Comments (2)
It's the initial hidden states being initialized. Figure 1 in An Analysis of Neural Language Modeling at Multiple Scales shows why you might want to do that. The model becomes more confident as it gets more context. If you initialized to zero, that climb might be a little shallower. There's also a blog post that discusses the situation.
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Thanks I'm checking them right now.
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Related Issues (20)
- save model
- Hyper parameter settings for PyTorch 1.0? HOT 2
- possible error on the neural cache model, pointer? Why use the groundtruth data? HOT 1
- Problems with the next word predictions
- Language model infer
- Increasingly occupied GPU memory HOT 6
- Score a test sentence
- AttributeError: 'Program' object has no attribute '_program' HOT 1
- AssertionError HOT 1
- Dropconnect layer implementation.
- TypeError: cannot assign 'torch.cuda.FloatTensor' as parameter 'weight_hh_l0' (torch.nn.Parameter or None expected) HOT 1
- Hidden state init of LSTM layers (after the first) HOT 3
- torch.cuda.FloatTensor
- AttributeError: 'LSTM' object has no attribute 'weight_hh_l0' HOT 1
- A UsingWarning about call flatten_parameters()
- val_loss suddenly increases to a larger number(from ~5 to more than 10000) HOT 1
- The model behaves normally during training, but during prediction, the weightdrop mechanism cannot be stopped and an error is reported
- The might be a bug in splitcross.py
- initial input dropout mask is not considered.
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