I know Matlab has the function TrainAutoencoder(input, settings) to create and train an autoencoder. The result is capable of running the two functions of "Encode" and "Decode".
But this is only applicable to the case of normal autoencoders. What if you want to have a denoising autoencoder? I searched and found some sample codes, where they used the "Network" function to convert the autoencoder to a normal network and then Train(network, noisyInput, smoothOutput)like a denoising autoencoder.
But there are multiple missing parts:
How to use this new network object to "encode" new data points? it doesn't support the encode().
How to get the "latent" variables to the features, out of this "network'?
I appreciate if anyone could help me resolve this issue.
Thanks,
-Moein
At present (2019a), MATALAB does not permit users to add layers manually in autoencoder. If you want to build up your own, you will have start from the scratch by using layers provided by MATLAB;
In order to to use TrainNetwork(...) to train your model, you will have you find out a way to insert your data into an object called imDatastore. The difficulty for autoencoder's data is that there is NO label, which is required by imDatastore, hence you will have to find out a smart way to avoid it--essentially you are to deal with a so-called OCC (One Class Classification) problem.
https://www.mathworks.com/help/matlab/ref/matlab.io.datastore.imagedatastore.html
Use activations(...) to dump outputs from intermediate (hidden) layers
https://www.mathworks.com/help/deeplearning/ref/activations.html?searchHighlight=activations&s_tid=doc_srchtitle
I swang between using MATLAB and Python (Keras) for deep learning for a couple of weeks, eventually I chose the latter, albeit I am a long-term and loyal user to MATLAB and a rookie to Python. My two cents are that there are too many restrictions in the former regarding deep learning.
Good luck.:-)
If you 'simulation' means prediction/inference, simply use activations(...) to dump outputs from any intermediate (hidden) layers as I mentioned earlier so that you can check them.
Another way is that you construct an identical network but with the encoding part only, copy your trained parameters into it, and feed your simulated signals.
Related
I am implementing a fuzzy logic based decision support system that uses nine variables but group each three together to form an output then take these three output to make the final output of the system.
I am using fuzzy logic toolbox in matlab, I made each one of these three outputs but I can't figure out how I can make these outputs as inputs again for the final output.
The system is shown in this picture:
system picture
Correct me if I'm wrong, but I think you would need two FIS separately , where the inputs from the second one will be the outputs from the first one.
Sorry If I didn't help much :/
I am trying to build a complex neural network using Computation Graph implementation in Deeplearning4J. I need to have multiple outputs so that's why I can't go with the generic MultiLayerConfiguration.
However, my problem is that in this case I do not know how to do the evaluation of my model and I would like at least to know the accuracy.
Has anybody worked with Comp Graphs in dl4j?
First of all yes: tons of people use computation graph. They usually start from our existing examples though and tend to mainly use it for things like seq2seq.
As for your question on evaluation, it's conceptually the same as multi layer network. How you evaluate is likely going to be task specific though. If you think about where evaluation happens, it's always tied to a task (classification,regression,binary classification,..) with an output layer . In the most common case usually you only have 1 output which outputs a classification. In that case you can just use the first array it outputs.
Otherwise for multiple outputs..you'd have to define what you're evaluating. Usually tasks merge to 1 path.
If they don't, you'd have multiple output layers where you want to do an evaluation object per output.
Computation graphs and multi layer network both use a .output method to give you raw arrays. That is typically what you pass to eval.eval.
I am dealing with multiple people tracking using single camera in Matlab, and need to calculate multiple objects tracking performance metrics which are MOTA, MOTP, FP and FN.
Is it possible to calculate them using ''classperf'' function which is found in Matlab? or there is another way?
Many thanks
If you need MATLAB implementation, go for motchallenge.net (multiple objects tracking benchmark) devkit: https://bitbucket.org/amilan/motchallenge-devkit.
There is also an excellent and easier to use Python implementation by Christoph Heindl: https://github.com/cheind/py-motmetrics. In both cases you will get along MOTA and MOTP also more powerful ID-measures.
I am trying to make a classification with spark-mllib, especially using RandomForestModel.
I have taken a look on this example from spark (RandomForestClassificationExample.scala), but I need a somewhat expanded approach.
I need to be able to train a model, save the model for future usage, but also to be able to load it and train further. Like, extend the dataset and train again.
I completely understand the need to export and import a model for future usage.
Unfortunately, training "further" isn't possible with Spark nor does it make sense. Thus it's recommended to retrain the model with the data from use to train the first model + new data.
Your first training values/metrics don't have much sense anymore if you want to add more data (e.g features, intercept, coefficients, etc.)
I hope that this answers your question.
You may need to look for some reinforcement learning technique instead of Random Forest if you want to use the old model and retrain it with new data.
That I know, there's deeplearning4j that implements deep reinforcement learning algorithms on top of Spark (and Hadoop).
If you only need to save JavaRDD[Object], you can do (in Java)
model.saveAsObjectFile()
Values will be writter out using Java Serialization. Then, to read your data you do:
JavaRDD[Object] model = jsc.objectFile(pathOfYourModel)
Be careful, object files are not available in Python. But you could use saveAsPickleFile() to write your model and pickleFile() to read it.
I thought this should be a simple task, I just can't find the way to do it:
I am using 'imregister' (MATLAB) to register two medical X-ray images.
To ensure I get the best registration outcome as possible, I use some image processing techniques such as contrast enhancement, blackening of objects that are different between images and even cropping.
The outcome of this seems to be quite satisfying.
Now, I want to perform the exact same registaration on the original images, so that I can display the two ORIGINAL images automatically in alignment.
I think that an output parameter such as tform serves this purpose of performing a certain registration on any two images, but unfortunately 'imregister' does not provide such a parameter, as far as I know.
It does provide as an output the spatial referencing object R_reg which might be the answer, but I still haven't figured out how to use it to re-preform the registration.
I should mention that since I am dealing with medical X-ray images on which non of the feature-detecting algorithms seem to work well enough to perform registration, I can only use intensity-based (as opposed to feature-based) registration, and therefore am using 'imregister'.
Does anyone know how I can accomplish this?
Thanks!
Noga
So to make an answer out of my comment, there are 2 things you can do depending on the Matlab release you are using:
Option 1: R2013a and earlier
I suggest modifying the built-in imregister function by forcing tform to be an output and save that function under another name.
For example:
function [movingReg,Rreg,tform] = imregister2(varargin)
save that, add it to your path and you're good to go. If you type edit imregister you will notice that the 1st line calls imregtform to get the geometric transformation required, while the last line calls imwarp, to apply that geometric transformation. Which leads us to Option 2.
Option 2: R2013b and later
Well in that case you can directly use imregtform to get the tform object and then use imwarpto apply it. Easy isn't it?
Hope that makes things clearer!