Comments (3)
I had the same error. Gradcheck requires double precision numbers to run. By changing all the tensors and modules to double precision the code will run fine and return True. You can change them with .double().
A more detailed explanation can be found here:
https://discuss.pytorch.org/t/why-is-nn-linear-failing-gradcheck-is-there-a-bug-in-gradcheck/17346
from convolutional_lstm_pytorch.
Thank you all. I will update the code soon.
from convolutional_lstm_pytorch.
You just need to change the res = torch.autograd.gradcheck(loss_fn, (output, target), raise_exception=True)
to res = torch.autograd.gradcheck(loss_fn, (output, target), eps=1e-2, atol=1e-2, raise_exception=True)
from convolutional_lstm_pytorch.
Related Issues (20)
- using for custom dataset HOT 1
- Is there any experiments results provided?
- Found the code is much slower than Keras counterpart (takes 2-3 times longer time). Do you know why?
- What's the shape of input? HOT 1
- I think forward code is wrong
- Input shape issue and lack of bias. HOT 9
- The shape of the output
- Why Wci, Wcf, Wco are Variables rather than nn.Parameters HOT 3
- Where is the squence length HOT 1
- why hidden_channels % 2 == 0 ?
- Error in backward HOT 1
- Peephole connections (Wci, Wcf, Wco) gradient update HOT 1
- Why self.num_features=4 in line 15?
- How can I use this module for predicting the moving mnist like the paper? HOT 1
- about concat HOT 5
- about forward HOT 1
- 关于input 和 step 的问题 HOT 2
- How to do the entire sequence all at once?
- Why Wci,Wcf,Wco should be initialized at the beginning of each batch? HOT 6
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from convolutional_lstm_pytorch.