" something, it is very difficult to understand how it actually makes its decisions.
There are, of course, many misuses of neural networks and in most non-research applications, other algorithms have been developed that are much more accurate. If a piece of business software proudly proclaims it uses a neural network, chances are it probably doesn't need it, and might be using it to inefficiently perform a task that could be performed in a much easier way. Unless the software is actually "learning" on the fly, which is very rare, neural networks are pretty much useless. And even when the software is "learning", sometimes neural networks aren't the best way to go.
While I admit, I tinker with Neural Networks because of my hopes in creating high level AI, however, you can look at a Neural Network as being more than just just an artificial representation of a human brain, but as a Mathematical construct.
For example Let's say you have a function y = f(x) or more abstractly y = f(x1, x2, ..., xn-1, xn), Neural networks themselves act as functions, or even a set of functions, taking in a large input and producing some output [y1, y2, ..., yn-1, yn] = f(x1, x2, ..., xn-1, xn)
Furthermore, they are not static, but instead can continue adapting and learning and eventually extrapolate(predict) interesting things. Their abstractness can even result in them coming up with unique solutions to problems that haven't haven't been thought up yet. For example the TDGammon program learned to play backgammon and beat the world champion. The world champion stated that the program play a unique end game that he had never seen. (that's pretty awesome if you ask me considering the complexity of NNs)
And then when you look at recurrent neural networks (i.e. can have internal feedback loops, or pipe their output back into their input, while consuming new input) they can solve even more interesting problems, and map even more complex functions.
In a nutshell Neural Networks are like a very very abstract high dimensional function and capable of mapping/learning very interesting things that would be otherwise impossible to program programmatically. For example, the energy needed to calculate the total net Forces of Gravity on a large number of objects is intense (you have to calculate it for each object, and against each object), but once a neural network learns how to map it they can do these complex calculations that would run in exponential or combinatoric? time in polynomial time. Just look at how fast your brain processes physics data, spatial data/ images / sound when you dream. That's the potential computation power of Neural Networks. And to also mention the way they store data is very clever as well (in synaptics patterns, i.e. memories)
Artificial intelligence is a branch of computer science devoted to making computers more 'biologic.' This is useful when you want a computer to do human(biologic) things like play chess, or imitate casual conversation.
Human brains are much more efficient and powerful in some ways than the most powerful computers, so it makes sense to try to imitate a biological way of processing information.
Most neural networks I'm aware of are nothing more than flexible interpolators. Backpropagating of errors is easy and fast, here are some possible uses :
Classification of data
Some games (modern backgammon AIs beat the best players in the world, the evaluation function is a neural net)
Pattern recognition (OCR ?)
There is nothing particularly related to human intelligence. There are other uses of neural nets, I have seen an implementation of associative memory which allowed for degradation without (much) data loss, pretty much like the brain which sees some neurons die with time.
I've witnessed a debate on this topic, I wanted to know your opinion ? From a programmers perspective, can a we consider a neural network a more advanced "data structure" ?
Is a neural network actually more than a data-structure ?
Yes! A neural network is an entire program, or at least a "system" or a "mechanism". A neural network uses data structures (e.g. arrays, hashmaps, linked lists...) to store its weights and connections, but most developers would draw a line between
data structures as the underlying "plumbing" and
advanced constructs like neural networks which sit on a higher abstraction layer.
I'm trying to think of a good analogy for your question. At the moment, the best I can come up with is asking "can we consider an automobile a more advanced internal combustion engine?"
A neural network is definitely not an "advanced 'data structure'"... a neural network is an algorithm for function/statistical estimation, optimization and dynamic behavior. It's also referred to as a machine learning algorithm.
"Is a neural network actually more
than a data-structure ?"
It does a lot more than a data structure... so from that stand point, yes, but in general it's an invalid comparison. It actually allows your computer to "learn" how to correlate certain patterns of input with certain patterns of output.
A neural network is a statistical model, not a data structure. Data structures are meant to store and recall information. A statistical model is meant to record events and provide useful information regarding the event's statistical properties.
Thus, a NN uses data structures but is itself not a data structure.
This question is like asking if Mr. Coffee is a computer simply because it may contain one to accomplish its task. Mr. Coffee is not a computer, but yes you could tear it apart and find interesting ways to mangle the control electronics inside to do some computing.
No. Neurons are not data, the brain is not data. It's all spatial/quantum/?? stuff, possibly infinitely complex depending on how deep you want to go. This cannot be represented as data, and can only be processed in its own context.
From Wikipedia:
In computer science, a data structure
is a particular way of storing and
organizing data in a computer so that
it can be used efficiently.
If you represent a neural network in a computer, then yes. It's a very poor abstraction. Can you even call it a proper neural network? If you have a queue of people, you wouldn't call it a queue of data items, or even a data structure.