I am using Weisfeiler-Lehman Graph Kernels from here to get the precomputed kernel for the Scikit learn SVM see description.
At test time, what should be the format of my data? I'm really confused about that. See dimension requirements.
Thanks very much.
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I am interested in calibrating a binary probabilistic classifier in TFX. I was about to try doing it in standard Python externally to TFX, but then I found this piecewise linear calibration layer.
The description is a bit cryptic to me. Is this layer the sort of thing one could stack to the output layer of a TFX model and calibrate the output using recent y_true and y_pred?
If not, is there a standard way to do calibration in TFX?
Calibration of the data should be done prior to the data the transformation and classification.
The piecewise data is only applicable when the data coincides to regions of observed data.
We are not given enough information to properly answer this question.
I need to run k-means algorithm from Tensorflow in Go, i.e. cluster a graph intro subgraphs according to nodes similarity matrix.
I came across this article which shows an example on how to run a Keras trained model in Go. In this example the algo is of a supervised learning type. However in clustering algos, as I understand, there will be no model to save and export it to Go implementation.
The reason I am interested in Tensorflow, is because I think its code is optimized and will run much faster than k-mean implementation in Go, even with the scenario I described above.
I need an opinion of whether:
It is indeed impossible to use a Tensorflow k-mean algorithm in Go, and it is much better just to use k-means implemented in Go for this case.
It is possible to do this, and some sort of example or ideas on how to do this are very much appreciated.
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 am building a logistic regression model in tensorflow to approximate a function.
When I randomly select training and testing data from the complete dataset, I get a good result like so (blue are training points; red are testing points, the black line is the predicted curve):
But when I select the spatially seperate testing data, I get terrible predicted curve like so:
I understand why this is happening. But shouldn't a machine learning model learn these patterns and predict new values?
Similar thing happens with a periodic function too:
Am I missing something trivial here?
P.S. I did google this query for quite some time but was not able to get a good answer.
Thanks in advance.
What you are trying to do here is not related to logistic regression. Logistic regression is a classifier and you are doing regression.
No, machine learning systems aren't smart enough to learn to extrapolate functions like you have here. When you fit the model you are telling it to find an explanation for the training data. It doesn't care what the model does outside the range of training data. If you want it to be able to extrapolate then you need to give it extra information. You could set it up to assume that the input belonged to a sine wave or a quadratic polynomial and have it find the best fitting one. However, with no assumptions about the form of the function you won't be able to extrapolate.
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!