How to optimize multi model's parameter in one optimizer

1

I know we can use “optimizer = optim.Adam(model1.parameters())” to optimize a model, but how can I optimize multi model in one optimizer?

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2 
optim.Adam(list(model1.parameters()) + list(model2.parameters())
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3

Could I put model1, model2 in a nn.ModulList, and give the parameters() generator to optimizer？

4

Yes you can do that.

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5

When I construct a optimizer like that, the torch.nn.utils.clip_grad_norm_ function is not available anymore. Is there any way to make both of them available?

6

Seems to be this issue. Let’s keep the discussion in the other thread to keep this one clean. :wink:

7

Hi @ptrblck and @smth, just wanted to make sure that method with concatenating lists of paramaters from two models would work even if there is some highly non-linear transformation between them?
The case I am thinking about is that model 1 will create some outputs y_1. Then a non-linear function f is applied applied, such that, the inputs to model 2: x_2 are x_2 = f(y_1).
Or is there a way of forcing a optimizer to be multiplied by a gradient of that function in between?

Thanks for your time!

8

The optimizer will apply its updating scheme to the passed parameters, so it should work if you pass the parameters of both models to it.

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9

You can use itertools.chain

from itertools import chain
chain(net1.parameters(), net2.parameters())
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10

Is it possible to use a different learning rate for each of the models with the same optimizer?

11

You can use different learning rate and other hyperparameters as well.
Take a look on official example

optim.SGD([
                {'params': model.base.parameters()},
                {'params': model.classifier.parameters(), 'lr': 1e-3}
            ], lr=1e-2, momentum=0.9)

Reference to official docs.

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13

I believe, all pytorch optimizers provide the same interface, so should work with Adam as well.