Comments (1)
t-ae
commented on June 3, 2024
It's OK in gan_mnist.py because real_data and fake_data has shape [BATCH_SZIE, OUTPUT_DIM], and Discriminator reshapes input at first.
Line 100 in ae47a18
But, in gan_cifar10.py, Discriminator requires input tensor to have rank 4. It means gradients also has rank 4.
Lines 89 to 93 in ae47a18
So computation of
gradient_penalty is not correct.
In original(?) implementation Discriminator requires rank 2 tensor.
https://github.com/igul222/improved_wgan_training/blob/master/gan_cifar.py#L71
In gan_language.py, original Discriminator takes rank 3 tensor. But norm is computed along two axes.
https://github.com/igul222/improved_wgan_training/blob/master/gan_language.py#L107
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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
- 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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