I'm relatively new to Matlab ANN Toolbox. I am training the NN with pattern recognition and target matrix of 3x8670 containing 1s and 0s, using one hidden layer, 40 neurons and the rest with default settings. When I get the simulated output for new set of inputs, then the values are around 0 and 1. I then arrange them in descending order and choose a fixed number(which is known to me) out of 8670 observations to be 1 and rest to be zero.
Every time I run the program, the first row of the simulated output always has close to 100% accuracy and the following rows dont exhibit the same kind of accuracy.
Is there a logical explanation in general? I understand that answering this query conclusively might require the understanding of program and problem, but its made of of several functions to clearly explain. Can I make some changes in the training to get consistence output?
If you have any suggestions please share it with me.
Thanks,
Nishant
Your problem statement is not clear for me. For example, what you mean by: "I then arrange them in descending order and choose a fixed number ..."
As I understand, you did not get appropriate output from your NN as compared to the real target. I mean, your output from NN is difference than target. If so, there are different possibilities which should be considered:
How do you divide training/test/validation sets for training phase? The most division should be assigned to training (around 75%) and rest for test/validation.
How is your training data set? Can it support most scenarios as you expected? If your trained data set is not somewhat similar to your test data sets (e.g., you have some new records/samples in the test data set which had not (near) appear in the training phase, it explains as 'outlier' and NN cannot work efficiently with these types of samples, so you need clustering approach not NN classification approach), your results from NN is out-of-range and NN cannot provide ideal accuracy as you need. NN is good for those data set training, where there is no very difference between training and test data sets. Otherwise, NN is not appropriate.
Sometimes you have an appropriate training data set, but the problem is training itself. In this condition, you need other types of NN, because feed-forward NNs such as MLP cannot work with compacted and not well-separated regions of data very well. You need strong function approximation such as RBF and SVM.
Related
Consider the training process of deep FF neural network using mini-batch gradient descent. As far as I understand, at each epoch of the training we have different random set of mini-batches. Then iterating over all mini batches and computing the gradients of the NN parameters we will get random gradients at each iteration and, therefore, random directions for the model parameters to minimize the cost function. Let's imagine we fixed the hyperparameters of the training algorithm and started the training process again and again, then we would end up with models, which completely differs from each other, because in those trainings the changes of model parameters were different.
1) Is it always the case when we use such random based training algorithms?
2) If it is so, where is the guaranty that training the NN one more time with the best hyperparameters found during the previous trainings and validations will yield us the best model again?
3) Is it possible to find such hyperparameters, which will always yield the best models?
Neural Network are solving a optimization problem, As long as it is computing a gradient in right direction but can be random, it doesn't hurt its objective to generalize over data. It can stuck in some local optima. But there are many good methods like Adam, RMSProp, momentum based etc, by which it can accomplish its objective.
Another reason, when you say mini-batch, there is at least some sample by which it can generalize over those sample, there can be fluctuation in the error rate, and but at least it can give us a local solution.
Even, at each random sampling, these mini-batch have different-2 sample, which helps in generalize well over the complete distribution.
For hyperparameter selection, you need to do tuning and validate result on unseen data, there is no straight forward method to choose these.
I am new to image classification, currently working on SVM(support Vector Machine) method for classifying four groups of images by multisvm function, my algorithm every time the training and testing data are randomly selected and the performance is varies at every time. Some one suggested to do cross validation i did not understand why we need cross validation and what is the main purpose of this? . My actual data set consist training matrix size 28×40000 and testing matrix size 17×40000. how to do cross validation by this data set help me. thanks in advance .
Cross validation is used to select your model. The out-of-sample error can be estimated from your validation error. As a result, you would like to select the model with the least validation error. Here the model refers to the features you want to use, and of more importance, the gamma and C in your SVM. After cross validation, you will use the selected gamma and C with the least average validation error to train the whole training data.
You may also need to estimate the performance of your features and parameters to avoid both high-bias and high-variance. Whether your model suffers underfitting or overfitting can be observed from both in-sample-error and validation error.
Ideally 10-fold is often used for cross validation.
I'm not familiar with multiSVM but you may want to check out libSVM, it is a popular, free SVM library with support for a number of different programming languages.
Here they describe cross validation briefly. It is a way to avoid over-fitting the model by breaking up the training data into sub groups. In this way you can find a model (defined by a set of parameters) which fits both sub groups optimally.
For example, in the following picture they plot the validation accuracy contours for parameterized gamma and C values which are used to define the model. From this contour plot you can tell that the heuristically optimal values (from those tested) are those that give an accuracy closer to 84 instead of 81.
Refer to this link for more detailed information on cross-validation.
You always need to cross-validate your experiments in order to guarantee a correct scientific approach. For instance, if you don't cross-validate, the results you read (such as accuracy) might be highly biased by your test set. In an extreme case, your training step might have been very weak (in terms of fitting data) and your test step might have been very good. This applies to ALL machine learning and optimization experiments, not only SVMs.
To avoid such problems just divide your initial dataset in two (for instance), then train in the first set and test in the second, and repeat the process invesely, training in the second and testing in the first. This will guarantee that any biases to the data are visible to you. As someone suggested, you can perform this with even further division: 10-fold cross-validation, means dividing your data set in 10 parts, then training in 9 and testing in 1, then repeating the process until you have tested in all parts.
I used ntstool to create NAR (nonlinear Autoregressive) net object, by training on a 1x1247 input vector. (daily stock price for 6 years)
I have finished all the steps and saved the resulting net object to workspace.
Now I am clueless on how to use this object to predict the y(t) for example t = 2000, (I trained the model for t = 1:1247)
In some other threads, people recommended to use sim(net, t) function - however this will give me the same result for any value of t. (same with net(t) function)
I am not familiar with the specific neural net commands, but I think you are approaching this problem in the wrong way. Typically you want to model the evolution in time. You do this by specifying a certain window, say 3 months.
What you are training now is a single input vector, which has no information about evolution in time. The reason you always get the same prediction is because you only used a single point for training (even though it is 1247 dimensional, it is still 1 point).
You probably want to make input vectors of this nature (for simplicity, assume you are working with months):
[month1 month2; month2 month 3; month3 month4]
This example contains 2 training points with the evolution of 3 months. Note that they overlap.
Use the Network
After the network is trained and validated, the network object can be used to calculate the network response to any input. For example, if you want to find the network response to the fifth input vector in the building data set, you can use the following
a = net(houseInputs(:,5))
a =
34.3922
If you try this command, your output might be different, depending on the state of your random number generator when the network was initialized. Below, the network object is called to calculate the outputs for a concurrent set of all the input vectors in the housing data set. This is the batch mode form of simulation, in which all the input vectors are placed in one matrix. This is much more efficient than presenting the vectors one at a time.
a = net(houseInputs);
Each time a neural network is trained, can result in a different solution due to different initial weight and bias values and different divisions of data into training, validation, and test sets. As a result, different neural networks trained on the same problem can give different outputs for the same input. To ensure that a neural network of good accuracy has been found, retrain several times.
There are several other techniques for improving upon initial solutions if higher accuracy is desired. For more information, see Improve Neural Network Generalization and Avoid Overfitting.
strong text
I'm implementing a neural network for a supervised classification task in MATLAB.
I have a training set and a test set to evaluate the results.
The problem is that every time I train the network for the same training set I get very different results (sometimes I get a 95% classification accuracy and sometimes like 60%) for the same test set.
Now I know this is because I get different initial weights and I know that I can use 'seed' to set the same initial weights but the question is what does this say about my data and what is the right way to look at this? How do I define the accuracy I'm getting using my designed ANN? Is there a protocol for this (like running the ANN 50 times and get an average accuracy or something)?
Thanks
Make sure your test set is large enough compared to the training set (e.g. 10% of the overall data) and check it regarding diversity. If your test set only covers very specific cases, this could be a reason. Also make sure you always use the same test set. Alternatively you should google the term cross-validation.
Furthermore, observing good training set accuracy while observing bad test set accuracy is a sign for overfitting. Try to apply regularization like a simple L2 weight decay (simply multiply your weight matrices with e.g. 0.999 after each weight update). Depending on your data, Dropout or L1 regularization could also help (especially if you have a lot of redundancies in your input data). Also try to choose a smaller network topology (fewer layers and/or fewer neurons per layer).
To speed up training, you could also try alternative learning algorithms like RPROP+, RPROP- or RMSProp instead of plain backpropagation.
Looks like your ANN is not converging to the optimal set of weights. Without further details of the ANN model, I cannot pinpoint the problem, but I would try increasing the number of iterations.
I have created a neural network and the performance is good. By using nprtool, we are allow to test the network with an input data and target data. Here is my question, what is the purpose of testing a neural network with target data provided? Isn't it testing should not hav e target data so that we can know how well can the trained neural network perform without target data is given? Hope someone will respond to this, thanks =)
I'm not familiar with nprtool, but I suspect it would give the input data to your neural network, and then compare your NN's output data with the target data (and compute some kind of success rate based on that).
So your NN will never see the target data, it's just used to measure the performance.
It's like the "teacher's edition" of the exercise books in school. The student (i.e. the NN) doesn't have the solutions, but her/his answers will be compared against them by the teacher (i.e. nprtool). (Okay, the teacher probably/hopefully knows the subject, but you get the idea.)
The "target" data t is the desired y of y=net(x) used as example to train the network.
What nprtool do is to divide the training set into three groups: the training set, the validation set and the test set.
The first one is used to actually update the network.
The second one is used to determine the performances of the net (note: this set is NOT used in any way to update the network): as the NN "learns" the error (as difference between the t and net(x)) over the validation set decreases. The trend will eventually stop or even reverse: this phenomena is called "overfitting", which means the NN is now chasing the training set, "memorizing" it at the cost of the ability to generalize (meaning: to perform well with unseen data). So the purpose of this validation set is to determine when to stop the training before the NN starts overfitting. This should answer your question.
Finally third set is for external testing, to leave you a set of data untouched by the training procedure.
Even though the total data set [training, validation and testing] are inputs to the training algorithm, the testing data is in no way used to design (i.e., train and validate) the net
total = design + test
design = train + validate
The training data is used to estimate weights and biases
The validation data is used to monitor the design performance on nontraining data. REGARDLESS OF THE PERFORMANCE ON TRAINING DATA, if validation performance degrades continuously for 6 (default) epochs, training is terminated (VALIDATION STOPPING).
This mitigates the dreaded phenomenon of OVERTRAINING AN OVERFIT NET where performance on nontraining data degrades even if the training set performance is improving.
An overfit net has more unknown weights and biases than training equations, thereby allowing an infinite number of solutions. A simple example of overfitting with two unknowns but only one equation:
KNOWN: a, b, c
FIND: unique x1 and x2
USING: a * x1 + b * x2 = c
Hope this helps.
Greg