Hi, Ryan,
Yes, the solution may be suitable for some cases. But in some complicated case, I am not sure it is able to handle. Such as there are three models, Q1, Q2, V, and two loss functions Loss1, Loss2. Loss1 is calculated with the output of V and Q1 (RMSE or other form loss function), but we only back prop the gradient to the model V's parameters rather than model Q1's parameters. This is a simple abstract of the SAC model, I am sure there are more diverse demands for the ANN module. I still need more time to figure out how to achieve this, hope our discussion will provide some thoughts (sparks) about it. I am not sure I explain the case clearly, please let me know if you need more information. Regards, Xiaohong At 2019-02-15 10:15:27, "Ryan Curtin" <[email protected]> wrote: >On Fri, Feb 15, 2019 at 09:35:03AM +0800, problemset wrote: >> Hi, all, >> >> Nowadays, as the ML/DL/RL developed quickly, there is more diversity >> demand on the flexibility of ANN module. I am wondering that is there >> a way to stopping gradient back prop through a particular layer in >> mlpack. Like Pytorch uses detach() while Tensorflow uses >> stop_gradien. > >Hey there Xiaohong, > >Could we create a layer we could add that just doesn't pass a gradient >through, perhaps? > >That may not be the best solution (in fact I am sure it is not) but it >could at least be a start. > >-- >Ryan Curtin | "I know... but I really liked those ones." >[email protected] | - Vincent
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