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@yamins81
If we want to fit models to behavioral data, I imagine we want to be able to optimize this objective jointly with correctly predicting the label of an image. I think this can work by just having 2 different cost layers, one for performance, and one for predicting the behavioral data with a square error cost.
If we don't have behavioral data for a given image, is there a way to turn off one of the cost layers (have it not backpropagate gradient for that image or batch) without having to modify cuda code?
I fear that restarting the training procedure for some batches will cause problems with the momentum of the weights, but maybe that will be ok.
The text was updated successfully, but these errors were encountered:
@yamins81
If we want to fit models to behavioral data, I imagine we want to be able to optimize this objective jointly with correctly predicting the label of an image. I think this can work by just having 2 different cost layers, one for performance, and one for predicting the behavioral data with a square error cost.
If we don't have behavioral data for a given image, is there a way to turn off one of the cost layers (have it not backpropagate gradient for that image or batch) without having to modify cuda code?
I fear that restarting the training procedure for some batches will cause problems with the momentum of the weights, but maybe that will be ok.
The text was updated successfully, but these errors were encountered: