I am implementing a ANFIS model with hydrid training method. I have 13 inputs that maps to one output. I framed rules with respect to my system. When I train data, i get the below error:
Number of output MF's is not equal to number of rules
Any clues about where I am going wrong?
Quote from the Fuzzy Logic Toolbox User's Guide:
Constraints of anfis:
anfis is much more complex than the fuzzy
inference systems discussed so far, and is not available for all of
the fuzzy inference system options. Specifically, anfis only supports
Sugeno-type systems, and these must have the following properties:
* Have no rule sharing. Different rules cannot share the same output
membership function, namely the number of output membership functions
must be equal to the number of rules.
The ANFIS model structure should therefore look like this:
FYI the error comes from the following code snippet:
/* output MF no. must be the same as rule no. */
if (fis->output[0]->mf_n != fis->rule_n) {
fisFreeFisNode(fis);
PRINTF("Number of output MF's is not equal to number of rules -->\n");
fisError("Parameter sharing in FIS is not allowed!");
}
Related
I am wondering would the number of parameters in the models like ResNet18, Vgg16, and DenseNet201 would change if we change the input size to the model?
I did measure the number of parameters with the following command
pytorch_total_params = sum(p.numel() for p in model.parameters() if p.requires_grad)
Also, I have tried this snippet, and the number of parameters did not change for different input size
import torchvision.models as models
model= models.resnet18(pretrained = False)
model.cuda()
summary(model, (1,64,64))
No it would not. Parameters of a model have the purpose of processing the input as it propagates inside the network pipeline.
The parameters are mostly trained to serve their purpose, which is defined by the training task. Consider a increase in number of parameters based on the input? What would their values be? Would they be random? How would this new parameters with new values affect the inference of the model?
Such a sudden, random change to the fine-tuned, well-trained parameters of the model would be impractical. Maybe there are some other algorithms that I am unaware of, that change their parameter collection based on input. But the architectures that have been mentioned in question do not support such functionality.
Traninable parameters do not change with the change in input. If you see the weights in first layer of the model with the command list(model.parameters())[0].shape you can realize that it does not depend on the height and width of the input, but it depends on the number of channels(e.g Gray, RGB, HyperSpectral), which usually is very insignificant in bigger models. For further information about getting the input shape, you can see this toy example.
This is my first time working with DL4J (Deep Learning for Java) and also my first Convolutional Neural Network. My Goal is to use the Convolutional Neural Netowrk to give me some predicted values about an image. I gathered and labelled my images myself. The labels or expected outputs consist of two numbers between 0 and 1 (I just wrote them in the file name like 0.01x0.87.jpg).
Now I can't find any way to use the DataSetIterator Class which DL4J uses so that I can also set my label values.
Is there a simple way to tell DL4J that I want to train my Network to recognize that image 0.01x0.01.jpg should spit out the values 0.01 and 0.01?
What you want to do is usually known as regression. In contrast to classification where you want to either have a 0 or 1 output, in regression any value can be the target.
In your case, you will likely want to use a network architecture that uses either a sigmoid (which forces your values to be between 0 and 1) or an identity (which keeps the values as is, i.e. allows for them to be outside of the 0 to 1 range) activation function.
As you have two values that you are trying to predict, you will have to also define that you are using two outputs.
So much for your model architecture.
For data loading, you can use the ImageRecordReader, but also pass it a PathMultiLabelGenerator of your own. When you implement the PathMultiLabelGenerator interface, you will get the full path of the image as a string, and you can do whatever you want with it, like for example remove the file ending, split on x and parse your filename into a list of DoubleWritable. DoubleWritable is just a simple wrapper class for double so creating that is as easy as just instantiating it by passing the actual value to the constructor.
To create a dataset iterator you can now follow the documentation on RecordReaderDataSetIterator.
Please could you show me example of input file for FDBSCAN in ELKI. I got error like this:
Task failed
de.lmu.ifi.dbs.elki.data.type.NoSupportedDataTypeException: No data type found satisfying: UncertainObject,field
Available types: DBID DoubleVector,dim=2
at de.lmu.ifi.dbs.elki.database.AbstractDatabase.getRelation(AbstractDatabase.java:126)
at de.lmu.ifi.dbs.elki.algorithm.clustering.uncertain.FDBSCANNeighborPredicate.instantiate(FDBSCANNeighborPredicate.java:131)
at de.lmu.ifi.dbs.elki.algorithm.clustering.gdbscan.GeneralizedDBSCAN.run(GeneralizedDBSCAN.java:122)
at de.lmu.ifi.dbs.elki.algorithm.clustering.gdbscan.GeneralizedDBSCAN.run(GeneralizedDBSCAN.java:79)
at de.lmu.ifi.dbs.elki.workflow.AlgorithmStep.runAlgorithms(AlgorithmStep.java:105)
at de.lmu.ifi.dbs.elki.KDDTask.run(KDDTask.java:112)
at de.lmu.ifi.dbs.elki.application.KDDCLIApplication.run(KDDCLIApplication.java:61)
at [...]
FDBSCAN requires data of the type UncertainObject, i.e. objects with uncertainty information.
If you simply load a CSV file, the data will be certain, and you cannot use uncertain clustering.
There are several ways of modeling uncertainty. These implement as filters in the typeconversions package.
UncertainSplitFilter can split a vector of length k*N into k possible instances, each of length N with uniform weight.
WeightedUncertainSplitFilter is similar, but every instance can also have a weight associated.
UncertainifyFilter can simulate uncertainty by e.g. assuming a Gaussian or Uniform distribution around the original vector.
UniformUncertainifier (the U-Model, see Javadoc of UniformContinuousUncertainObject)
SimpleGaussianUncertainifier (see Javadoc of SimpleGaussianContinuousUncertainObject)
UnweightedDiscreteUncertainifier (BID Model, see Javadoc of WeightedDiscreteUncertainObject)
WeightedDiscreteUncertainifier (as above)
or add your own uncertainty information by extending the API!
I have been working on "Script identification from bilingual documents".
I want to classify the pages/blocks as either Eng(class 1), Hindi (class 2) or Mixed using libsvm in matlab. but the problem is that the training data i have consists of samples corresponding to Hindi and english pages/blocks only but no mixed pages.
The test data i want to give may consists of Mixed pages/blocks also, in that case i want it to be classified as "Mixed". I am planning to do it using confidence score or probability values. like if the prob value of class 1 is greater than a threshold (say 0.8) and prob value of class 2 is less than a threshold say(0.05) then it will be classified as class 1, and class 2 vice-versa. but if aforementioned two conditions dont satisfy then i want to classify it as "Mixed".
The third return value from the "libsvmpredict" is prob_values and i was planning to go ahead with this prob_values to decide whether the testdata is Hindi, English or Mixed. but at few places i learnt that "libsvmpredict" does not produce the actual prob_values.
Is there any way which can help me to classify the test data into 3 classes( Hindi, English, Mixed) using training data consisting of only 2 classes in SVM.
This is not the modus operandi for SVMs.
In no way SVMs can predict a given class without knowing it, without knowing how to separate such class from all other classes.
The function svmpredict() in LibSVM actually shows the probability estimates and the greater this value is, the more confident you can be regarding your prediction. But you cannot rely on such values if you have just two classes in order to predict a third class: indeed svmpredict() will return as many decision values as there are classes.
You can go on with your thresholding system (which, again, is not SVM-based) but it most likely fail or give bad performances. Think about that: you have to set up two thresholds and use them in a logic AND manner. The chance of correctly classified non-Mixed documents will indeed drastically decrease.
My suggestion is: instead of wasting time setting up thresholds, with a high chance of bad performances, join some of these texts together or create some new files with some Hindi and some English lines in order to add to your training data some proper Mixed documents and perform a standard 3-classes SVM system.
In order to create such files you can as well use Matlab, which has a pretty decent file I/O functions such as fread(), fwrite(), fprintf(), fscanf(), importdata() and so on...
I am have built a small text analysis model that is classifying small text files as either good, bad, or neutral. I was using a Support-Vector Machine as my classifier. However, I was wondering if instead of classifying all three I could classify into either Good or Bad but if the support for that text file is below .7 or some user specified threshold it would classify that text file as neutral. I know this isn't looked at as the best way of doing this, I am just trying to see what would happen if I took a different approach.
The operator Drop Uncertain Predictions might be what you want.
After you have applied your model to some test data, the resulting example set will have a prediction and two new attributes called confidence(Good) and confidence(Bad). These confidences are between 0 and 1 and for the two class case they will sum to 1 for each example within the example set. The highest confidence dictates the value of the prediction.
The Drop Uncertain Predictions operator requires a min confidence parameter and will set the prediction to missing if the maximum confidence it finds is below this value (you can also have different confidences for different class values for more advanced investigations).
You could then use the Replace Missing Values operator to change all missing predictions to be a text value of your choice.