I have created a couple of models in MATLAB Neural Network Toolbox with a hundred of inputs and 10 outputs that correspond to different classes. At the end, NN gives a plot regarding a performance which is a number. What does this measure correspond to? Is it sum of errors from each outputs?
How can I know if NN is classifying well?
The performance metric depends on the performance function set in the neural network toolbox parameters. For instance, if performFcn is 'mse' then it will use the mean square error as the performance metric. See http://uk.mathworks.com/help/nnet/ug/analyze-neural-network-performance-after-training.html for more information on how Matlab sets these parameters. In general, when using anything like Neural Networks, it is important to understand what it is trying to optimise, and how, to avoid problems such as overfitting. There are a lot of parameters to tune!
Have a look at this answer for more detailed information
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I am working on a regression model with a set of 158 inputs and 4 outputs of glass manufacturing project which is a continuous process of inputs and outputs. Is the usage of Neural Net a suitable solution for such kind of regression models? If yes, I have understood that Recurrent Neural Nets can be used for time series data, which Recurrent Neural Net shall I use? If usage of NN is not suitable, what are the other types of solutions available other than Linear Regression and Regression Trees?
Neural Networks are indeed suitable for continuous data. In fact, it is continous by default I would say. It is possible to have discrete I/O for sure, it all depend on your functions.
Secondly, it is true that RNN are suitable for time series, in a way. RNN are in fact suitable for timesteps more than timestamps. RNN are working by iterations. Typically, each iteration can be seen as a fixed step forward in time. This said, if you data is more like (date, value) (what I call timestamp), it may not be so good. It would not be absolutely impossible, but that's not the idea.
Hope it helps, start with simple RNN, try to understand how it works, then, if you need more, read about more complex cells.
I'm developing a project for the university. I have to create a classifier for a disease. The data-set i have contains several inputs (symptoms) and each of them is associated to a multiplicative probability factor (e.g. if patient has the symptom A, he has a double probability to have that disease).
So, how can i do this type of classifier? Is there any type of neural network or other instrument to do this??
Thanks in advance
You should specify how much labeled data and unlabeled data you have.
Let's assume you have only labeled data. Then you could use neural networks, but IMHO, SVM or random forests are the best techniques for a first try.
Note that if you use machine learning techniques, your prior information about symptoms (multiplicative coefficients) are not used because the labels are used instead. If you want to use these coefficients, it's no more machine learning.
You can use neural network for this purpose also. If to speak about your situation, with binding symptom A to more chances for decease B, that is what neural network should be able to accomplish. To bind connection weights from input A ( symptom A ) to desease B. From your side, you can engrain such classification rule in case if you'll have enough training data in your training data set. Also I propose you to try two different approaches: 1. neural network with N outputs (N = number of deseases to clasif). 2. Create for each desease neural network.
I've recently been delving into artificial neural networks again, both evolved and trained. I had a question regarding what methods, if any, to solve for inputs that would result in a target output set. Is there a name for this? Everything I try to look for leads me to backpropagation which isn't necessarily what I need. In my search, the closest thing I've come to expressing my question is
Is it possible to run a neural network in reverse?
Which told me that there, indeed, would be many solutions for networks that had varying numbers of nodes for the layers and they would not be trivial to solve for. I had the idea of just marching toward an ideal set of inputs using the weights that have been established during learning. Does anyone else have experience doing something like this?
In order to elaborate:
Say you have a network with 401 input nodes which represents a 20x20 grayscale image and a bias, two hidden layers consisting of 100+25 nodes, as well as 6 output nodes representing a classification (symbols, roman numerals, etc).
After training a neural network so that it can classify with an acceptable error, I would like to run the network backwards. This would mean I would input a classification in the output that I would like to see, and the network would imagine a set of inputs that would result in the expected output. So for the roman numeral example, this could mean that I would request it to run the net in reverse for the symbol 'X' and it would generate an image that would resemble what the net thought an 'X' looked like. In this way, I could get a good idea of the features it learned to separate the classifications. I feel as it would be very beneficial in understanding how ANNs function and learn in the grand scheme of things.
For a simple feed-forward fully connected NN, it is possible to project hidden unit activation into pixel space by taking inverse of activation function (for example Logit for sigmoid units), dividing it by sum of incoming weights and then multiplying that value by weight of each pixel. That will give visualization of average pattern, recognized by this hidden unit. Summing up these patterns for each hidden unit will result in average pattern, that corresponds to this particular set of hidden unit activities.Same procedure can be in principle be applied to to project output activations into hidden unit activity patterns.
This is indeed useful for analyzing what features NN learned in image recognition. For more complex methods you can take a look at this paper (besides everything it contains examples of patterns that NN can learn).
You can not exactly run NN in reverse, because it does not remember all information from source image - only patterns that it learned to detect. So network cannot "imagine a set inputs". However, it possible to sample probability distribution (taking weight as probability of activation of each pixel) and produce a set of patterns that can be recognized by particular neuron.
I know that you can, and I am working on a solution now. I have some code on my github here for imagining the inputs of a neural network that classifies the handwritten digits of the MNIST dataset, but I don't think it is entirely correct. Right now, I simply take a trained network and my desired output and multiply backwards by the learned weights at each layer until I have a value for inputs. This is skipping over the activation function and may have some other errors, but I am getting pretty reasonable images out of it. For example, this is the result of the trained network imagining a 3: number 3
Yes, you can run a probabilistic NN in reverse to get it to 'imagine' inputs that would match an output it's been trained to categorise.
I highly recommend Geoffrey Hinton's coursera course on NN's here:
https://www.coursera.org/course/neuralnets
He demonstrates in his introductory video a NN imagining various "2"s that it would recognise having been trained to identify the numerals 0 through 9. It's very impressive!
I think it's basically doing exactly what you're looking to do.
Gruff
Recently, I am trying to using Matlab build-in neural networks toolbox to accomplish my classification problem. However, I have some questions about the parameter settings.
a. The number of neurons in the hidden layer:
The example on this page Matlab neural networks classification example shows a two-layer (i.e. one-hidden-layer and one-output-layer) feed forward neural networks. In this example, it uses 10 neurons in the hidden layer
net = patternnet(10);
My first question is how to define the best number of neurons for my classification problem? Should I use cross-validation method to get the best performed number of neurons using a training data set?
b. Is there a method to choose three-layer or more multi-layer neural networks?
c. There are many different training method we can use in the neural networks toolbox. A list can be found at Training methods list. The page mentioned that the fastest training function is generally 'trainlm'; however, generally speaking, which one will perform best? Or it totally depends on the data set I am using?
d. In each training method, there is a parameter called 'epochs', which is the training iteration for my understanding. For each training method, Matlab defined the maximum number of epochs to train. However, from the example, it seems like 'epochs' is another parameter we can tune. Am I right? Or we just set the maximum number of epochs or leave it as default?
Any experience with Matlab neural networks toolbox is welcome and thanks very much for your reply. A.
a. You can refer to How to choose number of hidden layers and nodes in neural network? and ftp://ftp.sas.com/pub/neural/FAQ3.html#A_hu
Surely you can do cross-validation to determine the parameter of best number of neurons. But it's not recommended as it's more suitable to use it in the stage of weights training of a certain network.
b. Refer to ftp://ftp.sas.com/pub/neural/FAQ3.html#A_hl
And for more layers of neural network, you can refer to Deep Learning, which is very hot in recent years and gets state-of-the-art performances in many of the pattern recognition tasks.
c. It depends on your data. trainlm performs better on function fitting (nonlinear regression) problems than on pattern recognition problems while training large networks and pattern recognition networks, trainscg and trainrp are good choices. Generally, Gradient Descent and Resilient Backpropagation is recommended. More detailed comparison can be found here: http://www.mathworks.cn/cn/help/nnet/ug/choose-a-multilayer-neural-network-training-function.html
d. Yes, you're right. We can tune the epochs parameter. Generally you can output the recognition results/accuracy at every epoch and you will see that it is promoting more and more slowly, and the more epochs the more computing time. You can make a compromise between the accuracy and computation time.
For part b of your question:
You can use like this code:
net = patternnet([10 15 20]);
This script create a network with 3 hidden layer that first layer has 10 neurons, second layer has 15 neurons and 3th layer has 20 neurons.
I'm trying to build an app to detect images which are advertisements from the webpages. Once I detect those I`ll not be allowing those to be displayed on the client side.
Basically I'm using Back-propagation algorithm to train the neural network using the dataset given here: http://archive.ics.uci.edu/ml/datasets/Internet+Advertisements.
But in that dataset no. of attributes are very high. In fact one of the mentors of the project told me that If you train the Neural Network with that many attributes, it'll take lots of time to get trained. So is there a way to optimize the input dataset? Or I just have to use that many attributes?
1558 is actually a modest number of features/attributes. The # of instances(3279) is also small. The problem is not on the dataset side, but on the training algorithm side.
ANN is slow in training, I'd suggest you to use a logistic regression or svm. Both of them are very fast to train. Especially, svm has a lot of fast algorithms.
In this dataset, you are actually analyzing text, but not image. I think a linear family classifier, i.e. logistic regression or svm, is better for your job.
If you are using for production and you cannot use open source code. Logistic regression is very easy to implement compared to a good ANN and SVM.
If you decide to use logistic regression or SVM, I can future recommend some articles or source code for you to refer.
If you're actually using a backpropagation network with 1558 input nodes and only 3279 samples, then the training time is the least of your problems: Even if you have a very small network with only one hidden layer containing 10 neurons, you have 1558*10 weights between the input layer and the hidden layer. How can you expect to get a good estimate for 15580 degrees of freedom from only 3279 samples? (And that simple calculation doesn't even take the "curse of dimensionality" into account)
You have to analyze your data to find out how to optimize it. Try to understand your input data: Which (tuples of) features are (jointly) statistically significant? (use standard statistical methods for this) Are some features redundant? (Principal component analysis is a good stating point for this.) Don't expect the artificial neural network to do that work for you.
Also: remeber Duda&Hart's famous "no-free-lunch-theorem": No classification algorithm works for every problem. And for any classification algorithm X, there is a problem where flipping a coin leads to better results than X. If you take this into account, deciding what algorithm to use before analyzing your data might not be a smart idea. You might well have picked the algorithm that actually performs worse than blind guessing on your specific problem! (By the way: Duda&Hart&Storks's book about pattern classification is a great starting point to learn about this, if you haven't read it yet.)
aplly a seperate ANN for each category of features
for example
457 inputs 1 output for url terms ( ANN1 )
495 inputs 1 output for origurl ( ANN2 )
...
then train all of them
use another main ANN to join results