Any suggestions on how to implement the stochastic predictor with a different dropout rate than that which was used in training? I have tried to modify the layer attributes (.rate), but this does not change the output of the stochastic predictor function (built on the keras backend function).
Any suggestions on how to implement the stochastic predictor with a different dropout rate than that which was used in training? I have tried to modify the layer attributes (.rate), but this does not change the output of the stochastic predictor function (built on the keras backend function).