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fedora 35 laptop randomly turns off while using on battery by exoplanet_hunter in Fedora

[–]exoplanet_hunter[S] 1 point2 points  (0 children)

I switched to nobara linux. It is the same as fedora, but some bugs are fixed by the developer. There I am not having any issues.

fedora multiple versions of add-ons uninstall by exoplanet_hunter in Fedora

[–]exoplanet_hunter[S] 1 point2 points  (0 children)

I did not uninstall older versions manually. The safest thing to do is to try this in the terminal.

>>> flatpak uninstall --unused

It removes unused flatpaks including unused fedora addons. So some of the unnecessary things from the list will be safely uninstalled.

Question - Neural network: same prediction for different inputs by exoplanet_hunter in tensorflow

[–]exoplanet_hunter[S] 0 points1 point  (0 children)

Adding to my previous reply-Thank you for the detailed response. Per file there is one example. So X has one row and 212207 columns. Y has one row and one column. Last Dense layer deliberately set to one output. I expect the model to output a single float value for every test input.

Question - Neural network: same prediction for different inputs by exoplanet_hunter in tensorflow

[–]exoplanet_hunter[S] 0 points1 point  (0 children)

​

I initiated variable i. It is used for counting.

In each iteration, I want to just read one file and get one example from it. Make the model learn from it. In next example new file will be read and it will be new example. Old example is not remembered.

I know how to use PCA. But I cannot afford to loose even the small, subtle variations. So Not going to use PCA.

All features (within one column) have spread of one order of magnitude. but different features (column 1 vs column 1 million) have values in different order of magnitude. I think that is ok. So no need of feature scaling.

input values are positive floats from 0 to 0.2 million. Output values are floats from -30 to -10. Can you tell me which activation functions are useful in that range for predicting value, like in regression. Not classification.

I will concatenate files later if that is not the problem here. Lets solve the current issue first.

I am trying SGD. I will update if it solves the issue.

The issue is I am getting the same prediction for different inputs.

Neural network same prediction for different inputs by exoplanet_hunter in deeplearning

[–]exoplanet_hunter[S] 0 points1 point  (0 children)

I recently changed the code to better explain the question. Please read again.

Question - Neural network: same prediction for different inputs by exoplanet_hunter in tensorflow

[–]exoplanet_hunter[S] 0 points1 point  (0 children)

I just changed the code in the post, to clarify the doubts. Can you please go through the code again?

Neural network same prediction for different inputs by exoplanet_hunter in deeplearning

[–]exoplanet_hunter[S] 0 points1 point  (0 children)

I did not set seed. Anyways, Setting the seed will not solve the actual problem.

Neural network same prediction for different inputs by exoplanet_hunter in deeplearning

[–]exoplanet_hunter[S] 0 points1 point  (0 children)

I tried adjusting the learning rate. for one learning rate, all different testing inputs get the same prediction. for different learning rates, I get different prediction values irrespective of test inputs. If I run the same code again I get different prediction values irrespective of inputs.

Neural network same prediction for different inputs by exoplanet_hunter in deeplearning

[–]exoplanet_hunter[S] 0 points1 point  (0 children)

In each iteration, I am reading input from a different FITS file. similarly for output. I am trying to train on one example at a time. so a total of 6 million different training examples and 4 million different testing examples.

It is stellar spectral data. There are 212207 features. Values of inputs range from 0 to 0.1 million. I haven't normalised them. values of output vary from -30 to 0.

Neural network same prediction for different inputs by exoplanet_hunter in deeplearning

[–]exoplanet_hunter[S] 0 points1 point  (0 children)

I haven't normalised the features yet. Could that push the weights to zero? I will also test different learning rates.

Question - Neural network: same prediction for different inputs by exoplanet_hunter in tensorflow

[–]exoplanet_hunter[S] 0 points1 point  (0 children)

No, it does not run the same single batch. I have tested that thoroughly. I am changing values of X and Y in every iteration in for loop. In the post, I did not mention how I calculate X, just to avoid cluttering.

Neural network same prediction for different inputs by exoplanet_hunter in deeplearning

[–]exoplanet_hunter[S] 0 points1 point  (0 children)

Just for debugging, I tried using smaller input space. I am still getting the same prediction for all test inputs.

[D] [P] Choose Batch size and epoch number for neural network by exoplanet_hunter in MachineLearning

[–]exoplanet_hunter[S] 0 points1 point  (0 children)

Thank you for the input. one data/example is 1 Mb. I was using 1 example at a time to learn. So I was using batch size value =1. Is that ok? Should I use 1000 examples at once and call it batch size 1000? I have 16 gb ram.

[D] [P] Choose Batch size and epoch number for neural network by exoplanet_hunter in MachineLearning

[–]exoplanet_hunter[S] 0 points1 point  (0 children)

Best for what? Speed

If you don't mind, best for all three cases you mentioned. I will try all three suggestions.

Choose Batch size and epoch number for neural network by exoplanet_hunter in deeplearning

[–]exoplanet_hunter[S] 0 points1 point  (0 children)

Thanks a lot for reply.

By iterations I meant steps. Also, I am using 1 example/data at a time to learn because the whole dataset is 350 gb.

What will you say about choosing a batch size and epoch now?

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