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  • C# Neural Networks with Encog

    - by JoshReuben
    Neural Networks ·       I recently read a book Introduction to Neural Networks for C# , by Jeff Heaton. http://www.amazon.com/Introduction-Neural-Networks-C-2nd/dp/1604390093/ref=sr_1_2?ie=UTF8&s=books&qid=1296821004&sr=8-2-spell. Not the 1st ANN book I've perused, but a nice revision.   ·       Artificial Neural Networks (ANNs) are a mechanism of machine learning – see http://en.wikipedia.org/wiki/Artificial_neural_network , http://en.wikipedia.org/wiki/Category:Machine_learning ·       Problems Not Suited to a Neural Network Solution- Programs that are easily written out as flowcharts consisting of well-defined steps, program logic that is unlikely to change, problems in which you must know exactly how the solution was derived. ·       Problems Suited to a Neural Network – pattern recognition, classification, series prediction, and data mining. Pattern recognition - network attempts to determine if the input data matches a pattern that it has been trained to recognize. Classification - take input samples and classify them into fuzzy groups. ·       As far as machine learning approaches go, I thing SVMs are superior (see http://en.wikipedia.org/wiki/Support_vector_machine ) - a neural network has certain disadvantages in comparison: an ANN can be overtrained, different training sets can produce non-deterministic weights and it is not possible to discern the underlying decision function of an ANN from its weight matrix – they are black box. ·       In this post, I'm not going to go into internals (believe me I know them). An autoassociative network (e.g. a Hopfield network) will echo back a pattern if it is recognized. ·       Under the hood, there is very little maths. In a nutshell - Some simple matrix operations occur during training: the input array is processed (normalized into bipolar values of 1, -1) - transposed from input column vector into a row vector, these are subject to matrix multiplication and then subtraction of the identity matrix to get a contribution matrix. The dot product is taken against the weight matrix to yield a boolean match result. For backpropogation training, a derivative function is required. In learning, hill climbing mechanisms such as Genetic Algorithms and Simulated Annealing are used to escape local minima. For unsupervised training, such as found in Self Organizing Maps used for OCR, Hebbs rule is applied. ·       The purpose of this post is not to mire you in technical and conceptual details, but to show you how to leverage neural networks via an abstraction API - Encog   Encog ·       Encog is a neural network API ·       Links to Encog: http://www.encog.org , http://www.heatonresearch.com/encog, http://www.heatonresearch.com/forum ·       Encog requires .Net 3.5 or higher – there is also a Silverlight version. Third-Party Libraries – log4net and nunit. ·       Encog supports feedforward, recurrent, self-organizing maps, radial basis function and Hopfield neural networks. ·       Encog neural networks, and related data, can be stored in .EG XML files. ·       Encog Workbench allows you to edit, train and visualize neural networks. The Encog Workbench can generate code. Synapses and layers ·       the primary building blocks - Almost every neural network will have, at a minimum, an input and output layer. In some cases, the same layer will function as both input and output layer. ·       To adapt a problem to a neural network, you must determine how to feed the problem into the input layer of a neural network, and receive the solution through the output layer of a neural network. ·       The Input Layer - For each input neuron, one double value is stored. An array is passed as input to a layer. Encog uses the interface INeuralData to hold these arrays. The class BasicNeuralData implements the INeuralData interface. Once the neural network processes the input, an INeuralData based class will be returned from the neural network's output layer. ·       convert a double array into an INeuralData object : INeuralData data = new BasicNeuralData(= new double[10]); ·       the Output Layer- The neural network outputs an array of doubles, wraped in a class based on the INeuralData interface. ·        The real power of a neural network comes from its pattern recognition capabilities. The neural network should be able to produce the desired output even if the input has been slightly distorted. ·       Hidden Layers– optional. between the input and output layers. very much a “black box”. If the structure of the hidden layer is too simple it may not learn the problem. If the structure is too complex, it will learn the problem but will be very slow to train and execute. Some neural networks have no hidden layers. The input layer may be directly connected to the output layer. Further, some neural networks have only a single layer. A single layer neural network has the single layer self-connected. ·       connections, called synapses, contain individual weight matrixes. These values are changed as the neural network learns. Constructing a Neural Network ·       the XOR operator is a frequent “first example” -the “Hello World” application for neural networks. ·       The XOR Operator- only returns true when both inputs differ. 0 XOR 0 = 0 1 XOR 0 = 1 0 XOR 1 = 1 1 XOR 1 = 0 ·       Structuring a Neural Network for XOR  - two inputs to the XOR operator and one output. ·       input: 0.0,0.0 1.0,0.0 0.0,1.0 1.0,1.0 ·       Expected output: 0.0 1.0 1.0 0.0 ·       A Perceptron - a simple feedforward neural network to learn the XOR operator. ·       Because the XOR operator has two inputs and one output, the neural network will follow suit. Additionally, the neural network will have a single hidden layer, with two neurons to help process the data. The choice for 2 neurons in the hidden layer is arbitrary, and often comes down to trial and error. ·       Neuron Diagram for the XOR Network ·       ·       The Encog workbench displays neural networks on a layer-by-layer basis. ·       Encog Layer Diagram for the XOR Network:   ·       Create a BasicNetwork - Three layers are added to this network. the FinalizeStructure method must be called to inform the network that no more layers are to be added. The call to Reset randomizes the weights in the connections between these layers. var network = new BasicNetwork(); network.AddLayer(new BasicLayer(2)); network.AddLayer(new BasicLayer(2)); network.AddLayer(new BasicLayer(1)); network.Structure.FinalizeStructure(); network.Reset(); ·       Neural networks frequently start with a random weight matrix. This provides a starting point for the training methods. These random values will be tested and refined into an acceptable solution. However, sometimes the initial random values are too far off. Sometimes it may be necessary to reset the weights again, if training is ineffective. These weights make up the long-term memory of the neural network. Additionally, some layers have threshold values that also contribute to the long-term memory of the neural network. Some neural networks also contain context layers, which give the neural network a short-term memory as well. The neural network learns by modifying these weight and threshold values. ·       Now that the neural network has been created, it must be trained. Training a Neural Network ·       construct a INeuralDataSet object - contains the input array and the expected output array (of corresponding range). Even though there is only one output value, we must still use a two-dimensional array to represent the output. public static double[][] XOR_INPUT ={ new double[2] { 0.0, 0.0 }, new double[2] { 1.0, 0.0 }, new double[2] { 0.0, 1.0 }, new double[2] { 1.0, 1.0 } };   public static double[][] XOR_IDEAL = { new double[1] { 0.0 }, new double[1] { 1.0 }, new double[1] { 1.0 }, new double[1] { 0.0 } };   INeuralDataSet trainingSet = new BasicNeuralDataSet(XOR_INPUT, XOR_IDEAL); ·       Training is the process where the neural network's weights are adjusted to better produce the expected output. Training will continue for many iterations, until the error rate of the network is below an acceptable level. Encog supports many different types of training. Resilient Propagation (RPROP) - general-purpose training algorithm. All training classes implement the ITrain interface. The RPROP algorithm is implemented by the ResilientPropagation class. Training the neural network involves calling the Iteration method on the ITrain class until the error is below a specific value. The code loops through as many iterations, or epochs, as it takes to get the error rate for the neural network to be below 1%. Once the neural network has been trained, it is ready for use. ITrain train = new ResilientPropagation(network, trainingSet);   for (int epoch=0; epoch < 10000; epoch++) { train.Iteration(); Debug.Print("Epoch #" + epoch + " Error:" + train.Error); if (train.Error > 0.01) break; } Executing a Neural Network ·       Call the Compute method on the BasicNetwork class. Console.WriteLine("Neural Network Results:"); foreach (INeuralDataPair pair in trainingSet) { INeuralData output = network.Compute(pair.Input); Console.WriteLine(pair.Input[0] + "," + pair.Input[1] + ", actual=" + output[0] + ",ideal=" + pair.Ideal[0]); } ·       The Compute method accepts an INeuralData class and also returns a INeuralData object. Neural Network Results: 0.0,0.0, actual=0.002782538818034049,ideal=0.0 1.0,0.0, actual=0.9903741937121177,ideal=1.0 0.0,1.0, actual=0.9836807956566187,ideal=1.0 1.0,1.0, actual=0.0011646072586172778,ideal=0.0 ·       the network has not been trained to give the exact results. This is normal. Because the network was trained to 1% error, each of the results will also be within generally 1% of the expected value.

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  • Neural network input preprocessing

    - by TND
    It's clear that the effectiveness of a neural network depends strongly on the format you give it to work with. You want to preprocess it into the most convenient form you can algorithmically get to, so that the neural network doesn't have to account for that itself. I'm working on a little project that (surprise!) is going to be using neural networks. My future goal is to eventually use NEAT, which I'm really excited about. Anyway, one of my ideas involves moving entities in continuous 2D space, from a top-down perspective (this would be a really cool game AI). Of course, unless these guys are blind, they're going to be able to see the world around them. There's a lot of different ways this information could be fed into the network. One interesting but expensive way is to simply render a top-down "view" of things, with the entities as dots on the picture, and feed that in. I was hoping for something much simpler to use (at least at first), such as a list of the x (maybe 7 or so) nearest entities and their position in relative polar coordinates, orientation, health, etc., but I'm trying to think of the best way to do it. My first instinct was to order them by distance, which would inherently also train the neural network to consider those more "important". However, I was thinking- what if there's two entities that are nearly the same distance away? They could easily alternate indexes in that list, confusing the network. My question is, is there a better way of representing this? Essentially, the issue is the network needs a good way of keeping track of who's who, while knowing (by being inputted) relevant information about the list of entities it can see. Thanks!

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  • Continuous output in Neural Networks

    - by devoured elysium
    How can I set Neural Networks so they accept and output a continuous range of values instead of a discrete ones? From what I recall from doing a Neural Network class a couple of years ago, the activation function would be a sigmoid, which yields a value between 0 and 1. If I want my neural network to yield a real valued scalar, what should I do? I thought maybe if I wanted a value between 0 and 10 I could just multiply the value by 10? What if I have negative values? Is this what people usually do or is there any other way? What about the input? Thanks

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  • Plain-English tutorial on artificial neural networks?

    - by Stuart
    I've Googled, StackOverflowed, everything, and I cannot seem to find a tutorial I can understand. I understand the concept of genetic algorithms, and how to implement them, (Though I haven't tried) but I cannot grasp the concept of neural networks. I know vaguely how they work... And that's about it. Could someone direct me to a tutorial that could help someone who has not even graduated middle school yet? Sure, I'm several years ahead of the majority of people my grade, but I don't understand summation, (which I apparently need if I don't want a simple binary output) vectors, and other things that I apparently should know. Is there a simple, bare-bones tutorial for neural networks? After I learn the basics, I'll proceed to more difficult ones. Preferably, they would be in Java. Thanks!

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  • Neural Network Always Produces Same/Similar Outputs for Any Input

    - by l33tnerd
    I have a problem where I am trying to create a neural network for Tic-Tac-Toe. However, for some reason, training the neural network causes it to produce nearly the same output for any given input. I did take a look at Artificial neural networks benchmark, but my network implementation is built for neurons with the same activation function for each neuron, i.e. no constant neurons. To make sure the problem wasn't just due to my choice of training set (1218 board states and moves generated by a genetic algorithm), I tried to train the network to reproduce XOR. The logistic activation function was used. Instead of using the derivative, I multiplied the error by output*(1-output) as some sources suggested that this was equivalent to using the derivative. I can put the Haskell source on HPaste, but it's a little embarrassing to look at. The network has 3 layers: the first layer has 2 inputs and 4 outputs, the second has 4 inputs and 1 output, and the third has 1 output. Increasing to 4 neurons in the second layer didn't help, and neither did increasing to 8 outputs in the first layer. I then calculated errors, network output, bias updates, and the weight updates by hand based on http://hebb.mit.edu/courses/9.641/2002/lectures/lecture04.pdf to make sure there wasn't an error in those parts of the code (there wasn't, but I will probably do it again just to make sure). Because I am using batch training, I did not multiply by x in equation (4) there. I am adding the weight change, though http://www.faqs.org/faqs/ai-faq/neural-nets/part2/section-2.html suggests to subtract it instead. The problem persisted, even in this simplified network. For example, these are the results after 500 epochs of batch training and of incremental training. Input |Target|Output (Batch) |Output(Incremental) [1.0,1.0]|[0.0] |[0.5003781562785173]|[0.5009731800870864] [1.0,0.0]|[1.0] |[0.5003740346965251]|[0.5006347214672715] [0.0,1.0]|[1.0] |[0.5003734471544522]|[0.500589332376345] [0.0,0.0]|[0.0] |[0.5003674110937019]|[0.500095157458231] Subtracting instead of adding produces the same problem, except everything is 0.99 something instead of 0.50 something. 5000 epochs produces the same result, except the batch-trained network returns exactly 0.5 for each case. (Heck, even 10,000 epochs didn't work for batch training.) Is there anything in general that could produce this behavior? Also, I looked at the intermediate errors for incremental training, and the although the inputs of the hidden/input layers varied, the error for the output neuron was always +/-0.12. For batch training, the errors were increasing, but extremely slowly and the errors were all extremely small (x10^-7). Different initial random weights and biases made no difference, either. Note that this is a school project, so hints/guides would be more helpful. Although reinventing the wheel and making my own network (in a language I don't know well!) was a horrible idea, I felt it would be more appropriate for a school project (so I know what's going on...in theory, at least. There doesn't seem to be a computer science teacher at my school). EDIT: Two layers, an input layer of 2 inputs to 8 outputs, and an output layer of 8 inputs to 1 output, produces much the same results: 0.5+/-0.2 (or so) for each training case. I'm also playing around with pyBrain, seeing if any network structure there will work. Edit 2: I am using a learning rate of 0.1. Sorry for forgetting about that. Edit 3: Pybrain's "trainUntilConvergence" doesn't get me a fully trained network, either, but 20000 epochs does, with 16 neurons in the hidden layer. 10000 epochs and 4 neurons, not so much, but close. So, in Haskell, with the input layer having 2 inputs & 2 outputs, hidden layer with 2 inputs and 8 outputs, and output layer with 8 inputs and 1 output...I get the same problem with 10000 epochs. And with 20000 epochs. Edit 4: I ran the network by hand again based on the MIT PDF above, and the values match, so the code should be correct unless I am misunderstanding those equations. Some of my source code is at http://hpaste.org/42453/neural_network__not_working; I'm working on cleaning my code somewhat and putting it in a Github (rather than a private Bitbucket) repository. All of the relevant source code is now at https://github.com/l33tnerd/hsann.

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  • Neural Network settings for fast training

    - by danpalmer
    I am creating a tool for predicting the time and cost of software projects based on past data. The tool uses a neural network to do this and so far, the results are promising, but I think I can do a lot more optimisation just by changing the properties of the network. There don't seem to be any rules or even many best-practices when it comes to these settings so if anyone with experience could help me I would greatly appreciate it. The input data is made up of a series of integers that could go up as high as the user wants to go, but most will be under 100,000 I would have thought. Some will be as low as 1. They are details like number of people on a project and the cost of a project, as well as details about database entities and use cases. There are 10 inputs in total and 2 outputs (the time and cost). I am using Resilient Propagation to train the network. Currently it has: 10 input nodes, 1 hidden layer with 5 nodes and 2 output nodes. I am training to get under a 5% error rate. The algorithm must run on a webserver so I have put in a measure to stop training when it looks like it isn't going anywhere. This is set to 10,000 training iterations. Currently, when I try to train it with some data that is a bit varied, but well within the limits of what we expect users to put into it, it takes a long time to train, hitting the 10,000 iteration limit over and over again. This is the first time I have used a neural network and I don't really know what to expect. If you could give me some hints on what sort of settings I should be using for the network and for the iteration limit I would greatly appreciate it. Thank you!

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  • Neural Networks test cases

    - by Betamoo
    Does increasing the number of test cases in case of Precision Neural Networks may led to problems (like over-fitting for example)..? Does it always good to increase test cases number? Will that always lead to conversion ? If no, what are these cases.. an example would be better.. Thanks,

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  • how useful is Turing completeness? are neural nets turing complete?

    - by Albert
    While reading some papers about the Turing completeness of recurrent neural nets (for example: Turing computability with neural nets, Hava T. Siegelmann and Eduardo D. Sontag, 1991), I got the feeling that the proof which was given there was not really that practical. For example the referenced paper needs a neural network which neuron activity must be of infinity exactness (to reliable represent any rational number). Other proofs need a neural network of infinite size. Clearly, that is not really that practical. But I started to wonder now if it does make sense at all to ask for Turing completeness. By the strict definition, no computer system nowadays is Turing complete because none of them will be able to simulate the infinite tape. Interestingly, programming language specification leaves it most often open if they are turing complete or not. It all boils down to the question if they will always be able to allocate more memory and if the function call stack size is infinite. Most specification don't really specify this. Of course all available implementations are limited here, so all practical implementations of programming languages are not Turing complete. So, what you can say is that all computer systems are just equally powerful as finite state machines and not more. And that brings me to the question: How useful is the term Turing complete at all? And back to neural nets: For any practical implementation of a neural net (including our own brain), they will not be able to represent an infinite number of states, i.e. by the strict definition of Turing completeness, they are not Turing complete. So does the question if neural nets are Turing complete make sense at all? The question if they are as powerful as finite state machines was answered already much earlier (1954 by Minsky, the answer of course: yes) and also seems easier to answer. I.e., at least in theory, that was already the proof that they are as powerful as any computer.

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  • WCF web service with Neural Network

    - by Gary Frank
    I am developing a web service that performs object recognition. It will be available for testing as soon as enough code has been developed, and then officially when it is finished. It is based on a radically new type of artificial neural network that I designed. Its goal is to recognize any type of object within an image. Besides the WCF web service, the project will also create a website to test and demonstrate the web service. Here is a link with more information. http://www.indiegogo.com/VOR

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  • Resilient backpropagation neural network - question about gradient

    - by Raf
    Hello Guys, First I want to say that I'm really new to neural networks and I don't understand it very good ;) I've made my first C# implementation of the backpropagation neural network. I've tested it using XOR and it looks it work. Now I would like change my implementation to use resilient backpropagation (Rprop - http://en.wikipedia.org/wiki/Rprop). The definition says: "Rprop takes into account only the sign of the partial derivative over all patterns (not the magnitude), and acts independently on each "weight". Could somebody tell me what partial derivative over all patterns is? And how should I compute this partial derivative for a neuron in hidden layer. Thanks a lot UPDATE: My implementation base on this Java code: www_.dia.fi.upm.es/~jamartin/downloads/bpnn.java My backPropagate method looks like this: public double backPropagate(double[] targets) { double error, change; // calculate error terms for output double[] output_deltas = new double[outputsNumber]; for (int k = 0; k < outputsNumber; k++) { error = targets[k] - activationsOutputs[k]; output_deltas[k] = Dsigmoid(activationsOutputs[k]) * error; } // calculate error terms for hidden double[] hidden_deltas = new double[hiddenNumber]; for (int j = 0; j < hiddenNumber; j++) { error = 0.0; for (int k = 0; k < outputsNumber; k++) { error = error + output_deltas[k] * weightsOutputs[j, k]; } hidden_deltas[j] = Dsigmoid(activationsHidden[j]) * error; } //update output weights for (int j = 0; j < hiddenNumber; j++) { for (int k = 0; k < outputsNumber; k++) { change = output_deltas[k] * activationsHidden[j]; weightsOutputs[j, k] = weightsOutputs[j, k] + learningRate * change + momentumFactor * lastChangeWeightsForMomentumOutpus[j, k]; lastChangeWeightsForMomentumOutpus[j, k] = change; } } // update input weights for (int i = 0; i < inputsNumber; i++) { for (int j = 0; j < hiddenNumber; j++) { change = hidden_deltas[j] * activationsInputs[i]; weightsInputs[i, j] = weightsInputs[i, j] + learningRate * change + momentumFactor * lastChangeWeightsForMomentumInputs[i, j]; lastChangeWeightsForMomentumInputs[i, j] = change; } } // calculate error error = 0.0; for (int k = 0; k < outputsNumber; k++) { error = error + 0.5 * (targets[k] - activationsOutputs[k]) * (targets[k] - activationsOutputs[k]); } return error; } So can I use change = hidden_deltas[j] * activationsInputs[i] variable as a gradient (partial derivative) for checking the sing?

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  • Neural Network problems

    - by Betamoo
    I am using an external library for Artificial Neural Networks in my project.. While testing the ANN, It gave me output of all NaN (not a number in C#) The ANN has 8 input , 5 hidden , 5 hidden , 2 output, and all activation layers are of Linear type , and it uses back-propagation, with learning rate 0.65 I used one testcase for training { -2.2, 1.3, 0.4, 0.5, 0.1, 5, 3, -5 } ,{ -0.3, 0.2 } for 1000 epoch And I tested it on { 0.2, -0.2, 5.3, 0.4, 0.5, 0, 35, 0.0 } which gave { NaN , NaN} Note: this is one example of many that produces same case... I am trying to discover whether it is a bug in the library, or an illogical configuration.. The reasons I could think of for illogical configuration: All layers should not be linear Can not have descending size layers, i.e 8-5-5-2 is bad.. Only one testcase ? Values must be in range [0,1] or [-1,1] Is any of the above reasons could be the cause of error, or there are some constraints/rules that I do not know in ANN designing..? Note: I am newbie in ANN

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  • Neural Network with softmax activation

    - by Cambium
    This is more or less a research project for a course, and my understanding of NN is very/fairly limited, so please be patient :) ============== I am currently in the process of building a neural network that attempts to examine an input dataset and output the probability/likelihood of each classification (there are 5 different classifications). Naturally, the sum of all output nodes should add up to 1. Currently, I have two layers, and I set the hidden layer to contain 10 nodes. I came up with two different types of implementations 1) Logistic sigmoid for hidden layer activation, softmax for output activation 2) Softmax for both hidden layer and output activation I am using gradient descent to find local maximums in order to adjust the hidden nodes' weights and the output nodes' weights. I am certain in that I have this correct for sigmoid. I am less certain with softmax (or whether I can use gradient descent at all), after a bit of researching, I couldn't find the answer and decided to compute the derivative myself and obtained softmax'(x) = softmax(x) - softmax(x)^2 (this returns an column vector of size n). I have also looked into the MATLAB NN toolkit, the derivative of softmax provided by the toolkit returned a square matrix of size nxn, where the diagonal coincides with the softmax'(x) that I calculated by hand; and I am not sure how to interpret the output matrix. I ran each implementation with a learning rate of 0.001 and 1000 iterations of back propagation. However, my NN returns 0.2 (an even distribution) for all five output nodes, for any subset of the input dataset. My conclusions: o I am fairly certain that my gradient of descent is incorrectly done, but I have no idea how to fix this. o Perhaps I am not using enough hidden nodes o Perhaps I should increase the number of layers Any help would be greatly appreciated! The dataset I am working with can be found here (processed Cleveland): http://archive.ics.uci.edu/ml/datasets/Heart+Disease

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  • Artifical neural networks height-weight problem

    - by hammid1981
    i plan to use neurodotnet for my phd thesis, but before that i just want to build some small solutions to get used to the dll structure. the first problem that i want to model using backward propagation is height-weight ratio. I have some height and weight data, i want to train my NN so that if i put in some weight then i should get correct height as a output. i have 1 input 1 hidden and 1 output layer. Now here is first of many things i cant get around :) 1. my height data is in form of 1.422, 1.5422 ... etc and the corresponding weight data is 90 95, but the NN takes the input as 0/1 or -1/1 and given the output in the same range. how to address this problem

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  • Creating Java Neural Networks

    - by Tori Wieldt
    A new article on OTN/Java, titled “Neural Networks on the NetBeans Platform,” by Zoran Sevarac, reports on Neuroph Studio, an open source Java neural network development environment built on top of the NetBeans Platform. This article shows how to create Java neural networks for classification.From the article:“Neural networks are artificial intelligence (machine learning technology) suitable for ill-defined problems, such as recognition, prediction, classification, and control. This article shows how to create some Java neural networks for classification. Note that Neuroph Studio also has support for image recognition, text character recognition, and handwritten letter recognition...”“Neuroph Studio is a Java neural network development environment built on top of the NetBeans Platform and Neuroph Framework. It is an IDE-like environment customized for neural network development. Neuroph Studio is a GUI that sits on top of Neuroph Framework. Neuroph Framework is a full-featured Java framework that provides classes for building neural networks…”The author, Zoran Sevarac, is a teaching assistant at Belgrade University, Department for Software Engineering, and a researcher at the Laboratory for Artificial Intelligence at Belgrade University. He is also a member of GOAI Research Network. Through his research, he has been working on the development of a Java neural network framework, which was released as the open source project Neuroph.Brainy stuff. Read the article here.

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  • BlissControl Is a Settings Management Dashboard for Popular Social Networks

    - by Jason Fitzpatrick
    BlissControl is a simple web app that organizes the different settings menus of over a dozen social networks and services into a streamlined dashboard to help you change your profile pic, privacy settings, and more. Much like previously reviewed NotificationControl and MyPermissions (which help you check and set email notifications and app permissions, respectively), BlissControl also takes the very convoluted menus of web-apps and social media sites and makes them super easy to navigate. You can easily click right through the page you need on Facebook, Flickr, Twitter, and more–you’ll no longer need to visit each service and click through a maze of menus to get to the right place to change your password or swap your profile pic. BlissControl is simply a dashboard that directs you to the appropriate page within the service you already use–you never share your login credentials with BlissControl. Hit up the link below to take it for a spin. BlissControl [via AddictiveTips] How to Own Your Own Website (Even If You Can’t Build One) Pt 1 What’s the Difference Between Sleep and Hibernate in Windows? Screenshot Tour: XBMC 11 Eden Rocks Improved iOS Support, AirPlay, and Even a Custom XBMC OS

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  • how Computer Networks is related to Web/Desktop Java programming

    - by C4CodeE4Exe
    Being a Java programmer , I am wondering how could my work experience would help me learning networking skills. I know C language is used in network socket programming. I know if one knows how to program in one language its not tough to learn another language. Question is I am not able to find much on networks when it comes to Java(may be my knowledge is limited). Do companies like CISCO,TELUS Inc. rely heavily on programmers with such background.

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  • Boosting my GA with Neural Networks and/or Reinforcement Learning

    - by AlexT
    As I have mentioned in previous questions I am writing a maze solving application to help me learn about more theoretical CS subjects, after some trouble I've got a Genetic Algorithm working that can evolve a set of rules (handled by boolean values) in order to find a good solution through a maze. That being said, the GA alone is okay, but I'd like to beef it up with a Neural Network, even though I have no real working knowledge of Neural Networks (no formal theoretical CS education). After doing a bit of reading on the subject I found that a Neural Network could be used to train a genome in order to improve results. Let's say I have a genome (group of genes), such as 1 0 0 1 0 1 0 1 0 1 1 1 0 0... How could I use a Neural Network (I'm assuming MLP?) to train and improve my genome? In addition to this as I know nothing about Neural Networks I've been looking into implementing some form of Reinforcement Learning, using my maze matrix (2 dimensional array), although I'm a bit stuck on what the following algorithm wants from me: (from http://people.revoledu.com/kardi/tutorial/ReinforcementLearning/Q-Learning-Algorithm.htm) 1. Set parameter , and environment reward matrix R 2. Initialize matrix Q as zero matrix 3. For each episode: * Select random initial state * Do while not reach goal state o Select one among all possible actions for the current state o Using this possible action, consider to go to the next state o Get maximum Q value of this next state based on all possible actions o Compute o Set the next state as the current state End Do End For The big problem for me is implementing a reward matrix R and what a Q matrix exactly is, and getting the Q value. I use a multi-dimensional array for my maze and enum states for every move. How would this be used in a Q-Learning algorithm? If someone could help out by explaining what I would need to do to implement the following, preferably in Java although C# would be nice too, possibly with some source code examples it'd be appreciated.

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  • How to program a neural network for chess?

    - by marco92w
    Hello! I want to program a chess engine which learns to make good moves and win against other players. I've already coded a representation of the chess board and a function which outputs all possible moves. So I only need an evaluation function which says how good a given situation of the board is. Therefore, I would like to use an artificial neural network which should then evaluate a given position. The output should be a numerical value. The higher the value is, the better is the position for the white player. My approach is to build a network of 385 neurons: There are six unique chess pieces and 64 fields on the board. So for every field we take 6 neurons (1 for every piece). If there is a white piece, the input value is 1. If there is a black piece, the value is -1. And if there is no piece of that sort on that field, the value is 0. In addition to that there should be 1 neuron for the player to move. If it is White's turn, the input value is 1 and if it's Black's turn, the value is -1. I think that configuration of the neural network is quite good. But the main part is missing: How can I implement this neural network into a coding language (e.g. Delphi)? I think the weights for each neuron should be the same in the beginning. Depending on the result of a match, the weights should then be adjusted. But how? I think I should let 2 computer players (both using my engine) play against each other. If White wins, Black gets the feedback that its weights aren't good. So it would be great if you could help me implementing the neural network into a coding language (best would be Delphi, otherwise pseudo-code). Thanks in advance!

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  • Neural Net Optimize w/ Genetic Algorithm

    - by ServAce85
    Is a genetic algorithm the most efficient way to optimize the number of hidden nodes and the amount of training done on an artificial neural network? I am coding neural networks using the NNToolbox in Matlab. I am open to any other suggestions of optimization techniques, but I'm most familiar with GA's.

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  • whats the diference between train, validation and test set, in neural networks?

    - by Daniel
    Im using this library http://pastebin.com/raw.php?i=aMtVv4RZ to implement a learning agent. I have generated the train cases, but i dont know for sure what are the validation and test sets, the teacher says: 70% should be train cases, 10% will be test cases and the rest 20% should be validation cases. Thanks. edit i have this code, for training.. but i have no ideia when to stop training.. def train(self, train, validation, N=0.3, M=0.1): # N: learning rate # M: momentum factor accuracy = list() while(True): error = 0.0 for p in train: input, target = p self.update(input) error = error + self.backPropagate(target, N, M) print "validation" total = 0 for p in validation: input, target = p output = self.update(input) total += sum([abs(target - output) for target, output in zip(target, output)]) #calculates sum of absolute diference between target and output accuracy.append(total) print min(accuracy) print sum(accuracy[-5:])/5 #if i % 100 == 0: print 'error %-14f' % error if ? < ?: break

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  • Spiking neural networks

    - by lmsasu
    Hi all, which is the book one should start with in the domain of spiking neural networks? I know about Gerstner's "Spiking Neuron Models", published in 2002. Is there a more recent book, or maybe a more suitable one? I have a background in maths and artificial neural networks. If there are some good articles or overviews in this domain, also add them to the list. Thanks.

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