# How to solve this optimization problem?

Hi everyone,

I am a beginner in PyTorch and I am trying to code one of the equations that is mentioned in a research paper.

This equation will solve by using a trained GAN model. Here the z is the latent space of trained gan and x is the generated image.

In terms of coding, how can I do it? How can I have the z of pretrained gan?

Hi Iram!

The equation you posted an image of means that you are to find
the values of `z[i]` that minimize `MSELoss (x[?], G (z))`, while
holding the parameters that define `G` constant.

(I believe that the use of `i` as the index on both sides of the
equation is a notational inconsistency – that’s why I used `?` as
the index for `x` – but please explain if you believe otherwise.)

To use pytorch to do this, you would use pytorch’s autograd and
with respect to `z`.

To do this you would start with `z` as a one-dimensional tensor
of length `d` (shape `[d]`), and wrap it as a `Parameter`. Initialize
`z` somehow – perhaps to zero or perhaps randomly or perhaps
to an initial guess, if you have one. Let me assume that `G` is a
pytorch “model,” that is, some sort of `Module`. Go through all of `G`s
`Parameter`s, and for each of them set `requires_grad = False`.
(This is so that you won’t unnecessarily calculate gradients for the
`Parameter`s of `G`.) Instantiate a pytorch optimizer with `z` as its
`Parameter`. Then run an optimization loop who’s iteration is:

``````loss = torch.nn.MSELoss() (x, G (z))
loss.backward()
opt.step()
``````

Each optimization step moves `z` in the direction that lowers the
mismatch between `G (z)` and `x`.

Best.

K. Frank

1 Like

Hi Franks,

The explanation of this equation is:

We first embed x1, x2 into latent space of G. Specifically, we optimize over the latent space to find vectors that produce inputs close to x1 and x2 in l2-norm.

I don’t think using of i on both sides of equation is notational inconsistency. Can you explain why you think this is inconsistent?