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Combining data_pipeline and simple_example #7
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Hi there, Note that for big magnitutes of alpha mean of tte is same as the complex estimate using log etc Furthermore
OBS NOT TESTED:
But even better debug-mode initially is to simply transform the data to [n_non_masked_samples,1,n_features] (feed in only seen timesteps) to a simple ANN and when that works test the RNN. Would love to see forks! |
There's multiple reasons for NANs to show up but just found a very important:
I'm trying to fix it asap |
Hi Egil, Thanks for the update! Here's a fork with the notebook Combined_data_pipeline_and_analysis in examples/keras.
The last cell shows an example of training with just one input sequence. It does result in a non-NaN loss, although a very large one (but I didn't optimize the initial alpha or the network config much). Cheers, |
@NataliaVConnolly Sorry for the wait. It took me some time to figure out what was wrong!
Check out the new data_pipeline and let me know if you have more questions! :) |
- Check it out. - Poor performance atm - TODO will add masking/batchsize>1 support soon.
Hi Egil,
Thank you so much for making your code available! This is really great stuff.
So in trying to understand better how it all works I tried using the tensorflow.log-extracted data (as in your data_pipeline notebook) as inputs to the network (same config as in your simple_example). Unfortunately I got all nan's as losses:
Model summary:
Results of running model.fit:
I was wondering if you've tried doing the same experiment and if so, whether it worked for you? Thanks so much!
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