Comments (3)
No, it should be disc_interpolates.size(). Because grad_outputs
is not the outputs of gradient. When performing backward procedure, it will multiply the outputs
and grad_outputs
.
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thank u
from wgan-gp.
Hi @caogang Why don't directly set grad_outputs=None ?
from wgan-gp.
Related Issues (20)
- D_real.backward(mone) RuntimeError: invalid gradient at index 0 - expected shape [] but got [1] HOT 5
- why D_real.backward(one) and D_fake.backward(mone)? HOT 2
- AttributeError: 'generator' object has no attribute 'next' HOT 2
- sometimes loss is negative during training model HOT 1
- Discriminator requires_grad=False when training Generator
- bug in calc_gradient_penalty? HOT 1
- Mode Collapse WGAN
- It requires grad clip in original paper which seems to be ignored in this implementation. HOT 2
- How to download the language dataset?
- ValueError: Tensor._shape cannot be assigned, use Tensor.set_shape instead. HOT 3
- (op: 'FusedBatchNorm') with input shapes: [64,256,8,8], [256], [256], [0], [0].
- Penalizing Norm of Jacobian
- WGAN-gp loss keeps going large HOT 4
- A question about Dcost
- The aixs of norm of the gradient HOT 1
- The loss funciont is wrong in the implement? HOT 1
- Is the G_loss wrong?
- How does optimizer work when there are 3 backwards(real, fake, penalty)?
- how to decide the value of λ HOT 2
- 一堆错误 HOT 1
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