I came across this Keras tutorial which is for a classification problem, but I'm trying to apply it to a regression one. Does anyone know why they change the dimension of the features? Has anybody used this for regression at all?
Also any link to a related tutorial would be appreciated.
Related
I have a regression model based on various independent features which eventually predict a value with a custom loss function. Somewhat similar to the link below.
https://www.evergreeninnovations.co/blog-quantile-loss-function-for-machine-learning/
The current model is built using Tensorflow library but now I want to use MXNet becuase of the speed and other advantages it provides. How to write a similar logic in MXNet with a custom loss function?
Simple regression with L2 loss is featured in 2 famous tutorials - you can just pick any of those and customize the loss:
In the D2L.ai book (used at many universities):
https://d2l.ai/chapter_linear-networks/linear-regression-gluon.html
In The Straight Dope (guide to the python API of MXNet,
gluon). A lot of that guide went into D2L.ai:
https://gluon.mxnet.io/chapter02_supervised-learning/linear-regression-gluon.html
In Tensorflow, you can either perform either classification or linear regression to train your inputs against the labels. Is it possible to perform some classification for your inputs (as pre-processing but not necessarily to use Tensorflow) and determine if you want to run the linear regression using Tensorflow?
For example in image denoising task, you have found that your linear regression algorithm can provide a good smoothing effect against the edges but in the meantime also remove the details for the texture objects. Therefore you would like to perform a binary classification to determine if an input is a texture object, and run the linear regression algorithm using Tensorflow; otherwise do nothing for texture object.
I understand Tensorflow supports transfer learning so I guess one of the possible solutions is to perform binary classification using Tensorflow, and transfer the "texture classification" knowledge to instruct Tensorflow to apply linear regression algorithm only when the input is a texture object? Please correct me if I am wrong as I am not too sure if the above task is do-able in Tensorflow (it would be great if you can describe how to do this in details if this is do-able :-) ).
I guess an alternative solution is to use some binary classification without Tensorflow, and filter out (remove) the texture inputs before passing them to Tensorflow.
Please kindly tell me if which of the above solution (or any other solution) is better (if do-able) for the above scenario? Any suggestions are welcome.
I've implemented the Neural Network using Tensorflow. During the implementation and training, I've found several not-so-trivial bugs.
Example: during the training I had same Mini-Batch loss for different steps/epochs, but different accuracy.
Now the neural network seems to be ready and working properly. I haven't managed to train it well yet, but I am working on it.
Anyway, I would like to check somehow that I haven't done any computational errors there. I am thinking about generating some artificial data for "fake" classification problem with lets say 4 features. The classification should have a very clear human-understandable dependency between the classification output and 4 features. The idea is to try to train the NN on it and see how it performs.
What do you think?
Stanford's c231n has a couple of general tips for this, like gradient checking.
If you're just learning neural networks, why don't you try to run your implementation on some known data? Many courses provide error and loss curves form models with specified hyperparameters, so you can check whether your implementation's behavior differs significantly from correct implementation.
Problem
Do you have a tutorial for LTSM or RNN time series anomaly detection using deep learning with CNTK? If not, can you make one or suggest a series of simple steps here for us to follow?
I am a software developer and a member of a team investigating using deep learning on time series data we have for anomaly detection. We have not found anything on your python docs that can help us. It seems most of the tutorials are for visual recognition problems and not specific to the problem domain of interest to us.
Using LTSM and RNN in Anomaly Detection
I have found the following
This link references why we are trying to use time series for anomaly detection
This paper convinced us that the first link is a respected approach to the problem in general
This link also outlined the same approach
I look around on CNTK here, but didn't find any similar question and so I hope this question helps other developers in the future.
Additional Notes and Questions
My problem is that I am finding CNTK not that simple to use or as well documented as I had hoped. Frankly, our framework and stack is heavy on .NET and Microsoft technologies. So I repeat the question again for emphasis with a few follow ups:
Do you have any resources you feel you can recommend to developers learning neural networks, deep learning, and so on to help us understand what is going on under the hood with CNTK?
Build 2017 mentions C# is supported by CNTK. Can you please point us in the direction of where the documentation and support is for this?
Most importantly can you please help get us unstuck on trying to do time series anomaly analysis for time series using CNTK?
Thank you very much for time and assistance in reading and asking this question
Thanks for your feedback. Your suggestions help improve the toolkit.
First Bullet
I would suggest that you can start with the CNTK tutorials.
https://github.com/Microsoft/CNTK/tree/master/Tutorials
They are designed from CNTK 101 to 301. Suggest that you work through them. Many of them even though uses image data, the concept and the models are amenable to build solutions with numerical data. 101-103 series are great to understand basics of the train-test-predict workflow.
Second Bullet:
Once you have trained the model (using Python recommended). The model evaluation can be performed using different language bindings, C# being one of them.
https://github.com/Microsoft/CNTK/wiki/CNTK-Evaluation-Overview
Third Bullet
There are different approaches suggested in the papers you have cited. All of them are possible to do in CNTK with some changes to the code in the tutorials.
The key tutorial for you would be CNTK 106, CNTK 105, and CNTK 202
Anomaly as classification: This would involve you label your target value as 1 of N classes, with one of the class being "anomaly". Then you can combine 106 with 202, to classify the prediction
Anomaly as an autoencoder: You can need to study 105 autoencoder. Now instead of a dense network, you could apply the concept for Recurrent networks. Train only on the normal data. Once trained, pass any data through the trained model. The difference between the input and autoencoded version will be small for normal data but the difference will be much larger for anomalies. The 105 tutorial uses images, but you can train these models with any numerical data.
Hope you find these suggestions helpful.
This is a newbie question for the tensorflow experts:
I reading lot of data from power transformer connected to an array of solar panels using arduinos, my question is can I use tensorflow to predict the power generation in future.
I am completely new to tensorflow, if can point me to something similar I can start with that or any github repo which is doing similar predictive modeling.
Edit: Kyle pointed me to the MNIST data, which I believe is a Image Dataset. Again, not sure if tensorflow is the right computation library for this problem or does it only work on Image datasets?
thanks, Rajesh
Surely you can use tensorflow to solve your problem.
TensorFlow™ is an open source software library for numerical
computation using data flow graphs.
So it works not only on Image dataset but also others. Don't worry about this.
And about prediction, first you need to train a model(such as linear regression) on you dataset, then predict. The tutorial code can be found in tensorflow homepage .
Get your hand dirty, you will find it works on your dataset.
Good luck.
You can absolutely use TensorFlow to predict time series. There are plenty of examples out there, like this one. And this is a really interesting one on using RNN to predict basketball trajectories.
In general, TF is a very flexible platform for solving problems with machine learning. You can create any kind of network you can think of in it, and train that network to act as a model for your process. Depending on what kind of costs you define and how you train it, you can build a network to classify data into categories, predict a time series forward a number of steps, and other cool stuff.
There is, sadly, no short answer for how to do this, but that's just because the possibilities are endless! Have fun!