Comments (5)
Are you able to provide more details on this one? Specially, are you suggesting to modify the forward pass
via neuralhydrology's customlstm
as defined here?
Thank you in advance, cheers!
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I think the two options are 1) change the way the forward pass is made, yes, but not through NH, do that through the BMI, and 2) change the way the model is trained with NH to match the way we have implemented it. I think option 1 is much more reasonable, for the short term. In the long term, the thing to do is develop a BMI directly in the NH code, so there is no potential for conflict between training and forward ngen predictions.
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@jmframe I'm digging up old issues today. What are the advantages to resetting the state space each time step? It seems unlikely we'd want to multiply the current execution time by the sequence length unless some dramatic performance increases (i.e. better streamflow prediction) resulted. At some point, given time resources, we could retrain LSTM to eliminate the state resetting per your second recommendation.
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Well. The advantage of resetting the state space and passing in the full sequence is that is how the model is trained, and that is what the weights of the model are trained to respond to. But, I did quite a bit of experimenting, and didn't find that it was much different, results-wise. The issue of computation and time constraints is an import one. It would be good to evaluate the performance of the large runs to see what this means in terms of extra costs.
There is a funded CIROH project to do all this, which was supposed to start in 2022, but you know the story...
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That's good to know @jmframe. I'll leave this issue open.
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Related Issues (20)
- Decide on an output unit, and make output available in get_value HOT 1
- Add functions for read_initial_state and save_current_state HOT 4
- put serialization example in a .py file HOT 2
- Get rid of the second basin area variable HOT 17
- Bound the runoff to zero HOT 10
- Temperature should actually be in kelvin HOT 2
- Ensure correct implementations for getter and setter functions HOT 2
- Using newly trained lstm models HOT 7
- Config file path should be type string HOT 1
- Simulated output in jupyter notebooks
- csdms standard name for precip HOT 2
- Broken link to example configuration file HOT 1
- GH Actions Fail for py v3.7 HOT 13
- Add ngen integration workflow to gh actions HOT 3
- Set_value functionality is mostly commented out. HOT 18
- Compatibility with heat model example from CSDMS HOT 7
- Unit Test Workflow Fails; NumPy v2.0
- Initialize all the possible forcings, and don't hard code any specific forcings. HOT 1
- set_values_dictionary is redundant, get rid of it and organize input values from _values HOT 1
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