I am a beginner with Tensorflow and machine learning in general.For my project I have to classify urban sound data.
I have extracted mfccs of my sample data and now I want to classify them by using a CNN in Tensorflow. I don't know how many channels I should use and why. Can anyone help me? Thanks.
I was working on a similar project and I found this paper useful with this.
http://karol.piczak.com/papers/Piczak2015-ESC-ConvNet.pdf
Related
I want to retrain a pre-trained model with two classes of the same object with and without an extra part on the object. So it has to recognize a detail.
I am a beginner in this area and couldn't find a solution, maybe you even know a better solution than working with TensorFlow.
I hope you can help me and I am happy about any idea. Thank you in advance.
I followed the tutorial of adanet:
https://github.com/tensorflow/adanet/tree/master/adanet/examples/tutorials
and was able to apply adanet to my own binary classification problem.
But how can I predict using the train model? I have a very little knowledge of TensorFlow. Any helps would be really appreciated
You can immediately call estimator.predict on a new unlabeled example and get a prediction.
Keras has preprocessing.image.flow_from_directory() to read the gray scale and rgb image formats. Is there some way i can read HDR images with 4 channels ('rgbe') using keras or similar library? Any ideas will be appreciated.
Thank you in advance.
The function preprocessing.image.flow_from_directory() is a very powerful one. Sadly it has only the two modes you mentioned. I would suggest you two things since there is not a similar library that could work for you:
Go from RGBE to RGB and use preprocessing.image.flow_from_directory()
Checkout this Github link. They talk about keras having preprocessing with 4 channels, I suggest you update keras.
If you want to use the E value, because you think it will have importance in your net, just build your own reader. This might help.
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!