Comments (1)
The predictions are logits. To convert them to probabilities, use softmax:
import torch.nn.functional as F
logits = model(x)
probabilities = F.softmax(logits, dim=-1)
from rtdl.
Related Issues (20)
- Code error, should be module_type and not str
- typos in CatEmbeddings
- wrong condition in _LVR_encoding
- LGBMRegressor on California Housing dataset is 0.68 >> 0.46 HOT 2
- embedding of categorical variables HOT 3
- Bugs in piecewise-linear encoding HOT 2
- Cannot link in the document of zero HOT 4
- Add additional validation when constructing PLE
- Regression results about the RTDL models. HOT 1
- How to resume training? HOT 3
- Typos? HOT 1
- rtdl example on dataset with categorical variables HOT 2
- # bug, located in rtdl.data.piecewise_linear_encoding line #618 HOT 9
- when to support torch 2? HOT 2
- How to get feature importance scores or attention heatmap HOT 2
- About piecewise_linear_encoding HOT 4
- Training fails (sometimes) when using several GPUs HOT 4
- VAE from microsoft
- Possibly wrong initialization in LinearEmbeddings HOT 2
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from rtdl.