Nonnegative predictions for deepar mxnet #2957
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lostella
reviewed
Aug 10, 2023
Improve readability on testing Co-authored-by: Lorenzo Stella <lorenzostella@gmail.com>
Hardcode nonnegative parameter in tests Co-authored-by: Lorenzo Stella <lorenzostella@gmail.com>
lostella
reviewed
Aug 10, 2023
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| if self.nonnegative_pred_samples: | ||
| return F.Activation(samples, act_type="relu") |
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I'm wondering if this works both in symbolic vs eager mode
Suggested change
| return F.Activation(samples, act_type="relu") | |
| return F.relu(samples) |
lostella
reviewed
Aug 10, 2023
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| dataset_train, dataset_test = datasets | ||
| predictor = estimator.train(dataset_train) | ||
| forecasts = list(predictor.predict(dataset_test)) |
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To ensure the added feature also works in symbolic mode, you can call as_symbol_block_predictor on the predictor object, see
gluonts/src/gluonts/mx/model/predictor.py
Line 316 in a818f69
lostella
approved these changes
Aug 11, 2023
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Issue #, if available:
Description of changes:
Add functionality to generate nonnegative prediction samples for DeepAR. This is applied only to final samples for prediction.
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