I seriously searched hard for a Particle Swarm Optimization example using Encoq 3.2 C# version. I would be very appreciative if someone could share one. My array type is continuous.
Thanks,
Dan Hickman
Encog uses Particle Swarm (PSO) as a means of training a neural network. So it is really just another trainer that can be swapped in for backprop, rprop and the others. There is an example included:
https://github.com/encog/encog-dotnet-core/blob/master/ConsoleExamples/Examples/XOR/XORPSO.cs
Related
I need hyperparameter optimization for Lstm.I will use meta-heuristic i.e particle swarm optimization or artificial bee colony algorithms but ı have no idea how to code,ı decided to use framework.ı could not find framework that works with metaheuristic for Lstm.May you help me? I am using nasa turbofan dataset.
I am coding on google colab
Knowing that Tensorflow is good for working with matrices, would I be able to use Tensorflow to create a cellular automata? And would this offer a great deal of speed over just coding it in Python?
Are there any tutorials or websites that could point me in the right direction to use Tensorflow for more general purpose computing than machine learning (for example, simulations)?
If so, could someone help point me in the right direction to the type of Tensorflow commands I would need to learn to make this program? Thanks!
A TensorFlow implementation is likely to offer an improvement in execution time, especially if executed by GPU, since CA can be executed in parallel. See: https://cs.stackexchange.com/a/320/67726.
A starting point for TensorFlow in general might be the official guide and documentation, which do go beyond just machine learning. Also available are two tutorials on non-ML examples: Mandelbrot Set, Partial Differential Equations.
While TensorFlow is usually mentioned in the context of machine learning, it is worth noting that:
TensorFlow™ is an open source software library for high performance
numerical computation. Its flexible architecture allows easy
deployment of computation across a variety of platforms (CPUs, GPUs,
TPUs), and from desktops to clusters of servers to mobile and edge
devices.
Edit: here's an implementation and a tutorial about Conway's Game of Life using TF.
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!
I have just started experimenting with Deep Learning and Computer Vision technologies. I came across this awesome tutorial. I have setup the TensorFlow environment using docker and trained my own sets of objects and it provided greater accuracy when I tested it out.
Now I want to make the same more real-time. For example, instead of giving an image of an object as the input, I want to utilize a webcam and make it recognize the object with the help of TensorFlow. Can you guys guide me with the right place to start with this work?
You may want to look at TensorFlow Serving so that you can decouple compute from sensors (and distribute the computation), or our C++ api. Beyond that, tensorflow was written emphasizing throughput rather than latency, so batch samples as much as you can. You don't need to run tensorflow at every frame, so input from a webcam should definitely be in the realm of possibilities. Making the network smaller, and buying better hardware are popular options.