leftthomas / capsnet Goto Github PK

a sample can be classify correctly, but confidence is small

Hi @leftthomas
the part of reconstruction for Capsnet works out by using nn.Linear as the last layer in self.decoder and for MNIST dataset, it is 784 output channel.
However, if we want to implement Capsnet to another image classification dataset such as 256*256 colored images, whether it has too much paremeters to be trained for reconstruction part. What should we do when facing this problem?
Very appreciate!

sorry to say that (maybe it's my fault) , my problem is that nothing happens in visdom enviroment here : http://127.0.0.1:8097/ .
I don't know maybe some time I should wait to get the result .

File "/home/lxt/pytorch/CapsNet/main.py", line 45, in
digit_caps = model(data, target)
File "/home/lxt/anaconda3/lib/python3.6/site-packages/torch/nn/modules/module.py", line 224, in call
result = self.forward(*input, **kwargs)
File "/home/lxt/pytorch/CapsNet/capsnet.py", line 29, in forward
digit_caps = self.digit_caps(primary_caps)
File "/home/lxt/anaconda3/lib/python3.6/site-packages/torch/nn/modules/module.py", line 224, in call
result = self.forward(*input, **kwargs)
File "/home/lxt/pytorch/CapsNet/functions.py", line 53, in forward
pred = [self.Wi for i, group in enumerate(u) for in_vec in group]
File "/home/lxt/pytorch/CapsNet/functions.py", line 53, in
pred = [self.Wi for i, group in enumerate(u) for in_vec in group]
File "/home/lxt/anaconda3/lib/python3.6/site-packages/torch/nn/modules/module.py", line 224, in call
result = self.forward(*input, **kwargs)
File "/home/lxt/anaconda3/lib/python3.6/site-packages/torch/nn/modules/linear.py", line 53, in forward
return F.linear(input, self.weight, self.bias)
File "/home/lxt/anaconda3/lib/python3.6/site-packages/torch/nn/functional.py", line 555, in linear
output = input.matmul(weight.t())
File "/home/lxt/anaconda3/lib/python3.6/site-packages/torch/autograd/variable.py", line 560, in matmul
return torch.matmul(self, other)
File "/home/lxt/anaconda3/lib/python3.6/site-packages/torch/functional.py", line 168, in matmul
return torch.mm(tensor1.unsqueeze(0), tensor2).squeeze_(0)
File "/home/lxt/anaconda3/lib/python3.6/site-packages/torch/autograd/variable.py", line 579, in mm
return Addmm.apply(output, self, matrix, 0, 1, True)
File "/home/lxt/anaconda3/lib/python3.6/site-packages/torch/autograd/_functions/blas.py", line 26, in forward
matrix1, matrix2, out=output)
TypeError: torch.addmm received an invalid combination of arguments - got (int, torch.cuda.FloatTensor, int, torch.cuda.FloatTensor, torch.FloatTensor, out=torch.cuda.FloatTensor), but expected one of:

• (torch.cuda.FloatTensor source, torch.cuda.FloatTensor mat1, torch.cuda.FloatTensor mat2, *, torch.cuda.FloatTensor out)
• (torch.cuda.FloatTensor source, torch.cuda.sparse.FloatTensor mat1, torch.cuda.FloatTensor mat2, *, torch.cuda.FloatTensor out)
• (float beta, torch.cuda.FloatTensor source, torch.cuda.FloatTensor mat1, torch.cuda.FloatTensor mat2, *, torch.cuda.FloatTensor out)
• (torch.cuda.FloatTensor source, float alpha, torch.cuda.FloatTensor mat1, torch.cuda.FloatTensor mat2, *, torch.cuda.FloatTensor out)
• (float beta, torch.cuda.FloatTensor source, torch.cuda.sparse.FloatTensor mat1, torch.cuda.FloatTensor mat2, *, torch.cuda.FloatTensor out)
• (torch.cuda.FloatTensor source, float alpha, torch.cuda.sparse.FloatTensor mat1, torch.cuda.FloatTensor mat2, *, torch.cuda.FloatTensor out)
• (float beta, torch.cuda.FloatTensor source, float alpha, torch.cuda.FloatTensor mat1, torch.cuda.FloatTensor mat2, *, torch.cuda.FloatTensor out)
  didn't match because some of the arguments have invalid types: (int, torch.cuda.FloatTensor, int, torch.cuda.FloatTensor, !torch.FloatTensor!, out=torch.cuda.FloatTensor)
• (float beta, torch.cuda.FloatTensor source, float alpha, torch.cuda.sparse.FloatTensor mat1, torch.cuda.FloatTensor mat2, *, torch.cuda.FloatTensor out)

torch version 2.0.3 , I try both python3 and python2