Comments (2)
You'd get model.statedict()
and save it with torch.save(...)
. Ideally, just save the model if it was of greater accuracy than any epoch before. One approach is to wrap the statedict
in an outer dictionary that also contains this value.
Load the statedict
with torch.load(...)
and then into the model with model.load_state_dict(...)
. Hope that helps. I might bolt that very necessary feature on one day ;)
from pytorch-retraining.
Sounds good approach. I see this package is already very nice for fine tuning. Would be superb to save models and use those to predict. Nice work!
from pytorch-retraining.
Related Issues (5)
- For own data. HOT 1
- CUDA running out of memory HOT 3
- Densenet not iterable HOT 3
- num_batches_tracked error HOT 1
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from pytorch-retraining.