Dropout rate multiplying factor at test time?
At test time in ANN model, we don't use dropout because we don't need to regularize model during testing but we still we scale down the output by a factor equal to dropout rate.
To balance for the fact that more units are active than at training time.
Ref : Hinton conspiracies paper
My question is why we need to balance? ( if test data is more it should not make any difference on an already trained model).
python-3.x deep-learning
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At test time in ANN model, we don't use dropout because we don't need to regularize model during testing but we still we scale down the output by a factor equal to dropout rate.
To balance for the fact that more units are active than at training time.
Ref : Hinton conspiracies paper
My question is why we need to balance? ( if test data is more it should not make any difference on an already trained model).
python-3.x deep-learning
add a comment |
At test time in ANN model, we don't use dropout because we don't need to regularize model during testing but we still we scale down the output by a factor equal to dropout rate.
To balance for the fact that more units are active than at training time.
Ref : Hinton conspiracies paper
My question is why we need to balance? ( if test data is more it should not make any difference on an already trained model).
python-3.x deep-learning
At test time in ANN model, we don't use dropout because we don't need to regularize model during testing but we still we scale down the output by a factor equal to dropout rate.
To balance for the fact that more units are active than at training time.
Ref : Hinton conspiracies paper
My question is why we need to balance? ( if test data is more it should not make any difference on an already trained model).
python-3.x deep-learning
python-3.x deep-learning
asked Nov 21 '18 at 11:30
Waseem AHmedWaseem AHmed
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