InfoGAN model error

[Sumching]

dist = torch.distributions.one_hot_categorical.OneHotCategorical(torch.tensor([0.1]*10))

lossf = MutualInformationPenalty()
z = torch.randn(2, 100)
c_cont = torch.randn(2, 30)
c_dis = dist.sample([2])
print(c_dis)

G = InfoGANGenerator(10, 30)
D = InfoGANDiscriminator(10, 30)

fake = G(z, c_dis, c_cont)

score, q_categorical, q_gaussian = D(fake, return_latents=True)

loss = lossf(c_dis, c_cont, q_categorical, q_gaussian)
loss.backward()

> Traceback (most recent call last):
  File "test.py", line 40, in <module>
    loss.backward()
  File "/usr/local/lib/python3.5/dist-packages/torch/tensor.py", line 102, in backward
    torch.autograd.backward(self, gradient, retain_graph, create_graph)
  File "/usr/local/lib/python3.5/dist-packages/torch/autograd/__init__.py", line 90, in backward
    allow_unreachable=True)  # allow_unreachable flag
RuntimeError: element 0 of tensors does not require grad and does not have a grad_fn

Hi,

There are quite a few errors in the documentation. And it took me a lot of time to build the model correctly.

When I used the above code, there were some errors and I was wondering if it was my usage error.

Thanks.

[avik-pal]

@Aniket1998 could you have a look at the error?

Also @Sumching we would really appreciate if you could point us to the errors in the documentation.