Question 1

What is the recommended dropout rate for dense layers?

Accepted Answer

For dense (fully connected) layers, a dropout rate of 0.5 is commonly used as a strong regularizer.

Question 2

What dropout rate should I use for convolutional layers?

Accepted Answer

Convolutional layers typically use a lower dropout rate between 0.2 and 0.3 to preserve spatial features.

Question 3

Is dropout recommended for recurrent / LSTM layers?

Accepted Answer

Yes, but with caution. A dropout rate around 0.2 is often used for recurrent layers to avoid overfitting without disrupting sequence learning.

Question 4

What is Monte Carlo Dropout?

Accepted Answer

Monte Carlo Dropout applies dropout at inference time to approximate Bayesian uncertainty. By running multiple forward passes with different dropout masks, you can estimate prediction variance.

Dropout Rate Optimizer

Layer settings

Effective capacity

Monte Carlo Dropout — uncertainty estimation