[Minor| Fix neural nets regressor shape #1589
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Model Benchmark
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Model training plots\n ## Model Training ### PeytonManning  ### YosemiteTemps  ### AirPassengers  ### EnergyPriceDaily  \n |
ourownstory
reviewed
Jun 21, 2024
| @@ -34,7 +34,7 @@ def __init__(self, config, id_list, quantiles, n_forecasts, device, config_trend | |||
| regressor_net.append(nn.Linear(d_inputs, self.d_hidden_regressors, bias=True)) | |||
| d_inputs = self.d_hidden_regressors | |||
| # final layer has input size d_inputs and output size equal to no. of forecasts * no. of quantiles | |||
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^ this comment may need updating.
Q: Is this NN truly applied separately to each observation and each forecast target?
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It's been applied for each batch layer by layer
ourownstory
requested changes
Jun 21, 2024
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If the removal of multiplying the outputs with n_forecasts addresses the problem, there may be more places where this needs to be addressed.
Essentially all of the files in components/future_regressors need to be screened for this, especially any with NN, but also auxiliary functions.
It seems like these code areas lack tests - we should add those, too.
… into smaller tests
ourownstory
approved these changes
Jun 21, 2024
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