How is the gradient is backpropogated from the critic to the generator if we set

```
for p in netD.parameters():
p.requires_grad = False # to avoid computation
```

in the generator training?

in this example

How is the gradient is backpropogated from the critic to the generator if we set

```
for p in netD.parameters():
p.requires_grad = False # to avoid computation
```

in the generator training?

in this example

That just tells pytorch not to calculate the gradients *with respect to* the parameters of D. The gradient still flows back via the inputs of D to the parameters of G.

isnâ€™t the gradient of G dependent on the gradient flow from the D ?

Yes, but it only depends on the gradient *with respect to the input of D*, it does not depend on the gradient *with respect to the parameters of D*.