I am trying to train a multilayer neural network in rapidminer.
The key to my problem is that I have three nodes on my output layer that I want predict:
(y1,y2,y3) = f(x1,x2,x3) with one hidden node.
As far as I understood, Rapidminer does not allow to assign a "special role" (e.g. label, id, prediction) to more than one variable.
I am assuming an output layer containing more than one node must be possible to model but how is this done?
Several posts recommended using the "setRole" operator - can you give me any hints on that if it is helpful?
Thank you,
Mat
You can create multiple labels as long as the types are of the form label1, label2 etc. Use the Generate Multi-Label Data operator to see an example.
Then use the Loop Labels operator and place the neural network operator within it. This will build as many models as there are labels.
Related
I cropped the following image from a tutorial.
this diagram shows a rough structure of a standard neural network. takes one image as input and make a prediction.
what I am thinking about is some kind of parallel structure. think about something like the following image.
not exactly as in the above image. But you can see I am trying to use two images to make one prediction. this image is for you to get an idea about what I am trying to ask.
is it possible to use more than one (two, three ..) images like this or any other way in order to make one prediction. now, this is not to be used in actual photo classification. But I think such a technique can be used in a file like audio classification where a graphical representation of data is used with image classification techniques.
any advice, guidance or opinion on this?
if we consider implementing exactly what is in the diagram, if I use a high-level API like Keras (Keras.model.sequential) all we can do is keep adding a layer one after the other.
so what kind of technology can I use to implement the parallel structure
Yes, you can use more than one image as input. See for example the Siamese Neural Network which takes as input 2 images and passes them through a shared network architecture.
If instead you want to have an arbitrary and variable number of images as input you can use an architecture based on Recurrent Neural Networks like Convolutional LSTM, which essentially applies a CNN to every image of the input sequence using an LSTM recurrent network.
I want to create a deep classification net, but my classes aren't mutually exclusive (that is what sofmaxlayer do).
Is it possible to define a non mutually exclusive classification layer (i.e., a data can be in more than one class)?
One way to do it, it would be with a logsig function in the classification layer, instead of a softmax, but I have no idea how to acomplish that....
In CNN you can have multiple class in last layer as you know. But if I understand correctly your need in last layer an out put with that is in a range of numbers instead of 1 or 0 for each class. Its mean you need regression. If your labels support this task it's OK and you can do it with regression just like what happen in bounding box regression for localization. And you don't need soft-max in last layer. just use other activation functions that produce sufficient out put for your task.
I am currently using Mallet for training a sequential model using CRF. I have understood how to provide features (that solely depend on input sequence) to the mallet package. Based on my understanding, in mallet, we have to compute all the values of the feature functions (upfront). Now, I would like to use indicator functions that depend on the label of a token. The value of these functions depends on the output label sequence and during training, I can compute the values of these indicator functions as the output label sequence is known. But, when I am applying this trained CRF model on a new input (whose output label sequene is unknown), how should I calculate the values for such features.
It will be very helpful to me if anyone can provide me any tips/relevant documents.
As you've phrased it, the question doesn't make sense: if you don't know the hidden labels, you can't set anything based on those unknown labels. An example might help.
You may not need to explicitly record these relationships. At training time the algorithm sets the parameters of the CRF to represent the relationship between the observed features and the unobserved state. Different CRF architectures can allow you to add dependencies between multiple hidden states.
I want to costruct a neural network which will be trained based on data i create. My question is what form these data should have? In other words does keras allow neural networks that take strings/characters as input? If not, and only is able to accept numbers in what range should the input/output be?
The only condition for your input data i.e features, is that it should be numerical. There isn't really any constraint on range but it's always a good idea to do Feature Scaling, Normalization etc to make sure that our model won't get confused. Neural Networks or other machine learning methods cannot accept string (characters, words) directly, therefore, you need to first convert string to numbers. There are many ways to do that, most common techniques include Bag of Words, tf-idf features, word embeddings etc.
Following tutorials (using scikit) might be a good starting point:
http://scikit-learn.org/stable/tutorial/text_analytics/working_with_text_data.html
https://www.kaggle.com/c/word2vec-nlp-tutorial/details/part-1-for-beginners-bag-of-words
Self organizing maps are more suited for clustering(dimension reduction) rather than classification. But SOM's are used in Linear vector quantization for fine tuning. But LVQ is a supervised leaning method. So to use SOM's in LVQ, LVQ should be provided with a labelled training data set. But since SOM's only do clustering and not classification and thus cannot have labelled data how can SOM be used as an input for LVQ?
Does LVQ fine tune the clusters in SOM?
Before using in LVQ should SOM be put through another classification algorithm so that it can classify the inputs so that these labelled inputs maybe used in LVQ?
It must be clear that supervised differs from unsupervised because in the first the target values are known.
Therefore, the output of supervised models is a prediction.
Instead, the output of unsupervised models is a label for which we don't know the meaning yet. For this purpose, after clustering, it is necessary to do the profiling of each one of those new label.
Having said so, you could label the dataset using an unsupervised learning technique such as SOM. Then, you should profile each class in order to be sure to understand the meaning of each class.
At this point, you can pursue two different path depending on what is your final objective:
1. use this new variable as a way for dimensionality reduction
2. use this new dataset featured with the additional variable representing the class as a labelled data that you will try to predict using the LVQ
Hope this can be useful!